1430348/FULLTEXT01.pdf · i Abstract The family of cytochrome P450 enzymes (P450s) belongs to one...

Theoretical Studies of Drug-

Metabolizing Cytochrome

P450 Enzymes

Junhao Li

Doctoral Thesis in Department of Theoretical Chemistry and Biology

School of Engineering Sciences in Chemistry, Biotechnology and Health

KTH-Royal Institute of Technology

S-106 91 Stockholm, Sweden 2020

© Junhao Li, 2020

ISBN 978-91-7873-546-4

TRITA-CBH-FOU-2020:29

Printed by Universitetsservice US-AB

Stockholm, Sweden 2020

To my family

i

Abstract

The family of cytochrome P450 enzymes (P450s) belongs to one of the

most important enzyme families in the human body. P450s are involved in the

synthesis of endogenous compounds and metabolism of exogenous substances.

In mammalian species, drug metabolizing P450s are anchored in the bilayer

lipid membrane, which allows the enzymes to interact with other proteins and

ligand molecules. A wealth of knowledge about the structures, functions, and

mechanisms of P450s have been obtained from both experimental and

theoretical studies. However, the mechanisms behind some experimental

results, such as the regio- and stereoselectivity and structural flexibility are

still elusive.

In this thesis, I present the work done in my doctoral studies, which was

focused on the catalytic selectivity and structural flexibility of P450s.

Multiple theoretical modeling approaches, such as homology modeling,

molecular docking, molecular dynamics, quantum mechanics, and quantum

mechanics/molecular mechanics, were applied in the studies. In papers I and

II, the regio- and stereoselectivity of CYP4F2, CYP3A4, and CYP19A1

catalyzed C–H hydroxylation of different substrates were studied. The results

indicate that the ligand reactivity and accessibility can be decisive for the

regio- and stereoselectivity. However, which of them is more important is

system-dependent. The quantum mechanics/molecular mechanics calculation

results imply that the distribution of spin natural orbitals could be used for

discriminating the roles of the reactivity and accessibility. In papers III and IV,

the conformational dynamics of the open and closed structures of CYP2B4

and the ligand cooperativity phenomenon of midazolam metabolized by

CYP3A4 were investigated using molecular dynamics simulations. From the

simulation results, we identified the key residues for the conformational

dynamics for the open-to-intermediate transition and found that the ligand

cooperativity is also caused by the large flexibility of P450. The results also

indicated that the homotropic cooperativity mainly occurs in the large and

flexible productive site, rather than in the remote allosteric site.

ii

Svensk sammanfattning

Familjen av cytokrom P450-enzymer (P450) tillhör en av de viktigaste

enzymfamiljerna i människokroppen. P450 är involverade i syntesen av

endogena föreningar och metabolism av exogena substanser. Hos

däggdjursarter är läkemedelsmetaboliserande P450s bundna till

lipidmembranet i cellerna, vilket påverkar P450s förmåga att interagera med

andra proteiner och ligandmolekyler. En mängd kunskap om P450:s struktur,

funktion och mekanism har erhållits från både experimentella och teoretiska

studier. Däremot är mekanismerna bakom vissa experimentella resultat, såsom

regio- och stereoselektivitet och strukturell flexibilitet fortfarande svårfångade.

I denna avhandling presenterar jag det arbete som gjorts under mina

doktorandstudier, som fokuserade på den katalytiska selektiviteten och

strukturella flexibiliteten hos P450. Flera teoretiska modelleringsmetoder,

såsom homologimodellering, molekylär dockning, molekyldynamik,

kvantmekanik och kvantmekanik/molekylmekanik har använts i studierna. I

artikel I och II studerades regio- och stereoselektiviteten för CYP4F2,

CYP3A4 och CYP19A1 C-H hydroxylering av olika substrat. Resultaten

indikerar att ligandreaktiviteten och tillgängligheten kan vara avgörande för

regio- och stereoselektiviteten. Vilken av dem som är viktigare är emellertid

systemberoende. Resultaten av beräkningen med

kvantmekanik/molekylmekanik innebär att fördelningen av naturliga spin

orbitaler kan användas för att urskilja rollen för substratets reaktivitet och

tillgänglighet. I artiklarna III och IV undersöktes konformationsdynamiken

för de öppna och slutna strukturerna av CYP2B4 och

ligandkooperativitetsfenomenet för midazolam metaboliserat genom CYP3A4

med hjälp av molekyldynamiksimuleringar. Från simuleringsresultaten

identifierade vi nyckelaminosyrorna för konformationens dynamik för den

öppen-till-intermediär-övergången och fann att ligandens kooperativitet också

orsakas av den stora flexibiliteten hos P450. Resultaten indikerade även att

den homotropa kooperativiteten huvudsakligen inträffar på det stora och

flexibla produktiva sätet, snarare än på det avlägsna allosteriska sätet.

iii

List of papers included in this thesis (†equally contributed)

Paper I Li, Junhao†; Zhang, Hongxiao†; Liu, Guixia; Tang, Yun; Tu,

Yaoquan; Li, Weihua. Computational Insight into Vitamin K1 ω-

hydroxylation by Cytochrome P450 4F2. Front. Pharmacol. 2018,

1065.

Paper II Li, Junhao; Tang, Yun; Li, Weihua; Tu, Yaoquan. Mechanistic

Insights into the Regio- and Stereoselectivities of Testosterone and

Dihydrotestosterone Hydroxylation Catalyzed by CYP3A4 and

CYP19A1. Chem. - Eur. J. 2020, doi: 10.1002/chem.201905272

Paper III Li, Junhao; Zhou, Yang; Tang, Yun; Li, Weihua; Tu, Yaoquan. Dissecting the Structural Plasticity and Dynamics of Cytochrome

P450 2B4 by Molecular Dynamics Simulations. Submitted for

publication.

Paper IV Li, Junhao; Chen, Yue; Tang, Yun; Li, Weihua; Tu, Yaoquan.

Mechanism of the Homotropic Cooperativity of Midazolam

Metabolism by Cytochrome P450 3A4: Insight from

Computational Studies. In manuscript.

Remarks on my contributions to the papers included:

As the first author of the included papers, I took major responsibility for the

design, calculation, analysis and writing of the papers. I also discussed the

results and revised the manuscripts with the other authors.

iv

List of papers not included in this thesis

Paper I Du, Hanwen; Li, Junhao; Cai, Yingchun; Zhang, Hongxiao; Liu,

Guixia; Tang, Yun; Li, Weihua. Computational Investigation of

Ligand Binding to the Peripheral Site in CYP3A4: Conformational

Dynamics and Inhibitor Discovery. J. Chem. Inf. Model. 2017, 57,

616-626.

Paper II Midde, M. Narasimha; Gong, Yuqing; Cory, J. Theodore; Li,

Junhao; Meibohm, Bernd; Li, Weihua; Kumar, Santosh. Influence

of Ethanol on Darunavir Hepatic Clearance and Intracellular

PK/PD in HIV-Infected Monocytes, and CYP3A4-Darunavir

Interactions Using Inhibition and in Silico Binding Studies. Pharm.

Res. 2017, 34, 1925-1933.

Paper III Xue, Yuhan; Li, Junhao; Wu, Zengrui; Liu, Guixia; Tang, Yun; Li,

Weihua. Computational insights into the different catalytic

activities of CYP3A4 and CYP3A5 toward schisantherin E. Chem.

Biol. Drug Des. 2019, 93, 854-864.

Paper IV Chen, Yue; Li, Junhao; Wu, Zengrui; Liu, Guixia; Tang, Yun; Li,

Weihua. Computational Insight into the Allosteric Activation

Mechanism of Farnesoid X Receptor. J. Chem. Inf. Model. 2020,

60, 1540-1550.

v

Acknowledgements

Time flies! The image of the heaviest snow here in 2016 is still vivid in my

mind. Now, I would like to express my most gratitude to my supervisor Dr.

Yaoquan Tu for offering me a chance to purse my PhD study at KTH. Under

his supervision, I learnt a lot in the four years. I greatly appreciate his patience,

motivation, suggestion, and immense knowledge. I also thank him for giving

me free rein to pursue the P450 projects and being supportive when I

encounter problems. His rigorous spirit in theory has set a good example for

me.

I am thankful to Prof. Weihua Li in East China University of Science and

Technology (ECUST), who gives me plenty of useful suggestions in the

modeling of P450s. He is an expert in the field of P450s and is rigorous in

scientific research. Many thanks to him since he brought me into this field 7

years ago.

I thank my co-supervisor Prof. Hans Ågren for all the helps he provided

me during my PhD studies. I also thank Prof. Yi Luo, Prof. Faris

Gelmukhanov, Prof. Olav Vahtras, Prof. Patrick Norman, Prof. Mårten

Ahlquist, Prof. Boris Minaev, Dr. Zivinas Rinkevicius, Dr. Xin Li, Dr.

Murugan Natarajan Arul, Dr. Viktor Kimberg, Dr. Mathieu Linares, Dr.

Haichun Liu, and Dr. Stefan Knippenberg for giving the classes, organizing

seminars, summer schools, and inviting researchers worldwide to broaden our

horizons. Although Mrs. Nina Bauer is now retired, I would like to express

my thanks to her for making our daily work in the department more

convenient.

I would like to thank Dr. Xianqiang Sun, Dr. Xu Wang, Dr. Guanglin

Kuang, Dr. Rongfeng Zou, and Yang Zhou for their scientific advice and

helpful discussions for my project.

Dr. Zengrui Wu from ECUST is acknowledged for his interesting ideas

and useful discussions.

I would like to acknowledge the other members in the division of

theoretical chemistry and biology in the past four years, including Dr. Ying

vi

Wang, Dr. Sai Duan, Dr. Zhen Xie, Dr. Shaoqi Zhan, Dr. Rafael Carvalho

Couto, Dr. Vinícius Vaz da Cruz, Dr. Ignat Harczuk, Dr. Nina Ignatova, Dr.

Xiyu Li, Dr. Junfeng Li, Dr. Weijie Hua, Dr. Yong Ma, Qingyun Liu, Xiaoyu

Chen, Ge Li, Dusanka Golo, Juan de Gracia, Dr. Hao Su, Karan Ahmadzadeh,

Manuel Brand, Dr. Pooria Farahani, Dr. Iulia Brumboiu, Viktoriia Savchenko,

Dr. Glib Barshnikov, Yogesh Todarwal, Dr. Haofan Sun, Dr. Dirk Rehn, Dr.

Markéta Paloncyova, Dr. Nanna Holmgaard List, Dr. Vadim Zakomirnyi,

Yuya Yamaura, Yuichiro Kanamori, Dr. Lucia Labrador Paez, Dr. Magnus

Ringholm, Dr. Michal Biler, and Dr. Lasse Kragh Sørensen. I feel happy to

stay with you and enjoy the time working or playing together with you in

Sweden.

It is the China Scholarship Council (CSC) that provides the financial

support for my study in Sweden and I express my sincere thanks to CSC.

Finally, I want to express my deep thanks to my parents for their selfless

care and love. I also thank my two brothers for their support and company in

the childhood. Special thanks to Zizi, for her constant encouragement,

inspirations, and endless support.

vii

Abbreviations

ASP Astex statistical potential

BDE bond dissociation energy

BO Born-Oppenheimer

Cpd 0 compound 0

Cpd I compound I

CPR cytochrome P450 reductase

CV collective variables

DFT density functional theory

DHT dihydrotestosterone

FAD flavin adenine dinucleotide

GaMD Gaussian accelerated molecular dynamics

GGA general gradient approximation

GOLD genetic optimization for ligand docking

HAT hydrogen atom transfer

HF Hartree-Fock

LDA local density approximation

LJ Lennard-Jones

MD molecular dynamics

MDZ midazolam

NAC near-attack conformation

NADPH reduced nicotinamide adenine dinucleotide phosphate

ONIOM Our own N-layered Integrated molecular Orbital and molecular

Mechanics

P450 cytochrome P450 enzyme

PCA principle component analysis

PDB protein data bank

PES potential energy surface

PMF potential mean force

QM quantum mechanics

QM/MM quantum mechanics/molecular mechanics

RC reactant complex

RMSD root-mean-square deviation

RMSF root-mean-square fluctuation

viii

SCF self-consistent-field

SIE self-interaction error

SNO spin natural orbital

SOM site of metabolism

SRS substrate recognition site

TES testosterone

TMH transmembrane helix

TS transition state

TST transition state theory

UV ultraviolet

vdW van der Waals

VK1 vitamin K1

XC exchange–correlation

ZPE zero-point energy

ix

CONTENTS

CHAPTER 1 INTRODUCTION .............................................................. 1

CHAPTER 2 BIOLOGICAL BACKGROUND OF P450S ............................. 3

2.1 The nomenclature and classification of P450s ................................................. 3

2.2 P450s and drug developments ............................................................................... 4 2.2.1 P450s as drug metabolizing enzymes ..................................................................... 4 2.2.2 P450s as drug targets ..................................................................................................... 6

2.3 General catalytic mechanism of P450s ................................................................ 7

2.4 Structures of P450s ..................................................................................................... 9 2.4.1 Overall structure ............................................................................................................... 9 2.4.2 Heme binding site.......................................................................................................... 10 2.4.3 Active site architecture ............................................................................................... 12 2.4.4 Access and egress channels in P450s ................................................................... 13

2.5 Ligand-P450 interactions .......................................................................................13 2.5.1 Substrate ........................................................................................................................... 13 2.5.2 Inhibitor ............................................................................................................................. 14 2.5.3 Driving force for ligand binding .............................................................................. 16 2.5.4 Ligand cooperativity .................................................................................................... 18

2.6 P450-membrane interactions ...............................................................................19

2.7 Catalytic selectivity of P450s .................................................................................21 2.7.1 Reactivities ....................................................................................................................... 22 2.7.2 Accessibility ..................................................................................................................... 23 2.7.3 Combined approaches and anomalies ................................................................. 25

CHAPTER 3 COMPUTATIONAL METHODS ........................................ 27

3.1 Homology modeling ..................................................................................................27

3.2 Molecular docking .....................................................................................................28

x

3.3 Molecular dynamics ................................................................................................. 30 3.3.1 Fundamentals .................................................................................................................. 30 3.3.2 Force fields ....................................................................................................................... 31 3.3.3 System initialization ..................................................................................................... 33 3.3.4 Integration Algorithm .................................................................................................. 33 3.3.5 Temperature coupling ................................................................................................. 34 3.3.6 Pressure coupling .......................................................................................................... 35

3.4 Quantum mechanics calculations ........................................................................ 36 3.4.1 Hartree-Fock theory ..................................................................................................... 36 3.4.2 Density functional theory .......................................................................................... 37 3.4.3 Transition state theory ............................................................................................... 39

3.5 Quantum mechanics/molecular mechanics calculations (QM/MM) ...... 40

CHAPTER 4 P450 CATALYTIC SELECTIVITY........................................ 43

4.1 Vitamin K1 ω-hydroxylation by CYP4F2 (Paper I). ....................................... 43

4.2 Oxidation of testosterone and dihydrotestosterone by CYP3A4 and 19A1 (Paper II). ................................................................................................................. 47

CHAPTER 5 P450 PLASTICITY .......................................................... 51

5.1 Probing the CYP2B4 plasticity by MD simulations (Paper III). ................ 51

5.2 Homotropic Cooperativity of Midazolam Metabolism by CYP3A4 (Paper IV). .......................................................................................................................................... 55

CHAPTER 6 SUMMARY ................................................................... 59

REFERENCES................................................................................... 61

Chapter 1 Introduction 1

Chapter 1 Introduction

Thousands of years of evolution makes enzymes become the most versatile

and miraculous catalysts in nature.1,2 They play important roles in almost all

aspects of life.3

Enzymes are generally proteins with or without cofactors for their

functionalities. The cytochrome P450 enzymes (P450s) belong to a

superfamily of heme-containing metalloenzymes. The number 450 comes

from its complex with carbon monoxide, which has a characteristic absorption

peak at 450 nm on the ultraviolet (UV) light spectrum.4 As one of the mixed-

function oxidases, P450s are responsible for the biosynthesis of endogenous

compounds as well as the metabolism and detoxification of exogenous

substances, such as drugs, carcinogens, and environmental pollutants.5-7 P450s

are very versatile catalysts that catalyze various types of reactions with

perplexing mechanisms.5,8 In many cases, these reactions are difficult to occur

in the context of conventional synthetic chemistry. For example, the non-

reactive C–H bond activation in a typical hydrogen abstraction reaction is

more easily catalyzed by P450s than other inorganic catalysts.9-11 By

engineering P450s, the characteristic regio- and stereoselectivity can also be

achieved.12-15 Hence, P450s have captured intensive study interest from

chemist and bioengineer.

P450s are also the major drug metabolizing enzymes involved in the phase

I metabolism in the human body.7,16 The metabolism of a drug molecule can

be inhibited and induced by other drugs, i.e. drug-drug interactions, via

different modes of action.17,18 An inhibitor usually acts on P450s directly,

while an inducer regulates the gene expression and activities of P450s by

binding to the related nuclear receptors.19 Notably, inhibition or induction of

the metabolism of a drug by P450s can be directed by other substance from

food or environmental pollutants. Besides the inhibition/induction, the

polymorphism phenomenon of the P450s in different human races can

sometimes seriously affect the metabolism of drug molecules and leading to

2 | Chapter 1 Introduction

undesirable effects.20,21 As such, P450s play important roles in the

development of new drugs with better metabolism profile.7,22

The study of drug metabolizing P450s covers many disciplines, such as

chemistry, physics, biology, clinical pharmacology, and pharmacokinetics.23

These studies also include the experimental and theoretical sides and have

provided a wealth of knowledge of P450 regarding to its function and

structures. On one hand, the liver microsome sample has been frequently used

to investigate the metabolism of drugs by P450s in pharmacokinetics. In order

to characterize the structure of P450, the X-ray crystallography and nuclear

magnetic resonance techniques has been used and a mature protocol for

obtaining P450 molecules has been developed, which includes gene cloning,

mutation, expression, and protein purification.24 On the other hand, theoretical

chemistry and physics provide complimentary methods to investigate P450s

more deeply for understanding and explaining the experimental results. For

example, the development of valence bond theory25 as well as quantum

chemistry has greatly promoted the understanding of the mechanism of P450

catalyzed reactions.

Albeit great efforts have been made for gaining the knowledge of P450s,

there are still challenging experimental observations that are hard to

explain.8,26-28 As a followed-up of my master study, this thesis is mainly

focused on: (i) the catalytic selectivity, and (ii) the flexibility, of drug

metabolizing P450s. The thesis is organized as follows. The biological

background about several important aspects of P450s will be presented in

Chapter 2. It includes the structures, functions, and mechanisms of P450s.

Then, in Chapter 3, a brief introduction is given to the methods used in the

thesis, including homology modeling, molecular docking, molecular dynamics

(MD), quantum mechanics (QM), and quantum mechanics/molecular

mechanics (QM/MM), which is followed by brief summaries and discussions

on their applications to several P450 systems as seen in Chapter 4 and

Chapter 5. Finally, Chapter 6 provides the summary and outlook of this

thesis.

Chapter 2 Biological background 3

Chapter 2 Biological background of P450s

2.1 The nomenclature and classification of P450s

The P450s enzymes are distributed in a broad range of living species, such

as mammalian, insects, plants, fungi, bacteria, etc.29,30 They share a certain

sequence similarity and a similar folding pattern.29 To date, more than 20, 000

genes encoding P450s have been identified and characterized. The

nomenclature of P450s is based on their amino acid identity. The sequence

identities above 40% and 55% are classified into a family and a subfamily,

respectively.31 To name a specific isoform, an Arabic number and an English

letter are used to name a family and a subfamily, respectively, followed by

another Arabic number for naming the individual, e.g. CYP1A1, CYP2B4,

and CYP3A4. Generally, the numbers 1-49 and 301-499, 51-69 and 501-699,

71-99 and 701-999, and 101-299, are used for naming the families belonging

to animal, lower eukaryotes, plants, and bacteria, respectively.32

The P450s in human body are encoded by 57 kinds of genes. In total, 18

families and 43 subfamilies of P450s are discovered (Table 2.1).33,34 They

play many fundamental roles on the physiology activities in our body, such as

detoxification of hazardous compounds, metabolism of drugs, and synthesis

and deactivation of endogenous regulatory substances.34,35

4 | Chapter 2 Biological background

Table 2.1. Human P450s: families, subfamilies, isoforms and functions.33

family Isoforms Functions (partial)

1 1A1, 1A2, 1B1 drug metabolism

2 2A6, 2A7, 2A13, 2B6, 2C8, 2C9, 2C18, 2C19,

2D6, 2E1, 2F1, 2J2, 2R1, 2S1, 2U1, 2W1 drug/steroid metabolism

3 3A4, 3A5, 3A7, 3A43 drug metabolism

4 4A11, 4A22, 4B1, 4F2, 4F3, 4F8, 4F11, 4F12,

4F22, 4V2, 4X1, 4Z1 fatty acid metabolism

5 5A1 synthesis of thromboxane

7 7A1, 7B1 steroid 7α-hydroxylation

8 8A1, 8B1 synthesis of prostacycline

11 11A1, 11B1, 11B2 synthesis of steroids

17 17A1 synthesis of steroids


20 20A1 function unknown


24 24A1 deactivation of vitamin D

26 26A1, 26B1, 26C1 hydroxylation of biostearin

27 27A1, 27B1, 27C1 synthesis of cholalic acid

39 39A1 function unknown

46 46A1 hydroxylation of steroids

51 51A1 hydroxylation of kryptosterol

2.2 P450s and drug developments

Developing a new drug is often costly and time-consuming. For a drug

candidate, its binding affinity to the target and the metabolism profile are of

crucial importance.36 In the drug development process, P450s can serve as

either drug metabolizers or drug targets.37

2.2.1 P450s as drug metabolizing enzymes

As drug metabolizing enzymes, P450s are responsible for the metabolism

of about 75% clinically-used drugs.6,38 The beneficial effect of drug

metabolism is to avoid the accumulation of drug in body, which might cause

undesirable side-effect. However, in some cases, the P450s mediated

metabolism is utilized for the activation of prodrugs, which is designed for

avoiding the first pass effect in the digestive system.39 For example, the

antiplatelet agent clopidogrel is a prodrug that can be activated in vivo by


several P450s, including CYP2C19, CYP3A4, and CYP2B6.40 Amongst 57

human P450 isoforms, CYP3A4, 2D6, 2C8, 2C9, 2C19, 2E1, and 1A2 are

deemed as the major drug metabolizing P450s.6 For the drugs that are known

to be metabolized by P450s, 90% of the metabolic reactions are catalyzed by

these isoforms.6

CYP3A4 is the most abundant P450 expressed in liver and small intestine

and is responsible for the metabolism of ~30%-50% drugs in clinic use.6 The

substrates of CYP3A4 cover a broad range of drugs, including macrolides

antibiotics, antiarrhythmic agents, benzodiazepines, immunomodulators, anti-

HIV drugs, and antihistamines.41 Besides participating in drug metabolism,

CYP3A4 is also involved in the oxidation of carcinogens, e.g. aflatoxin.42

CYP2D6, 2C8, 2C9, 2C19 and 2E1 are members of the CYP2 family,

which is the largest and most complicated P450 family (Table 2.1) in

human.33 CYP2D6 is mainly expressed in the liver, but it is also present in

brain and duodenum.43 An important feature of CYP2D6 is its genetic

polymorphism, which leads to the expression of non-functional alleles in

different human races.43,44 Therefore, the polymorphism phenomenon in

CYP2D6 can cause some undesired drug-drug interactions and drug

tolerances.45 CYP2C9 is the most abundant isoform in the CYP2C subfamily

and responsible for the metabolism of about 20% of drugs.46 CYP2C19 is the

major drug metabolizing enzyme for the proton pump inhibitors, such as

omeprazole, lansoprazole and pantoprazole.47,48 With a relatively large active

site, CYP2C8 is responsible for the metabolism of more than 100 drugs,

including amodiaquine, dasabuvir, imatinib, pioglitazone, and rosiglitazone,

and the number keeps increasing.49,50 In contrast, the CYP2E1 active site is

relatively small and associated with the oxidative stress and oxidative liver

injury by alcohol.51

The CYP1A2 enzymes are distributed in the liver and catalyze the

oxidation of drugs with xanthine scaffolds, such as imipramine and

propranolol.52

In development of new drug, drug candidates are commonly optimized to

have a great binding affinity with its target in vitro, however, their potencies

in clinical test are often unexpectedly weak or maintain only for a short


duration.53 This is usually caused by the poor bioavailability of the drug

candidate, which is affected by many factors, e.g. being metabolized too fast

by P450s. Therefore, to improve the bioavailability, it necessary to gain

knowledge about P450s in many aspects, including polymorphism, enzyme

inhibition, oxidation selectivity, and reaction mechanisms.54,55

2.2.2 P450s as drug targets

P450 is one of the most important enzymes in the biosynthesis pathway of

steroids. For the diseases caused by or related to the disorder of endogenous

steroids, the inhibition of relevant P450s is a good strategy for the treatment.

Two important P450 isoforms, CYP17A1 and 19A1 have received much

attention in the treatment of prostatic and breast cancers, respectively.56-58

Their roles on the biosynthesis of steroids are hydroxylase (both 17A1 and

19A1), lysase (17A1), and aromatase (19A1).8 Though the mechanisms for

the reactions catalyzed by lysase or aromatase are still controversial,8,59 the

drugs targeting these enzymes have already been approved and marketed.

Abiraterone is a marketed drug targeting CYP17A1 with nano-mole binding

affinity.60 However, abiraterone (Figure 2.1a) is a steroid derivative that may

cause the off-target effect, which is related to the undesirable drug-drug

interactions via the inhibition of other steroid-metabolizing P450s, such as

CYP3A4.61 The marketed drug anastrozole (Figure 2.1b) has a non-steroid

scaffold and inhibits CYP19A1 via the formation of the Fe–N bond with the

heme cofactor.62

Besides CYP17A1 and 19A1, other P450s may also serve as drug targets

whenever they play a vital role in the diseases process. For example, CYP1B1

has been considered as a potential anticancer therapeutic target because of its

overexpression in the tumor cells.63 In addition, CYP1B1 has been recognized

as a biomarker for detection of tumor phenotype.63


Figure 2.1. The chemical structures of abiraterone and anastrozole.

2.3 General catalytic mechanism of P450s

P450s are able to catalyze many kinds of reactions, the majority of which

are oxidation reactions, including aliphatic hydroxylation, aromatic

hydroxylation, N-dealkylation, O-dealkylation, N-oxidation and S-

oxidation.5,64 The aliphatic hydroxylation is the most typical reaction, which

inserts an oxygen into the C–H bond of the substrate. The mechanism of this

reaction has attracted intense of study interest.9-11,65,66 As depicted in Figure

2.2, the oxidation of a substrate containing C–H bond (R–H) is accomplished

by a cycle of several sub-reactions with different states of the heme cofactor.64

Before the substrate binding, the heme is usually coordinated with a water

molecule, which is deemed as a “rest” state. After the substrate enters the

active site, the system receives one electron transported from co-enzymes.

The transportation of this electron is dependent on the cellular localization of

P450s. For P450s in the endoplasmic reticulum, the electron is transported

from reduced nicotinamide adenine dinucleotide phosphate (NADPH) to

cytochrome P450 reductase (CPR) and then to P450s. For the mitochondrial

P450s, the path is: NADPH → flavin adenine dinucleotide (FAD) → P450s.

In the next stage, a triplet dioxygen binds to the ferrous cofactor followed by

the transportation of the second electron, which comes from CPR or

cytochrome b5.67 This is called “second-stage reduction” and it is the rate-

determining step for the catalytic cycle in many P450s. After the second-stage

reduction, the Fe(III)–O–O- species (E) captures one proton from the active

site environment, which generates a nucleophile species (E' in Figure 2.2).

The E and E' species are named sometimes compound 0 (Cpd 0).64 The


system then continues to capture one more proton and generates a highly

reactive species called compound I (Cpd I).64 At this stage, the substrate is

approaching to Cpd Ⅰ driven by the enzyme active site. By the hydrogen atom

transfer (HAT) and the “rebound” mechanisms (Figure 2.3), the oxo moiety

of Cpd Ⅰ is inserted into the substrate and the catalytic cycle is finalized.

Figure 2.2. General catalytic cycle of the aliphatic-hydroxylation. The protein

environment, the interactions between substrate (R-H) and protein, and the

heme, are simplified as purple lines, a dash line, and a parallelogram with the

iron in the center and ligated with cysteine (“S”), respectively. Reproduced

with permission from American Chemical Society.

Figure 2.3. Mechanisms underlying the last stage in the P450 catalytic cycle (a.

substrate rebound). Reproduced with permission from American Chemical

Society.

The HAT step (Figure 2.3) is usually the rate-determining step for the Cpd

Ⅰ stage (F → A) and is determined by the barrier height of hydroxylation.64


Produced by breaking the C–H bond via the HAT mechanism, the reductant

form of Cpd Ⅰ and the radical form of the substrate are highly reactive, which

impel them rebind with each other and assemble the alcohol product. Finally,

replaced by a water molecule, the product is detached from the iron (Figure

2.3). Interestingly, a barrier in the “Rreb” step exists for the spin state S=3/2,

but not for the S=1/2.11,65,68

Cpd Ⅰ is not only the active species in the aliphatic hydroxylation, but also

the active species in other P450 catalyzed oxidation reactions, such as

aromatic hydroxylation, N- and O-dealkylations.64 Unlike the HAT process in

the aliphatic hydroxylation and N- and O-dealkylations, the hydroxylation of

aromatic rings or olefins is initialized by the oxo of Cpd Ⅰ capturing the

unsaturated carbon atom.69 The resultant intermediate product may be latter

transformed to the hydroxylation or epoxidation products.65 The catalytic

cycle presented in Figure 2.2 is prevalent in P450s. However, Cpd Ⅰ is not the

only active species.64 For example, in the androgen production by CYP17A1

and nabumetone oxidation by CYP1A2, the peroxo anion E is proposed to be

a crucial active species (Figure 2.4).70,71

Figure 2.4. Mechanisms of lyase activities in CYP1A2 (a) and CYP17A1 (b).

Reproduced with permission from American Chemical Society.

2.4 Structures of P450s

2.4.1 Overall structure

P450s have a helix-rich secondary structure architecture and an enclosed

production (active) site.5,72 A heme cofactor is located in the bottom area of


the active site with the iron tethered to a cysteine thiolate.5 The secondary-

structure elements, including 13 α-helices and 2-5 β-sheets, have generally

been found in prokaryotic and eukaryotic P450s (Figure 2.5). The I-helix is

the longest helix in all P450s, which is above the two vinyl groups of heme.

Typically, there are many water molecules in the region near I-helix in the

P450 crystal structures. In some crystal structures, the water molecules in this

region can change the hydrogen bonding network in the I-helix, which results

in a slight bend in the middle of I-helix.34 The helix named with a prime is

very short and not conserved for different species. For example, the F'- and

G'-helices are not found in the crystal structures of prokaryotic P450s.

Figure 2.5. Overview of the secondary structure elements of CYP2B4 (PDB

code: 3MVR73). The cofactor heme is depicted in red sticks.

2.4.2 Heme binding site

The cofactor heme is buried in the core of an enzyme and surrounded by

the helix-rich and loop-rich domains (Figure 2.6a). The amino acid sequences

of the heme binding site are highly conserved. The two propionate groups of

heme are embedded in a region with 2-4 positive charged residues (Figure

2.6b). The iron is hexa-coordinated with the four pyrrole nitrogen atoms of

protoporphyrin, the oxygen atom of a water molecule and the cysteine sulphur

atom.74 This is a typical resting state of heme (Figure 2.2). For the crystal

structures with the substrate bound, this water molecule is replaced by the

substrate and the iron is therefore penta-coordinated.


Figure 2.6. The heme binding site of CYP3A4 (PDB code: 1TQN75). The iron

is depicted in pale cyan sphere.

A covalent modification on one of the heme methyl group by a glutamic

acid on the I-helix is commonly seen in the fatty-acid metabolizing CYP4

family.74 This residue is relatively conversed in the active site of CYP4

enzymes. The heme covalent modification was early identified by UV spectra

studies,76-78 while the crystallography evidence was published recently

(Figure 2.7).79 Due to this covalent bond, the planarity of the heme plane in

the rest state is reduced (Figure 2.7a). In most drug metabolizing P450s, the

two propionate groups are located in the same side along the heme plane. This

feature is not seen in the crystal structural of rabbit CYP4B1, where the

interactions between the propionate groups and base residues are less than the

other drug metabolizing P450s (Figure 2.7b).

Figure 2.7 (a) Comparing the heme structure in CYP4B1 (colored in cyan,

PDB: 5T6Q) and CYP3A4 (colored in magenta, PDB: 1TQN). (b) Hydrogen-

bonding interactions between the propionate groups of heme and CYP4B1.


2.4.3 Active site architecture

The active site of P450s is constituted by 6 substrate recognition sites

(SRS) and heme (Figure 2.8a). It has a broad range of (apparent) volumes

varied from ~190 (CYP2E1, PDB 3E6I) to 1438 Å3 (CYP2C8, PDB

2NNI).80,81 Additionally, the reported size of the substrate binding site has

reached 2446 Å3 from a bacterial P450.82 The configurations of the 6 SRSs

differ between various P450 isoforms and often exhibit high conformational

flexibility.83 Many fascinating phenomena are related with the high active-site

flexibility of P450s, such as the broad range of substrate specificity, various

ligand binding and unbinding pathways, oxidation of chemically non-reactive

sites, and multiple substrates/inhibitors binding.

Figure 2.8 Substrate recognition sites of P450s (a) and the front (b) and top (c)

views of the cleft with surface representations in CYP2B4 (PDB 1PO584). The

heme is depicted in red sticks.

A typical example regarding structural flexibility of P450 is from the

mammalian CYP2B subfamily,85,86 which has been served as a prototypical

model for studying the relationship between structural flexibility and drug

metabolism. The first crystal structure of CYP2B4 exhibits a distinct open

conformation, that is, a large conformational cleft between the F'-G' and B'

helices (Figures 2.8b and 2.8c).84,86 This cleft makes the CYP2B4 active site

more accessible to the substrate, and in particular exposes the heme to the

outer environment, which is rarely observed in the other P450 crystal

structures. Subsequent studies solved the CYP2B4 structures with open and

closed conformations under different conditions. These crystal structures

thereby illustrate the immense flexibility of P450s and the important role of

P450 flexibility in drug metabolism.


2.4.4 Access and egress channels in P450s

Since the active site is buried into the center of P450, the access/egress of

ligand requires conformation changes of certain residues. This has been an

intriguing issue about P450s. To date, several ligand access/egress channels,

partially constituted by the SRS, have been proposed in different P450s.87

According to the residues lining the channel, the ligand egress channels are

divided into 5 types named with numbers 1-5. Channel 2 is a combination of

at least 5 sub-channels, e.g. channels 2a, 2b, 2c, etc.87 Water molecule is not

only involved in the catalytic cycle (Figure 2.2), but also in the stabilization

of substrate binding in some cases, therefore, there exists water and solvent

channels in P450s. Similarly, the shape and composition of these channels are

dependent on the isoform of P450s. The diversity of the channel structures

and compositions is also in consistent with the active site diversity.

Understanding the diversity of channels and their dynamics during

catalytic process is important for the enzyme kinetics and catalytic functions.

The MOLE and CAVER packages are two of the most frequently used

toolkits for identifying channels in P450s.88,89 The channels for a given static

P450 geometry is usually identified by grid spacing using a sphere probe over

the entire protein.90 These algorithms provide us a way to visualize the shapes

and quantitatively decompose the structures of P450 channels.

2.5 Ligand-P450 interactions

Functionally, P450 ligands can be categorized into substrates and

inhibitors. The P450 inducers, which typically increase the catalytic activities

by binding to the proteins related to the modulation of P450 gene

expression,91 are not classified as P450 ligands in this thesis. The substrates

and inhibitors bind to P450s with many fascinating features. In this section, a

brief introduction of these features is given as follow.

2.5.1 Substrate

A substrate is the reactant in an enzyme catalyzed chemical reaction.

Unlike the high substrate specificity in other enzymes, the remarkable

substrate promiscuity makes P450s become one of the most versatile


biocatalyst.92 This feature is particularly noticeable in CYP3A4 that has a

highly flexible active site.93-95 Before the production step, which involves the

break and formation of chemical bonds (Figure 2.3), the non-covalent

interactions between substrate and P450 are important for positioning the

substrate in the active site. Hydrogen bond is often observed for such

positioning.96 Its formation with ligand can be either occurred on the

backbone or the side chain of polar residues. For example, the steroid

molecules are positioned in the active site of CYP19A1 by forming hydrogen

bonds with the side chain of Asp309 and the backbone of Met374 (Figure

2.9a). The non-polar interaction is also important in the positioning of P450

substrate. One classical example is the fatty acid ω-hydroxylase in CYP4

family. The steric hindrance of the active site places the substrate with its

aliphatic ω-terminal toward the reaction center (Figure 2.9b).

Figure 2.9. (a) Interactions between testosterone and CYP19A1 (PDB: 5JKW);

(b) a schematic of the narrow active site of fatty acid ω-hydroxylase.

Reprinted with permission from Elsevier (b).

2.5.2 Inhibitor

According to the UV spectral shift after binding, the P450 inhibitors can be

classified as two types: “type Ⅰ” and “type Ⅱ”.97 The “type Ⅰ” inhibitors are

featured for non-covalent interactions, while the “type Ⅱ” inhibitors are

typically featured by the formation of a covalent bond between the iron of

heme and the aromatic nitrogen or oxygen atom of the inhibitor.98 Therefore,


the inhibitory potency of “type Ⅱ” inhibitors are generally stronger than that

of “type Ⅰ”. The “type I” inhibitor can form hydrogen bonds with P450

indirectly with water bridges, which is often observed in the crystal structures

as well as in the computational predictions (Figure 2.10).

Figure 2.10. (a) Interactions between an inhibitor and CYP2C9 (PDB:

4NZ299); (b) the interactions between the inhibitor and CYP2C19 observed in

computational modeling.100 Reprinted with the permission from Royal Society

of Chemistry (b).

For the metabolism of some other substances, the reactive intermediates

can inactivate the enzyme via the covalent binding with the protein residues or

heme cofactor.101,102 This phenomenon is named as “suicide inhibition”, e.g.

the inactivation of heme by the carbene intermediate generated from the

metabolism of podophyllotoxin (Figure 2.11).103

Figure 2.11. Suicide inhibition of P450s by podophyllotoxin via a carbene

formation mechanism.103


2.5.3 Driving force for ligand binding

Many factors can affect the conformational dynamics of P450s upon the

binding of small molecules, which result in diverse ligand-P450 interactions.5

P450s have immense conformational diversity for the binding of various

ligands. For example, the active site conformations differ in CYP3A4 upon

the binding of an inhibitor and a substrate (Figure 2.12a), in which the

substrate binding causes a large fluctuation in the F-F' loop, G'-G loop, G'-

helix, C-terminal and B-C loops. In particularly, the F'-helix exhibits an

unstructured loop after the binding of the substrate midazolam (Figure 2.12a).

However, the binding of different inhibitors does not change the

conformations of CYP3A4 as significantly as substrates (Figure 2.12b). A

recent study by Chuo and co-workers indicated that the spatial restraints from

the crystal lattice could be a reason for the less pronounced conformational

changes of CYP3A4 with various inhibitors.104

Figure 2.12 Comparison of the CYP3A4 crystal structures. (a) the apo (PDB

1TQN, colored in green) and the midazolam (substrate, PDB 5TE8, colored in

marine blue) bound structures; (b) the apo (PDB 1TQN, colored in green), the

fluconazole (inhibitor, PDB 6MA7, colored in magenta) bound, and the PKT

(inhibitor, PDB 4D7D, colored in yellow) bound structures.

The driving force for the diversity of P450 conformations upon ligand

binding has been questioned for many years.105-111 Apparently, such

conformational diversity is caused either by the ligand induced adaption or by

inherently existing multi-states, from which a ligand could “choose” one state

to bind, with the former corresponding to “ligand-induced fit” and the latter to

“conformation-selection” (Figure 2.13a).112 The early evidence from the

crystallization of CYP2C5 with the substrate diclofenac indicated that there


exists a reasonable ligand induced fit model for P450s to recognize

structurally diverse substrates.106 Another work on CYP2B4 by Sean and co-

workers identified that the ligand-induced structural response is via helix

repositioning upon the binding of an inhibitor.107 Recently, by investigating

the enzymic kinetics for a series of P450s binding to their substrates,

Guengerich and co-workers concluded that the conformation-selection model

best fits the binding kinetics.110,111 Hence, the mechanism of ligand

recognition seems to be system-dependent. In some substrate-bound P450

crystal structures, there exists a nonproductive substrate conformation, i.e. the

site of metabolism (SOM) of the substrate does not orient to the iron. For

example, warfarin in CYP2C9 orients its SOM to the B-C loop, which is far

away (11.1 Å) from the iron (Figure 2.13b).113 The binding of warfarin does

not cause significant conformational changes for CYP2C9, and further ligand-

induced conformational changes are required for the oxidation of warfarin by

CYP2C9. However, there are many examples in which the crystal structure of

a P450 bound with the substrate with a near-attack conformation (NAC), that

is, the SOM is close to the iron (Figure 2.13c). Taking the CYP19A1-

testosterone complex as an example,114 the conformation that allows

testosterone to have the NAC may already existed before the substrate binding.

Figure 2.13. (a) Illustration of ligand-induced fit and conformation-selection

mechanisms. (b) Crystal structure of CYP2C9-warfarin complex (PDB:

1OG5113). (c) Crystal structure of CYP19A1-testosterone complex (PDB:

5JKW114). The SOM atoms and iron are depicted in spheres and the distance

(Å) between them are presented.


2.5.4 Ligand cooperativity

Several crystal structures of P450s show that there exist more than one

ligand molecules bound to the active site or the peripheral site of a P450.

Multiple-ligand binding can enhance their interactions with enzyme, which is

also refer to as ligand cooperativity. Ligand cooperativity can be divided into

two types: (i) homotropic cooperativity, in which a substrate stimulates its

own metabolism, and (ii) heterotropic cooperativity, in which the stimulation

is caused by the addition of a different substance.6,115,116 A plot of catalytic

velocity versus substrate concentration in homotropic cooperativity is usually

featured with a sigmoidal or hyperbolic shape.117 The heterotropic

cooperativity was first reported in the animal-derived P450s, while the

homotropic cooperativity was reported later in the human P450s.6 The

mechanism of the cooperativity has been investigated for many years.118 Two

major points about the ligand cooperativity has been proposed. One

assumption is that the second ligand fits to the catalytic active site close to the

first substrate.6 Now, much experimental evidence, including crystallography,

has verified this point. Two or three ligands occupying the active site has been

seen in the crystal structures of several drug metabolizing P450s (Figure

2.14), including CYP2C8 (PDB: 2NNH), 2C9 (PDB: 5XXI, 5X23), 3A4

(PDB: 2V0M, 4K9U and 4D6Z), and 2B6 (PDB: 3UA5).81,94,119-122 Another

point assumes that the second ligand binds to the allosteric site of P450s. This

is somewhat less evidence. Although three X-ray structures of CYP3A4 have

been reported with a steroid molecule bound in a peripheral site, the function

of the peripheral site is still not clear.123 Some other evidence indicated that it

may be a temporary site for stay during the substrate accessing to the active

site.104 Understanding the ligand cooperativity is also important for the P450

inhibitors in the area of drug development and bioengineering.


Figure 2.14 (a) Example of homotropic cooperativity in CYP2C8 (PDB:

2NNH81). (b) Example of heterotropic cooperativity in CYP3A4 (PDB:

4D6Z121).

2.6 P450-membrane interactions

Eukaryotic P450s are membrane anchored proteins. Most of the drug

metabolizing P450s are anchored on the membrane in the endoplasmic

reticulum side. The catalytic domain is anchored outside the membrane by a

preceding N-terminal polypeptide chain with the length of ~30-50 amino

acids, which contains the transmembrane helix (TMH) and a short linker

region (~10 amino acids) connecting the TMH and the catalytic domain. Since

the linker region is located near the negatively charged phosphate group of

membrane lipids, it is often rich in positive charged residues.124 The existence

of the linker provides a certain conformational freedom for the entire catalytic

domain, which is beneficial to the electron transfer from CPR or FAD.125

Unlike the G-protein-coupled receptor, which has 7 TMHs, the only TMH in a

P450 does not play any role in protein function. There are extensive in vitro

experimental evidences showing that the recombination P450s without TMH

have the similar catalytic activities as in the in vivo condition.124 Therefore,

the TMH was truncated in the crystallography of P450s. An exception is

CYP17A1, about half of its TMH residues were kept and display a disordered

conformation.57 This also indicated that the TMH conformation is maintained

in the presence of a membrane.

A portion of the catalytic domain, which is usually composed by the

residues between the end of the F-helix and the beginning of the G-helix,

namely the F-G cassette sometimes, has close contacts with the membrane

lipids. This region often exhibits two short helices, namely F' and G' in


mammalian P450s (Figure 2.15). This region may severe as a gate for

transporting a substrate from membrane to the active site of P450.124 Besides

this region, the B-C loop/B' helix, β1, β2, β4-β5 sheets can also contact with

the membrane extensively. However, the F-G cassette is buried most deeply

in the membrane in many P450 systems.125

Figure 2.15. Initial model of CYP3A4 (PDB 1TQN) embedded in a lipid

bilayer membrane. The N-terminal TMH is represented as green cartoon; the

F' and G' helices are represented as blue cartoon; other protein residues are

represented as gray cylindrical helices. The heme is colored in red sticks. The

lipid membrane molecules are colored in orange with the phosphorus atoms

depicted as spheres.

The membrane embedding also prevents a P450 from moving to other

subcellular organelles.26 The need for understanding the interactions between

a full-length P450 and membrane in a native environment has captured the

interest of both computational and experimental researchers. The dynamic

behaviors of P450s on membrane have been experimentally studied by small-

angle X-ray scattering, linear dichroism, and rotational diffusion etc., using

the lipid nanodiscs as a membrane model.26 Molecular dynamics (MD)

simulation has been extensively used for studying the dynamics of P450s on

the membrane since 2011 when the first atomistic-level study of the full-

length CYP2C9 embedded into a lipid bilayer was published.126 Other models


of the full-length P450s have also been used in the MD simulations, in which

the missing coordinates of the TMH were usually modeled by homology

modeling. Barnaba and co-workers used the nanodiscs to mimic the bio-

membrane and carried out MD simulations to characterize the interactions

between CYP2B4 and membrane. They concluded that: (i) the membrane is

associated with the thermostability of P450s, and vice versa, P450 is also able

to modify its surrounding lipid environment, and (ii) the interactions with

lipid membrane are strong for both TMH and soluble domain. A recent study

explored the atomistic details of the CYP19A1-CPR complex on membrane,

of which the size of the system reached 520 000 atoms, by using the MD

simulations with the time scale up to microseconds.127 The results from such

microsecond-MD simulations correlated well with the experiments and show

the predictive power of MD simulations.

2.7 Catalytic selectivity of P450s

Due to the diversity of both P450 structures and substrates, the mechanism

of the regio- and stereoselectivities of a drug metabolized by P450s is

complicated and often conflicts with the intuition gained in the study of

conventional chemical reactions.13,66 A substrate can be selectively oxidized at

different sites with distinct stereoselectivities by various P450s. For example,

the hydroxylation of nelfinavir and testosterone can be mediated by CYP3A4,

2C19 and 19A1 at different sites (Figure 2.16), including chemically reactive

and non-reactive sites.128-130 For nelfinavir, the hydroxylation reactions occur

on the bulky tertiary-butyl group by CYP2C19 and the aromatic carbon by

CYP3A4. In the hydroxylation of testosterone by CYP3A4, the primary SOM

is located at a chemically reactive site, where the pro-β hydrogen atom is

abstracted. But the non-reactive angular methyl group is the primary SOM in

the hydroxylation mediated by CYP19A1. The regioselectivity often

determines the SOM(s) of a substrate, while the stereoselectivity governs the

product distribution of a substrate with the prochirality SOMs. Many factors

can affect the regio- and stereo-selectivities for the oxidation reactions

mediated by P450s, including bond strength, active site conformations,

substrate concentration, and even the environment pH. Understanding the

mechanism underlying the regio- and stereo-selectivities is important for

predicting the SOMs and products of P450 mediated metabolism.131,132


Figure 2.16. Structures of nelfinavir and testosterone with the primary SOMs

of CYP3A4, 2C9, and 19A1 marked as red stars, magenta triangle, and green

circle, respectively.

Various experimental techniques have been developed for identifying the

SOMs of P450 substrates, but they are costly, laborious, and time-

consuming.131,133 In the past two decades, a plethora of computational

methods have been developed for predicting the most likely SOMs. These

methods are mainly focused on two aspects: reactivity and accessibility.133 In

addition to these two aspects, the fingerprint-based machine learning

approaches have become popular in recent years.134,135 Utilizing machine

learning to predict SOMs is established by recognizing the hidden pattern

within the molecular fingerprints. This topic is out of the scope of this thesis

and will not be deeply discussed in this thesis.

2.7.1 Reactivities

The reactivities of a chemical reaction generally differ between various

functional groups (sites) of a compound. By utilizing a prevailing truncated

Cpd Ⅰ model, quantum mechanics (QM) calculations are performed for

determining the activation barriers of various sites of a substrate.136 Though

the orientations of a site above the Cpd I is fully optimized, the initial

position of the substrate is usually guessed based on the reaction occurrence,

which is delicate and sensitive. Reactivities derived from QM calculations

often perform well in ranking the SOMs of a substrate. Nevertheless, locating

and optimizing the transition state (TS) structure for calculating a barrier


height is not straightforward and can be computationally expensive. To

address this issue, Rydberg and co-workers constructed an atom reactivity

library for fast reactivity mapping, which is based on the QM calculations for

the SOMs of more than 200 compounds.137 For predicting the reactivity of a

new compound not in the library, the SMART rules are used for 2D fragment

matching with penalization scores. This strategy has been proved to be

successful and implemented in their program, SMARTCyp, for fast SOM

prediction based on reactivity.137 Alternatively, Afzelius and co-workers used

QM based bond-order analysis to reduce the computational cost of locating

and optimizing TS structures.138 Recently, He and co-workers computed 56

descriptors for the bonds in substrates based on the Mulliken population

analysis, hybrid orbital and valence-bond theories. These descriptors are then

incorporated into the machine learning models to classify the SOM and non-

SOM sites.134 Such efforts have made massive predictions of SOMs based on

the reactivity feasible.

Most of the drug metabolizing reactions mediated by P450s are carbonic

hydroxylation, which requires the C–H bond activation. Therefore, computing

the bond dissociation energy (BDE) for C–H bonds, which is cost-effective, is

also beneficial to rank the reactivities of each site of a substrate. The BDE

ranking often correlates well with the QM ranking and has been considered as

an important factor in predicting SOMs of P450 substrates.139 However, the

BDE is unable to predict the stereoselectivity of a prochirality site because the

carbon radical is planar.

2.7.2 Accessibility

The SOMs of a compound determined from experiment could be

inconsistent with the reactivity ranking. This inconsistence is associated with

the accessibility of a ligand site to the catalytic center. Because of the steric

hindrance in the active site of a P450, the catalytic center may be inaccessible

to the reactive site of a substrate. In this case, the P450 active site can penalize

the high barrier brought by the relatively non-reactive, but highly accessible

site.131,133 In consideration of saving computational cost, it is possible to have

a glance of the accessibility profile by quantitatively estimating the topology

of a substrate and implicitly describing the enzyme active site. In the


SMARCyp program, this strategy was successfully implemented for the

accessibility of each site of a substance, albeit it is sometimes less

interpretable.132

With the availability of more crystal structures of P450s, constructing

structure-based models for evaluating the accessibility has become possible.

Rooted in the physical reliability, molecular docking has been deemed as a

fast tool for SOM prediction, which places a small molecule into the P450

binding pocket and ranks the binding poses with a scoring function. The sites

within a certain distance to the catalytic center (iron or iron-oxo) in the top-

ranked poses are deemed as the most accessible sites.140 However, many

factors have restricted the predictive power of molecular docking, including

the accuracy of the scoring function, the structural flexibility of the binding

pocket, accuracy of the enthalpy and entropy estimations, and the solvation

effect. To increase the predictive power of molecular docking, many efforts

have been made, including flexible sampling based ensemble docking,

reactivity-incorporated scoring functions, and inclusion of explicit water

molecules in the binding pocket.141-143 In spite of that, molecular docking-

based assessment of accessibility is still able to distinguish the enantiomers

for the prochirality centers.

Although MD simulations require much more computational resources

than molecular docking, it can be valuable in rationalizing the biological

properties between distinct enantiomers.132 MD simulations are much suitable

than molecular dockings for evaluating the accessibility profile of individual

P450 ligands. It is possible that a site predicted to be accessible by the

docking pose could be inaccessible during MD simulations. In the MD

simulations, the number of NAC of a site can be counted from the snapshots

to identify the most accessible site.132 By applying multiple 10-ns MD

simulations, Bonomo and co-workers derived the accessibility profiles of the

possible SOMs of aflatoxin B1 in the active sites of CYP3A4 and 1A2, which

explains the experimental results and interpret the interactions between the

ligand and P450s.144 However, in some extent, the MD-derived accessibility

profile may be premature for a reaction, since bond dissociation and formation

are not involved in the conventional MD simulations. For those substrates

with small size and large reactivity gap between different sites, the reactivity


could conversely decide the accessibility profiles in the P450 active site.

Moreover, the cooperative ligand binding behavior can play a role in the

accessibility of different sites. For example, the distribution of midazolam

(MDZ) metabolites is dependent on the concentration of the substrate,

whereas the major accessible sites are 1'-methyl and the 4-carbon at the low

and high substrate concentrations, respectively.145 Therefore, combined

methods may be required to evaluate the roles of reactivity and accessibility

for interpreting the selectivity of P450 catalyzed reactions.

2.7.3 Combined approaches and anomalies

The reactivity and accessibility are decisive for determining the regio- and

stereoselectivities of metabolizing reactions. The “DR-predictor” developed

by Huang and co-workers combined the reactivity descriptors calculated by

MOPAC package and the binding energy score from AutoDock Vina package

to rank the training set of molecules.146 Previously, I used a linear

combination of SMARTCyp and molecular docking scores to rank each

potential SOM for the CYP2C19 substrates. The combined models were

found to be better than docking or SMARTCyp for the test set of substrates

(Figure 2.17).143

Figure 2.17. A combined model implemented in a previous work for

predicting the SOMs of CYP2C19 substrates.143


In recent years, various methods and algorithms have been implemented

for SOM prediction with high prediction rate. However, there are still many

anomalies that are unpredictable. Computational studies using expensive

methods, e.g. quantum mechanics/molecular mechanics, are helpful for

understanding these difficult cases and interpreting the experimental

observations.8,147

Chapter 3 Computational methods 27

Chapter 3 Computational methods

3.1 Homology modeling

In the absence of experimental structures, protein structure prediction has

been widely adopted for structural-based drug design.148 Homology modeling

is based on the assumption that the structure of a protein (target protein) can

be predicted from the sequence-similar and structure-characterized protein

(template protein).149,150 This method has been extensively used in

computational modeling of proteins. Generally, the identity between target

and template sequences are required to reach 50% for obtaining a moderate

structure model. When the sequence identity is lower than 30%, the resulting

homology models are usually not reliable.151,152

The following steps are generally included in the comparative process of

homology modeling:

(i). Searching the database for the template sequence with highest identity

and homology to the target sequence;

(ii). Locating the sequence conserved regions between target and template

for sequence alignment, which inserts gap to the unaligned regions;

(iii). Assigning the coordinates for the mainchain atoms in the conserved

regions of target sequence;

(iv). Using loop modeling to construct the coordinates of the main chain

atoms in the gap area;

(v). Constructing the coordinates of the side chain atoms from database

and optimizing the conformation of the side chains;

(vi). Performing geometrical optimization for the entire target protein;

(vii). Assessing the quality of the outputs.

The decisive steps for achieving accuracy and reliability are sequence

alignment (step 2) and loop modeling (step 4).151 To find the best alignment


28

for the target and template sequences, a comparative scoring matrix is usually

constructed to rank all the possible alignments. The amino acids in the two

sequence are filled into the first row and column of the scoring matrix and a

gap is inserted into the alignment if there exists a gap in the shortest-path of

scoring matrix.153 Based on the alignment, the regions having 100% sequence

identities with the template are constructed directly by copying the

coordinates of the atoms from the corresponding regions in the template

structure. For other regions with less identities, the loop modeling method is

implemented. These loop areas are either constructed by the ab initio

calculation or from the loop structure database. The ab initio calculation

optimizes the energies of the initial structure, which is time-consuming for

large loops. Constructing the loop structure from known database is a

frequently adopted method in homology modeling.154

Although homology modeling has been widely used, it still has many

limitations, especially in the optimization of the generated models. The

quality of the models is dependent on the template structure, sequence

alignment, and unaligned loop model construction. However, the strategy of

using a template structure has saved much more computational resources in

homology modeling compared to the ab initio structural predictions.

3.2 Molecular docking

Molecular docking has been deemed as a core component in a rational

drug design process.155 It is generally classified into the protein-small

molecule (named ligand) and protein-protein dockings. In this thesis, I mainly

focus on the protein-ligand docking. For the projects included in the thesis,

docking is accomplished by the GOLD (Genetic Optimization for Ligand

Docking) program.156,157

Ligand conformational sampling and pose ranking by a scoring function

are generally involved in a molecular docking process, in which the accuracy

of the scoring function is the key factor.155 GOLD implemented several built-

in empirical scoring functions for ranking the docked poses, such as

ChemScore,158 GoldScore,157 and ASP (Astex statistical potential).159 These

scoring functions were trained from the experimental binding free energies of


protein-ligand complexes. In an empirical scoring function, the binding free

energy of a protein-ligand complex is the sum of the score for each weighted

energy term, such as the van der Waals (vdW) interaction, electrostatic

interaction, hydrogen bonding, de-solvation energy, and entropy,

𝛥𝐺𝑏𝑖𝑛𝑑𝑖𝑛𝑔 = ∑ 𝛥𝐺𝑖 ∙ 𝑓𝑖

𝑖

(3.1)

where 𝑓𝑖 are the energy terms expressed with different functions, 𝛥𝐺𝑖 are the

coefficients obtained by least-square fitting to the experimental binding free

energies of protein-ligand complexes. Compared to the force-field based

soring function, the empirical scoring function has higher computational

efficiency and accuracy for certain protein-ligand complexes. ChemScore is

one of the most frequently used scoring functions,158 which contains the

following terms:

𝛥𝐺𝑏𝑖𝑛𝑑𝑖𝑛𝑔 = 𝛥𝐺0 + 𝛥𝐺ℎ𝑏𝑜𝑛𝑑 ∑ 𝑔1(𝛥𝑟)𝑔2(𝛥𝑎)

𝑖𝐼

+ 𝛥𝐺𝑚𝑒𝑡𝑎𝑙 ∑ 𝑓(𝑟𝑎𝑀)

𝑎𝑀

+ 𝛥𝐺𝑙𝑖𝑝𝑜 ∑ 𝑓(𝑟𝑙𝐿)

𝑙𝐿

+ 𝛥𝐺𝑟𝑜𝑡𝐻𝑟𝑜𝑡 (3.2)

The scoring function contains the hydrogen bonding ( 𝑔1𝑔2 ), metal

(𝑓(𝑟𝑎𝑀)), lipophilicity (𝑓(𝑟𝑙𝐿)), and entropy (𝐻𝑟𝑜𝑡) terms. In the hydrogen

bonding term, 𝑖 and 𝐼 represent the ligand and protein atoms, respectively. 𝛥𝑟

is the deviation from the equilibrium hydrogen-bond length (1.85 Å) and 𝛥𝛼

is the deviation from the idea N/O-H∙∙∙O/N angle (180°). A function 𝑓(𝑟) is

used to describe the metal and lipophilicity terms, which has the following

form:

𝑓(𝑟) = {

1.0 (𝑟 ≤ 𝑅1)𝑅2 − 𝑟

𝑅2 − 𝑅1 (𝑅1 < 𝑟 ≤ 𝑅2)

(3.3)

where 𝑅1 and 𝑅2 are empirical parameters and vary in different terms in

equation (3.2). In the metal term ∑ 𝑓(𝑟𝑎𝑀)𝑎𝑀 , 𝑟𝑎𝑀is the distance between a

metal-coordinated ligand atom and the metal atom of the protein, where

𝑅1and 𝑅2 are 2.2 and 2.6 Å, respectively.


30

The entropy term is usually approximate as the penalty (𝐻𝑟𝑜𝑡 ) for the

number of frozen rotatable bonds. Such approximation has also been widely

adopted in other scoring functions, e.g. the force field-based scoring function

in AutoDock Vina.160

The accuracy of an empirical scoring function is limited by the size of

training set and the accuracy of the linear regressions.158 Apparently, over

fitting to the training set of complexes will reduce the applicability for scoring

other structurally diverged protein-ligand complexes. However, this also

indicated that developing an exclusive scoring function for a specific class of

protein-ligand complexes is possible. For example, Kirton and co-workers

have optimized the empirical scoring functions in GOLD for the heme-

containing proteins, such as P450s and hemoglobin.161 They introduced

several adaptions to the original scoring functions in GOLD. For the metal

term, the contact density between the iron and ligand accepter atoms was

introduced.161

3.3 Molecular dynamics

The conformational dynamics in biomacromolecules is complicated in

many aspects, including folding/unfolding, ligand binding/unbinding, and

protein-protein intearctions.162,163 Molecular dynamics (MD) simulation is the

most frequently used in silico modeling method for studying the dynamics of

biomacromolecules.164 MD simulation can be used to disclose conformational

transitions, optimize ligand binding modes, and calculate binding free

energies of a biomolecuar system.165 In this section, we will discuses the basic

procedure of an MD simulation.

3.3.1 Fundamentals

Considering a molecular system of N atoms, the total energy of the system

is the sum of the kinetic and potential energies. The potential energy is a

function of the atomic spatial positions. According to the classical mechanics,

the force acting on an atom 𝑖 is

�⃗�𝑖 = −∇𝑖𝑈 = − (𝑖̇⃗𝜕

𝜕𝑥𝑖+ 𝑗̇⃗

𝜕

𝜕𝑦𝑖+ �⃗⃗�

𝜕

𝜕𝑧𝑖) 𝑈 (3.4)


With the force derived from equation (3.4) and Newton’s law of motion, we

can obtain the acceleration of atom 𝑖 as �⃗�𝑖 = �⃗�𝑖/𝑚𝑖. We can then calculate the

velocity and spatial position of atom 𝑖 by the integral of acceleration over time

𝑡:

𝑑2

𝑑𝑡2𝑟𝑖 =

𝑑

𝑑𝑡𝑣𝑖 = �⃗�𝑖

�⃗�𝑖 = �⃗�𝑖0 + �⃗�𝑖𝑡

𝑟𝑖 = 𝑟𝑖0 + �⃗�𝑖

0𝑡 +1

2�⃗�𝑖𝑡2 (3.5)

where 𝑟𝑖and �⃗�𝑖 are the spatial position and velocity of atom 𝑖, respectively, the

superscript “0” is the initial value of the coressponding terms.

Newton’s law of motion is the fundamental rule in MD simulation. From

equation 3.4, the force acting on each atom, as well as the acceleration (from

�⃗�𝑖 = �⃗�𝑖/𝑚𝑖), can be derived. By assigning an initial velocity for each atom,

we can calculate the position and velocity of each atom at 𝑡 + 𝛿𝑡. The new

position of each atom is then used for updating the system at 𝑡 + 𝛿𝑡 .

Equations 3.4 and 3.5 are applied again for deriving the position and velocity

at 𝑡 + 2𝛿𝑡, … etc. Finally, we obtain a trajectory that records the position of

each atom at different time.

3.3.2 Force fields

A force field consists of a set of empirical functions with parameters

derived from experimental data or quantum mechanics calculations for

calculating the potential energy of an atomic system. In a force field, the total

potential is divided into the non-bonded interactions (usually simplified as the

van der Waals and Coulomb terms) and the bonded interaction,

𝑈 = 𝑈𝑉𝐷𝑊 + 𝑈𝐶𝑜𝑢𝑙𝑜𝑚𝑏 + 𝑈𝑖𝑛𝑡 (3.6)

where 𝑈𝑉𝐷𝑊 is the potential for the non-bonded VDW interaction, and

𝑈𝐶𝑜𝑢𝑙𝑜𝑚𝑏 is the potential for the non-bonded Coulomb interaction, and 𝑈𝑖𝑛𝑡 is

the bonded potential.


32

There are many force fields available, of which the AMBER, CHARMM,

and OPLS force fields are widely used for simulating biomolecular systems.

Taking the AMBER force field166 as an example, the total potential can be

expressed as:

𝑉 = ∑ 𝑘𝑙(𝑙 − 𝑙0)2

𝑏𝑜𝑛𝑑𝑠

+ ∑ 𝑘𝜃(𝜃 − 𝜃0)2

𝑎𝑛𝑔𝑙𝑒𝑠

+ ∑𝑉𝑛

2𝑑𝑖ℎ𝑒𝑑𝑟𝑎𝑙𝑠

[1 + cos(𝑛𝛷 − 𝛾)]

+ ∑ 𝑘𝜔(𝜔 − 𝜔0)2

𝑖𝑚𝑝𝑟𝑜𝑝𝑒𝑟𝑠

+ ∑ {4휀𝑖𝑗 [(𝑅𝑚𝑖𝑛,𝑖𝑗

𝑟𝑖𝑗)

12

− (𝑅𝑚𝑖𝑛,𝑖𝑗

𝑟𝑖𝑗)

6

] +𝑞𝑖𝑞𝑗

4𝜋휀0𝑟𝑖𝑗}

𝑛𝑜𝑛𝑏𝑜𝑛𝑑𝑒𝑑,𝑖<𝑗

(3.7)

where the first four terms correspond to the bonded interactions due to bond

stretching, angle bending, dihedral rotation, and improper dihedral bending

(Figure 3.1). The last term utilized the distance-based Lennard-Jones (LJ)

repulsion/dispersion potential and a Columbic potential to accounts for the

van der Waals and electrostatic interactions, respectively. The empirical

parameters in the above equation, including force constants and the reference

values for the distances, angles, and dihedrals, are derived either from

experimental data or quantum mechanics calculations.

Figure 3.1. Illustration of typical bonded terms in a force field.

Equation 3.7 is very efficient in calculation by using the non-redundant

internal coordinates. The redundant problem is easily understood by a simple

example: ethane. An ethane molecule contains 8 atoms, which can be

described either by 8 cartesian or 28 internal (7 bonds, 12 angles, and 9

dihedrals) coordinates. However, in view of its chemical structure, it is not

necessary to fit all the 28 sets of parameters. According to the chemical types


of bonds, angles, and dihedrals, only 5 sets of parameters are required (C–H

and C–C stretching, H–C–C and H–C–H bending, and H–C–C–H rotation).

The parameters fitted in this way, which is referred to as typing rules, can be

applied to other molecules with similar types of internal coordinates. The

atom type is thereby introduced for defining various types of bonds, angles,

dihedrals and improper torsions. The implementation of typing rules has

resulted in the specialization of force fields.167

3.3.3 System initialization

Before the production stage of an MD simulation (equation 3.5), a system

needs to be initialized, which includes the initializations of the coordinates

and velocities of atoms. The initial coordinates for an MD simulation should

ideally be located in an energy minimum.168 This is usually accomplished by

energy minimization, which is especially important for biomacromolecules. If

the system is not energy-minimized, it is possible that the forces acting on

some atoms are too large, leading to that the simulation becomes very

unstable or even crashes. Energy optimization can also save the simulation

time required for the system to reach equilibrium.

Velocity is initialized based on the Maxwell-Boltzmann distribution:

𝑃(𝑣𝑥) = √𝑚

2𝑘𝐵𝑇𝑒𝑥𝑝 [

−𝑚𝑣𝑥2

2𝑘𝐵𝑇] (3.8)

where 𝑃(𝑣𝑥) represents the probability of finding a particle with the mass of

𝑚 and the velocity 𝑣 along the 𝑥 direction. This distribution is actually a

Gaussian distribution. Starting from the randomly generated initial set of

atomic velocities, an equilibration is required to obtain a gaussian distribution

of the velocities.

3.3.4 Integration Algorithm

To update the velocities and coordinates, equation 3.5 needs to be solved

numerically. The Verlet integration scheme169 is commonly used:


34

𝑣(𝑡) =𝑑𝑟

𝑑𝑡=

1

2𝛿𝑡[𝑟(𝑡 + 𝛿𝑡) − 𝑟(𝑡 − 𝛿𝑡)] (3.9)

The velocity of a particle at 𝑡 can be derived from the positions at 𝑡 + 𝛿𝑡 and

𝑡 − 𝛿𝑡. However, 𝛿𝑡 is usually very small in an application (~ 10-15 s), the

term 1/𝛿𝑡 will thus introduce large deviations in the velocity. An improved

version of Verlet algorithm has been developed, which is named leap-frog

algorithm170:

𝑣 (𝑡 +1

2𝛿𝑡) = 𝑣 (𝑡 −

1

2𝛿𝑡) + 𝑎(𝑡)𝛿𝑡

𝑟(𝑡 + 𝛿𝑡) = 𝑟(𝑡) + 𝑣 (𝑡 +1

2𝛿𝑡) 𝛿𝑡 (3.10)

3.3.5 Temperature coupling

Constant-temperature MD simulations have many applications because

many experimental measurements are made under such condition. For

example, experiments for studying protein-ligand systems are usually carried

out at a constant temperature of 300 K.

The temperature of a system is dependent on the kinetic energy (𝐸𝑘):

⟨𝐸𝑘⟩ = ∑𝑚𝑖𝑣𝑖

2

2

𝑁

𝑖=1

=3

2𝑁𝑘𝐵𝑇 (3.11)

where 𝑘𝐵 is the Boltzmann constant. A direct manner to adjust the

temperature is to introduce a scaling factor 𝜆 , which is multiplied to the

velocity 𝑣. The 𝜆 value is derived from:

𝜆 = √𝑇𝑟𝑒𝑞

𝑇𝑐𝑢𝑟 (3.12)


where 𝑇𝑟𝑒𝑞 is the expected temperature after adjustment; 𝑇𝑐𝑢𝑟 is the current

temperature. This method was introduced by Woodcock in 1971.171

In 1984, Berendsen proposed a thermostat that assumes there exists an

infinite heat bath outside the system with constant temperature, which

maintains the temperature of the system around the heat bath temperature.172

The change of the system’s temperature with time is given as

𝑑

𝑑𝑡𝑇(𝑡) =

1

𝜏[𝑇𝑏𝑎𝑡ℎ − 𝑇(𝑡)] (3.13)

where 𝑇𝑏𝑎𝑡ℎ is the temperature of the bath; 𝜏 is the coupling constant, which is

used to describe how much the system is coupled with the heat bath. The

corresponding 𝜆 factor is:

𝜆 = √1 +𝛿𝑡

𝜏[𝑇𝑏𝑎𝑡ℎ

𝑇(𝑡)− 1] (3.14)

3.3.6 Pressure coupling

The pressure can be maintained within a range by simply scaling the

volume, or, in a similar way to the temperature coupling, by using a barostat.

Berendsen pressure coupling is a typical example for controlling the

pressure,172 where the change of the pressure with time is given as

𝑑𝑃(𝑡)

𝑑𝑡=

1

𝜏𝑝

[𝑃𝑏𝑎𝑡ℎ − 𝑃(𝑡)] (3.15)

where 𝑃𝑏𝑎𝑡ℎ is the pressure of the bath and 𝜏𝑝 is the coupling constant. The

new position of atom 𝑖 is then scaled by the corresponding 𝜆:

𝑟𝑖′ = √𝜆

3𝑟𝑖 = [1 − 𝜅

𝛿𝑡

𝜏𝑃

(𝑃 − 𝑃𝑏𝑎𝑡ℎ)] 𝑟𝑖 (3.16)


36

3.4 Quantum mechanics calculations

Quantum mechanics (QM) is established to describe the behaviors of

microscopy particles, such as molecules, atoms, and elementary particles.

Since electrons are explicitly represented, QM calculations are widely used in

the subject of chemistry. In QM, the time-independent Schrödinger equation

is often used for calculating the properties of a molecular system,

𝓗Ψ = 𝐸Ψ (3.17)

where 𝓗 is the Hamiltonian operator, Ψ is the wavefunction (state) of the

system, and 𝐸 is the total energy. Most QM calculations are aiming at solving

this equation numerically.

3.4.1 Hartree-Fock theory

For a many-electron system, it is impossible to solve the Schrödinger

equation exactly. According to the Born-Oppenheimer (BO) approximation,173

the motion of electrons can be decoupled from that of nuclei, because the

mass of a nucleus is much greater than that of the electron. Therefore, the

motion of nuclei is split from the movements of electrons and we can consider

the Hamiltonian operator including only the electron kinetics 𝑻𝒆 , nuclei-

electron potential 𝑽𝑵𝒆 , electron-electron potential 𝑽𝒆𝒆 , and nuclei-nuclei

potential 𝑽𝑵𝑵,

𝓗𝒆 = 𝑻𝒆 + 𝑽𝑵𝒆 + 𝑽𝒆𝒆 + 𝑽𝑵𝑵 (3.18)

By introducing the orbital approximation, it is possible to solve the

Schrödinger equation for a poly-atomic system under the Hartree-Fock (HF)

framework,174 which is a wavefunction-based approach. A Slater determinant

was introduced to approximate the wavefunction,175 which means that the

electron correlation effect is not considered and an electron is independently

moving in a mean field generated by the nuclei and other electrons. The

elements (𝑖) in the Slater determinant are the spin orbitals (𝜑𝑖 ), with the

energies 휀𝑖 , and can be obtained by solving the following equation self-

consistently,


𝑓𝑖|𝜑𝑖⟩ = 휀𝑖|𝜑𝑖⟩ (3.19)

Equation 3.19 is known as the canonical Hartree-Fock (HF) equation, where

the Fock operator 𝑓𝑖 is the one-electron Hamiltonian, including the operator

for the kinetic energy of a single electron, the interactions of the electron with

the nuclei and other electrons, and the exchange interaction of the electron

with the other electrons with the same spin.

To find the energy eigenvalue, equation 3.19 is solved iteratively based on

the so-called self-consistent-field (SCF) procedure.176 If the difference in the

total energies from two consecutive SCF calculations is smaller than the pre-

defined threshold, the SCF calculation is deemed as converged. Otherwise,

the iterative procedure will continue until the convergence is reached.

3.4.2 Density functional theory

In 1964, Hohenberg and Kohn presented a new approach, which is now

known as the density functional theory (DFT), to calculate the energy of a

many-electron system.177 In DFT, the total electron density is as important as

the wavefunction in HF. However, the total density is more efficient to

calculate than the wavefunction at the same level of accuracy. Because for an

N-electron system, the DFT calculation only requires 3 spatial variables,

whereas a wavefunction-based approach needs 4N (3 spatial and 1 spin for

each electron) variables. Modern DFT is constructed under the Kohn-Sham

framework,178 which introduces the “orbital” concept to define the electron

density and express the energy as a function of density 𝜌:

𝜌[𝑟] = ∑|𝜙𝑖(𝑟)|2

𝑁

𝑖

𝐸[𝜌] = 𝑇[𝜌] + 𝐸𝑁𝑒[𝜌] + 𝐽[𝜌] + 𝐸𝑋𝐶[𝜌] (3.20)

where 𝑇[𝜌] is the functional for the kinetic energy, 𝐸𝑁𝑒[𝜌] and 𝐽[𝜌] represent

the nuclei-electrons attraction and electron-electron Coulomb energy,

respectively. The exchange-correlation functional 𝐸𝑋𝐶[𝜌] is comprised by an

exchange term (𝐸𝑋[𝜌]) comparable to that in the Hartree-Fock approach and a


38

“correlation” term ( 𝐸𝐶[𝜌] ). 𝐸𝑋𝐶[𝜌] is unknown and can only be derived

approximately. Equation 3.20 is also solved iteratively by the SCF procedure.

There are many 𝐸𝑋𝐶[𝜌] functionals available, including local density

approximation (LDA), general gradient approximation (GGA), and hybrid

functionals, though the accurate 𝐸𝑋𝐶[𝜌] functional is still unknown. Of these

functionals, B3LYP is the most commonly used functional in treating

chemical reactions.179 B3LYP is a hybrid exchange functional that involves

the HF exchange term,

𝐸𝑋𝐶𝐵3𝐿𝑌𝑃 = (1 − 𝑎)𝐸𝑋

𝐿𝐷𝐴 + 𝑎𝐸𝑋𝐻𝐹 + 𝑏𝛥𝐸𝑋

𝐵𝑒𝑐𝑘𝑒 + (1 − 𝑐)𝐸𝐶𝐿𝐷𝐴 + 𝑐𝐸𝐶

𝐿𝑌𝑃 (3.21)

where 𝐸𝑋𝐵𝑒𝑐𝑘 is the Becke88 exchange functional and 𝐸𝐶

𝐿𝑌𝑃 is the correlation

functional developed by Lee, Yang and Parr.179 The empirical parameters are

introduced for the sake of accuracy, where a=0.1161, b=0.9262, and

c=0.8133.179

The electron correlation energy not included in HF is in fact included in

DFT, which makes DFT a more accurate method than HF. There are some

known problems in the current exchange-correlation (XC) functionals. The

most concerned one is that the dispersion interaction is not included. Solutions

have been constructed to account for the dispersion interactions in DFT

calculations. The frequently used one in the theoretical studies of enzyme

catalytic reactions is to add the attractive empirical long-range dispersion

correction directly (DFT-D)

𝐸𝑑𝑖𝑠𝑝 = −𝑠6 ∑𝐶6

𝑖𝑗

𝑅𝑖𝑗6 𝑓𝑑𝑎𝑚𝑝(𝑅𝑖𝑗)

𝑖𝑗

(3.22)

where 𝑓𝑑𝑎𝑚𝑝(𝑅𝑖𝑗) is a damping function and 𝑠6 is the functional dependent

scaling factor.180 Besides dispersion interactions, the self-interaction error

(SIE) is another defect of DFT, because the electron’s Coulombic repulsion

with itself is not exactly canceled in 𝐸𝑋𝐶[𝜌]. SIE does not affect the energy

too much in most systems except those having loosely bound electrons.181


3.4.3 Transition state theory

The transition state theory (TST) is originally used to qualitatively explain

the process of a chemical reaction and the reaction rate of an elementary

reaction.182 TST assumes that there exist one or more transition state (TS)

structures along a reaction path. A transition state is often considered together

with the energy barrier of a chemical reaction. The path(s) along which the

reactants become the products is called the reaction coordinate(s) and the

corresponding energies constitute the potential energy surface (PES).

According to the BO approximation, a chemical reaction can be described as

nuclei move on the PES from one minimum to another. TST assumes that

there is a minimum-energy path along the reaction coordinate for a chemical

reaction. Along this minimum-energy path, the transitional state is a

configuration of the reaction coordinates, which could divide the reactant and

product on PES. The corresponding geometry splitting the reactants and

products is the TS structure. The theory also assumes that the energy of the

TS structure is a Boltzmann distribution. Therefore, the reaction constant (𝑘)

is derived by

𝑘 = (𝑘𝐵𝑇

ℎ) exp (−

𝛥𝐺

𝑅𝑇)

𝛥𝐺 = 𝛥𝐻 − 𝑇𝛥𝑆 (3.23)

where 𝛥𝐻 is the enthalpy change and is the entropy change 𝛥𝑆. S can be

calculated approximately from the vibrational frequencies.

There are stationary points on the PES. A stationary point means that it is

an inflection point between two different trends on the PES. Usually, there are

more than one such points on the PES, which are characterized by the first

and second derivatives of the energy with respect to the nuclear coordinates

(atom positions). The first and second energy derivatives versus position are

gradients and hessian matrix, respectively. A stationary point corresponds to

its gradient to be zero on the PES. If all the eigenvalues of elements in the

hessian matrix are positive, the stationary point represents a local minimum.

For the saddle point, it has one or more than one negative eigenvalues of the

Hessian with the rest eigenvalues being positive. Notably, the saddle point


40

with only one negative eigenvalue represents the transition state, where the

minimum-energy path “passes” through this point.

The discussion above is based on the elementary reaction theory. The

energy difference between the local minimum and transition state is the rate

limiting barrier, which decides the reaction rate. For a reaction contains

multiple barriers, the rate limiting barrier differs in solution and gas phase.

Generally, the rate limiting step in solution is determined by the largest barrier

of the TS with respect to the previous local minimum.

3.5 Quantum mechanics/molecular mechanics calculations

MD simulations using force fields are based on classical mechanics and

provide details about the behaviors of atoms in a large molecular system, such

as a protein, an enzyme, or a polysaccharide. QM methods can provide the

details about the behaviors of electrons in a molecule, which are therefore

suitable for describing chemical reactions and other properties related to the

electronic structure. The system that can be treated by a QM method is usually

much smaller than that by an MD simulation. As a compromise, the combined

quantum mechanics/molecular mechanics (QM/MM) approaches have been

proposed for treating chemical reactions occurring in large systems. QM/MM

has been successfully used in the studies of P450s.147,183

The basic idea in QM/MM is to partition the system into different regions

treated with different methods. In a classical two-layer model, the system is

partitioned into the QM and MM regions, where the region of interest is

treated at the QM level. In an enzymic reaction, atoms involved in the

catalytic process, such as the substrate, protein residues, water molecules and

co-factors, are usually involved in the QM region. The MM area is treated

with molecular mechanics, which uses a force field to model the interactions

between the atoms. The partition also introduces a boundary between the QM

and MM regions. Processing the boundary is critical in QM/MM calculations,

because the two regions interact with each other. Particularly, in the QM/MM

calculations of enzymic reactions, partitioning the system often across some

covalent bonds.


In a classical two-layer QM/MM method, the QM region is only treated

with QM method, with the rest of the system being the MM region treated

with the MM method. The interactions between the QM and MM regions are

then added to evaluate the total energy of the system:

𝐸𝑠𝑦𝑠𝑡𝑒𝑚 = 𝐸𝑄𝑀 + 𝐸𝑀𝑀 + 𝐸𝑄𝑀−𝑀𝑀 (3.24)

where 𝐸𝑄𝑀−𝑀𝑀 is the QM/MM coupling energy that accounts for the

interactions between the QM and MM regions. It generally includes the

bonded and nonbonded interactions. And the nonbonded interaction is divided

into the vdW and electrostatic terms:

𝐸𝑄𝑀−𝑀𝑀 = 𝐸𝑄𝑀−𝑀𝑀𝑏𝑜𝑛𝑑 + 𝐸𝑄𝑀−𝑀𝑀

𝑣𝑑𝑊 + 𝐸𝑄𝑀−𝑀𝑀𝑒𝑙𝑒𝑐𝑡𝑟𝑜𝑠𝑡𝑎𝑡𝑖𝑐 (3.25)

Equation 3.24 represents the so-called “additive” scheme. An alternative

QM/MM scheme was proposed by Maseras and co-workers in 1995,184 in

which the MM energy of the QM region (named “model”) needs to be

subtractive. In such a “subtractive” scheme, the MM energy of the entire

system (named “real”) and the QM energy of the “model” part are calculated.

Since the “real” part contains the “model” part, the MM energy of the “model”

part (𝐸𝑀𝑀,𝑚𝑜𝑑𝑒𝑙) is double counted and needs to be subtracted:

𝐸𝑠𝑦𝑠𝑡𝑒𝑚 = 𝐸𝑄𝑀,𝑚𝑜𝑑𝑒𝑙 + 𝐸𝑀𝑀,𝑟𝑒𝑎𝑙 − 𝐸𝑀𝑀,𝑚𝑜𝑑𝑒𝑙 (3.26)

The subtraction in equation 3.26, i.e. 𝐸𝑀𝑀,𝑟𝑒𝑎𝑙 − 𝐸𝑀𝑀,𝑚𝑜𝑑𝑒𝑙, is actually the

effect of the environment (protein, solvent, etc.) on the small model system

( 𝐸𝑄𝑀,𝑚𝑜𝑑𝑒𝑙 ). As a result, it can be deemed that the subtractive scheme

extrapolates the size of QM calculations by adding the environmental effect,

which is inexpensively evaluated by the MM method. This is also the typical

mechanical embedding approach.185,186 In this manner, the system can be

easily partitioned into more than 2 layers (𝑛 layers), which is referring to as

the “Our own N-layered Integrated molecular Orbital and molecular

Mechanics” (ONIOM) method.187,188


42

The ONIOM method implemented in the Gaussian program is a classical

implementation of the subtractive scheme. Similar to the additive QM/MM

scheme, hydrogen atom capping is used to treat the broken bonds between the

QM and MM regions. The coupling between the MM and QM regions,

especially the electrostatic interaction, is difficult to be evaluated. In the

mechanical embedding approach, the atomic charges are fixed and thus the

polarization effect of the MM environment to the QM wavefunction is not

included. To improve the accuracy of electrostatic interaction calculation, the

so-call electronic embedding approach introduces a term related to the atomic

charge of the MM environment to the QM Hamiltonian:

ℋ𝑄𝑀,𝑚𝑜𝑑𝑒𝑙𝑒𝑒 = ℋ𝑄𝑀,𝑚𝑜𝑑𝑒𝑙 − ∑ ∑

𝑆𝑀𝑞𝑀

𝑅𝐸𝑀𝑀𝐸

+ ∑ ∑𝑁𝐾𝑆𝑀𝑞𝑀

𝑅𝐾𝑀𝑀𝐾

(3.27)

where the 𝐸, 𝑀, and 𝐾 represent the QM electron, MM atom, and QM atom,

respectively.189 For a MM atom 𝑀, 𝑞𝑀 is the embedded charge and 𝑆𝑀 is a

scale factor. 𝑁𝐾 is the nuclear charge of the QM atom 𝐾 . In this manner,

although the QM/MM electrostatic interaction originates from the fixed MM

point charges, it is involved in the self-consistent calculation for the QM

region. However, the MM charges near QM region, especially those from the

linked hydrogen atoms, may leads to the over consideration of polarization for

the QM wavefunction.

Deciding when the electronic embedding approach should be used to treat the

QM/MM electrostatic interactions seems to be tricky and system-

dependent.188 It has been noted by Morokuma and co-workers that the

QM/MM electrostatic interaction is less important for geometries.188 For the

C–H amination mediated by P450, Li and co-worker reported that the

electronic embedding has a small impact on the activation barriers.190 In the

ONIOM calculation involved in this thesis, the electronic embedding is not

considered for all systems.

Chapter 4 P450 catalytic selectivity

43


4.1 Vitamin K1 ω-hydroxylation by CYP4F2 (Paper I).

The cytochrome P450 4 family (CYP4) is responsible for the ω-

hydroxylation of fatty acids. The substrates that are ω-hydroxylated by CYP4

span from short-chain (C7-C10) to long-chain (C22-C26).79,191 It is worth to

note that the ω-1 or ω-2 positions can sometimes be hydroxylated by different

CYP4 isoforms, whereas the ratios of ω-, ω-1, and ω-2 varied.79 The structural

features of the ω-hydroxylation were firstly investigated through mutations

and UV spectra, which confirmed that there exists a covalent linkage between

the CYP4 enzyme and heme cofactor.76,192 It is hypothesized that this linkage

is an ester bond between the carboxyl group of a glutamic acid of I-helix and

the 5-methyl group of heme. The existence of the ester bond was confirmed

by the first crystal structure of CYP4B1 in 2017.79 Compared to the apo

structure of CYP3A4, the distance between the B'-helix and C-terminal loop

in this structure is much smaller (Figure 4.1), which restricts the

conformation of the substrate’s aliphatic chain.193 Additionally, there is a

hydrogen bond between Tyr110 of the B'-helix and the 7-propionate group of

heme. These features are believed to be related to the ω-hydroxylation.

Figure 4.1. Comparison of the crystal structures of CYP4B1 (PDB: 5T6Q,

colored in imperial green) and CYP3A4 (PDB: 1TQN, colored in white).


44

Although the heme-bonded glutamic acid is conversed in CYP4 ω-

hydroxylates, the dynamics of the active site residues for the ω-hydroxylation

is still unknown. In this work, we studied vitamin K1 (VK1) oxidation by the

human CYP4F2 to understand the role of reactivity and active site residues in

the ω-hydroxylation. The reactivity profile was investigated by two trimmed

conformers of VK1 (Conformers 1 and 2, Figure 4.2) with the prevailing

truncated Cpd I model. By considering the dispersion corrections, the general

activation barriers were ranked as ω-1 < ω-2(R/S), ω-3(R/S) < ω sites. This

trend is in accordance with the bond strength for the tertiary-, secondary, and

primary C–H bonds. However, the gap between the ω and the most reactive

ω-1 site is about 4-5 kcal/mol in both conformers. By considering more than

one abstracted hydrogen atoms at the same site with different substrate

conformations, the error is reduced to about 1 kcal/mol.

Figure 4.2. Two conformers of the trimmed VK1 models with the calculated

activation barriers (kcal/mol, D3 dispersion correction included) presented in

parenthesis.

Next, homology modeling was conducted to obtain the initial structure of

the human CYP4F2 based on the rabbit CYP4B1 (PDB: 5T6Q), followed by

three independent 100-ns MD simulations to relax the structure. The

molecular docking of VK1 into the active site of a representative snapshot

was then carried out (Figure 4.3). From the MD-relaxed structure, we

obtained a model with a fluctuating active site, in which the outer and inner

cavities merged together due to the fluctuations of Leu504, Phe124 and


45

Val397. This conformation is beneficial for the binding of VK1, which is

much larger in size than the native substrate of CYP4B1. Molecular docking

can reproduce the NAC (see the definition in section 1.5.3) of VK1 with one

of its ω sites oriented to the oxo moiety of Cpd I. In the outer region, the

aromatic ring moiety of VK1 was trapped in a hydrophobic region formed by

Trp59, Trp61, Met92, and His236.

Figure 4.3. (A), (B) Superimposition of the initial CYP4F2 homology model

(colored in green) and the representative snapshot from the MD relaxation

(colored in cyan or marine blue); (C), (D) The top-1 ranked binding mode of

VK1 in the inner and outer active site of CYP4F2.

Starting from the docked CYP4F2-VK1 complex, three independent 400-

ns MD simulations were conducted (Figure 4.4). It is worth to note that the

flexible aliphatic side chain of VK1 did not fluctuate so much during the MD

simulations, despite the freely movement of VK1’s aromatic ring in the outer

region of the active site. Distance and angle criteria were defined to

quantitively assess the NAC for VK1 in each snapshot. The number of NACs


46

for the ω-site is the largest, which indicates that the ω-site is the most

accessible site to the catalytic center. Even the MD simulations were started

with the NAC of a non-ω site, the ω site still maintains the highest

accessibility in the MD simulations. This indicates that the active residues of

CYP4F2 are strongly favorable for the ω-hydroxylation of VK1. This is

further supported by the ONIOM calculations, which indicate that the

activation barrier for the ω and ω-1 sites are 15.4 and 16.6 kcal/mol,

respectively.

Figure 4.4. (A) (B) Side- and top-views of the representative snapshots in the

three independent MD simulations of CYP4F2-VK1; (C) the geometrical

criteria used to describe the NAC of a ligand site; (D) the accessibility profile

(number of NAC snapshots) of the ω, ω-1, ω-2R, ω-2S, ω-3R, and ω-3S sites;

(E) the accessibility profile of these sites without considering the angle

criterion; (F) the accessibility profile of these sites in the additional 400-ns

MD simulation started with the NAC of ω, ω-1, ω-2R, and ω-2S sites, which

are denoted as “W”, “W1”, “W2R”, and “W2S” systems, respectively.


47

4.2 Oxidation of testosterone and dihydrotestosterone by CYP3A4 and

19A1 (Paper II).

In this work, by using molecular dockings, MD simulations, and QM and

ONIOM calculations, I evaluated the regio- and stereoselectivity of the

hydroxylation of testosterone (TES) and dihydrotestosterone (DHT) catalyzed

by CYP3A4 and 19A1 (Figure 4.5).

Figure 4.5. Conformations of TES (marine blue) and DHT (gray). The

experimental SOMs and the corresponding hydrogen atoms are depicted in

spheres with the number labeled. The selectivity of hydroxylation by

CYP3A4 and 19A1 is summarized in the table below.

Both the docking results and MD simulations can interpret well the SOMs

of TES and DHT by CYP3A4 and 19A1. Molecular docking results indicate

that there are two binding modes of TES in the active site of CYP3A4,

namely 17-OH_UP and 17-OH_DOWN. Similar to the work in paper I, the

accessibility for a ligand site was evaluated by assessing the number of “NAC

snapshots” during the MD simulations, in which the ligand site adopted a

near-attack conformation. It should be noted that for these MD systems, not

all the accessibilities (Figure 4.6) are in line with experiments. For example,

for the docking mode corresponding to the NAC of DHT’s 2β site, namely,

DHT-2β_3UA1, the most accessible site was the 4β site (red columns in


48

Figure 4.6B). However, sites 18 and 19 were still dominating the NAC

snapshots in the CYP3A4 system, indicating that sites 18 and 19 were more

accessible to the catalytic center than other sites. In the MD simulations of

CYP19A1, site 19 was the most accessible site for both TES and DHT.

Figure 4.6. Accessibility profiles for the MD systems. The percentage is

calculated from the number of NAC snapshots for the accessed site divided by

the number of NAC snapshots for all the accessed sites.

The reactivities for the sites in TES were calculated using the prevailing

truncated Cpd I model without considering the protein environment. The

calculated activation barriers correlate well with the C–H bond dissociation

energies (BDEs). In contrast to the trend observed in the MD simulations,

sites 18 and 19 are the most inactive sites because of the high activation

barriers (Table 4.1). Additionally, the stereoselectivity of TES hydroxylation

by CYP3A4 can be well explained by the site reactivities.


49

Table 4.1. Activation barriers for TES (kcal/mol)

Sites with D3a without D3b

1α 18.7 14.4

1β 19.1 13.5

2α 15.4 13.4

2β 13.0 9.9

6α 18.1 12.2

6β 10.3 5.3

15α 17.9 14.9

15β 15.5 10.2

18 20.4 13.0

19 22.8 18.3

8 21.1 10.0 a. Calculated using the B3LYP functional with the BS2 basis set and the ZPE

correction using the BS1 basis set. b. Calculated using the B3LYP-D3 functional and Becke-Johnson damping

with the BS2 basis set and the ZPE correction using the BS1 basis set.

Both the ligand reactivities and protein environment play roles in

determining the regio- and stereoselectivity of hydroxylation mediated by

P450s. But how important the roles are varying for different isoforms.

Summing up the results, we see clearly that the protein environ dominates the

regioselectivity of the hydroxylation of TES and DHT by CYP19A1.

However, the dominating factors affecting the regio-selectivity of

hydroxylation by CYP3A4 differ for TES and DHT. The ONIOM calculations

were then employed to incorporate both reactivity and accessibility for

interpreting the complex roles of reactivity and accessibility in the

hydroxylation of TES and DHT. The activation barriers calculated by

ONIOM correlate well with the experimental results. The activation energies

for site 19 are lower in the DHT than in the TES systems. From the ONIOM

calculations of the CYP19A1 systems, we found that site 19 corresponds to

the lowest-barrier site amongst all the accessible sites (19, 1β, and 2β). To

understand the difference in the activation barriers, the spin natural orbital

(SNO) analysis was carried out. The QM region corresponds to an open-shell

system including several protein residues. In the classical HAT mechanism,

the SNO is mainly localized on the direction breaking the C–H bonds.194 In


50

this study, it seems that when the occupied α SNO distributes along the

hydrogen atom transferring direction, the corresponding TS is stable (Figure

4.7). We believe that a modest difference in the P450 active site configuration

could significantly affect the site-preference for the HAT process.

Figure 4.7. SNO distributions for the TS structures for site 19 in the CYP3A4-

DHT systems: (A) the α electron density in 17-OH_UP; (B) the β electron

density in 17-OH_UP; (E) the α electron density in 17-OH_DOWN; (F) the β

electron density in 17-OH_DOWN, and the CYP19A1 systems: (C) the α

electron density in CYP19A1-TES; (D) the β electron density in CYP19A1-

TES; (G) the α electron density in CYP19A1-DHT; (H) the β electron density

in CYP19A1-DHT.

Chapter 5 P450 plasticity

51


5.1 Probing the CYP2B4 plasticity by MD simulations (Paper III).

The plasticity of P450 is an important factor governing the substrate

promiscuity.83,195,196 The X-ray crystallography experiment has provided a

wealth of information on the structural features of P450s,85 which is beneficial

for understanding the structural flexibility of P450s. In the mammalian P450s,

the rabbit CYP2B4 has been frequently used as a model P450 isoform for

biochemical and biophysical studies. In this work, I analyzed the structural

features of the available CYP2B4 crystal structures, followed by MD

simulations to study the dynamics of open and closed structures with and

without the membrane environment, and umbrella sampling to obtain the free

energy profile for the conformational change.

To date, there are 21 crystal structures of CYP2B4 available. Root-mean-

square deviation (RMSD) and three distance indices were used to analyze the

features of the open and closed structures (Figure 5.1). Five structures with

the PDB codes of 1PO5, 2BDM, 3R1B, 3G5N, and 3G93 were found

significantly different from the closed structures.84,107,197,198 These structures

are defined as open or intermediate structures, in which the formation of a

dimer conformation were identified. The range of the distance indices were

summed up in Table 5.1.

Table 5.1. Averaged values (Å) of the dist_1, dist_2, and dist_3 for the open,

intermediate, and closed crystal structures. dist_1a dist_2a dist_3a

open 23.2 ± 2.1 22.4 ± 6.5 21.3 ± 2.9

intermediate 14.4 ± 0.3 25.8 ± 0.6 31.0 ± 1.8

closed 14.5 ± 0.4 9.1 ± 0.1 15.4 ± 0.9

a. dist_1: the distance between the centroids of the Cα atoms of residues 213-

218 (F'-helix) and 42-45 (A'-helix); dist_2: the distance between the centroids

of the Cα atoms of residues 102-108 (B-C loop) and 231-235 (G-helix);

dist_3: The distance between the centroids of the Cα atoms of residues 102-

108 (B-C loop) and 476-478 (C-Terminal loop).


52

Of the three distance indices, dist_2 has the largest difference (~13 Å)

between the closed and open structures, indicating that the gap between the F-

G cassette and B-C loop contributes mostly to the open structure. The open or

intermediate conformations are found to have the dimer formation, regardless

of the mutation and ligand-binding states. To deeply understand the structural

flexibility and dynamics of CYP2B4, the two apo structures with high

crystallography resolution, the closed-form 3MVR and open-form 1PO5, were

selected for the subsequent MD simulations.

Figure 5.1. Illustration of dist_1, dist_2 and dist_3 using the open structure

(PDB: 1PO5) as an example. The Cα atoms used for defining the distances are

depicted in white spheres and the centroids of the Cα atoms are represented by

blue spheres. The F-G cassette and B-C loop are colored in yellow and

magenta, respectively.

In this work, 5 systems with and without the membrane environment were

constructed for the unbiased MD simulations, as illustrated in Table 5.2.

Table 5.2. Simulation systems in this project

System description simulation

time

number

of atoms

1PO5_mon_w monomer open structure in water 1 μs 60151

1PO5_dim_w dimer open structure in water 1 μs 98409

3MVR_w closed structure in water 1 μs 58896

1PO5_mon_mem monomer open structure on membrane 1 μs 112374

3MVR_mem closed structure on membrane 1 μs 112976

The long-time unbiased MD simulation results indicate that the open


53

conformation in the monomer form is not stable and trends to transit to an

intermediate structure that is not seen in the crystal structures. The MD

simulations also verify that the open conformation in aqueous solution is

stabilized by the formation of the dimer. However, the local conformation

may still be unstable, such as the B-C loop, supporting the existence of great

plasticity in CYP2B4 as observed in the crystal structures.

For the simulations with the membrane environment considered, the open

structure (1PO5_mon_mem) was also not stable and changed to an

intermediate conformation. The root-mean-square fluctuation (RMSF)

analysis indicates that the B-C loop and F-G cassette areas are more flexible

than the other residues around the active site.

Comparing the open structures in water and membrane environments, it is

clear that the membrane can stabilize the helix conformation in the active site,

especially the F-G cassette. The dynamical network communication analysis

revealed that the communication between the F-G cassette and B-C loop,

which determines the length of dist_2, is much stronger in 1PO5_mon_w than

in 1PO5_mon_mem (Figure 5.2). In these two systems, the Asp105 in B-C

loop was able to form hydrogen bonds with the residues in the F-G cassette,

indicating that it may play an important role in transiting the CYP2B4 from

the open to the closed structure.

Figure 5.2. Dynamical network analysis for the 1PO5_mon_w and

1PO5_mon_mem systems.

The umbrella sampling along the path CV was carried out to estimate the


54

free energies for open-to-closed transition. The open structures as a monomer

form are not stable in aqueous solution or on membrane, because of the

increased free energies for transiting the closed-state F-G cassette to the open

state (Figure 5.3). This is in consistent with the distance, RMSD, and RMSF

analysis of the earlier MD simulation results for the open and closed

monomers (1PO5_mon_w versus 3MVR_w and 1PO5_mon_mem versus

3MVR_mem). The free energy for converting the closed-state F-G cassette to

the open state on membrane is ~14 kcal/mol lower than that in water.

Figure 5.3. (A) Reference structures for pulling the F-G cassette from the

closed (red tubes) to open (blue tubes) conformations. The intermediates are

represented by transparent tubes. (B) Free energy profile for pulling the F-G

cassette from the closed to open conformations with and without membrane.


55

5.2 Homotropic Cooperativity of Midazolam Metabolism by CYP3A4

(Paper IV).

The high plasticity of a P450 also allows multiple ligands to bind

cooperatively to its productive site or allosteric site, as demonstrated in

section 1.5.4. Experimentally, hydroxylation of midazolam (MDZ) occurs at

the C1' and C4 sites. And the cooperative binding of MDZ can change the

ratio of the C1'- and C4-hydroxylated products.145 In this work, I used QM

calculations, MD and Gaussian-accelerated MD (GaMD) simulations to

investigate the homotropic cooperativity phenomenon of midazolam (MDZ)

metabolism by CYP3A4.

The rankings of the activation barriers for these sites are: H41 < H1'1 <

H42 < aromatic sites. The rebound steps were also calculated for the H41,

H1'1, and H42 sites. The results indicate a chirality inversion for the 2IMH41

species (Figure 5.4), which produced the H42 alcohol. Such inversion was

caused by breaking the hydrogen bond between MDZ N5 atom and the heme

bound OH group in the rebound step with S=1/2.

Figure 5.4. Energy profiles for the H41, H1'1, and H42 sites with different

spin states along the reaction coordinates.


56

Besides self-docking, the top 1 ranked pose in the 4K9T system has the

structure closest to the crystal structure (PDB: 5TE8) with the RMSD of 1.86

Å (Figure 5.5). A second MDZ molecule can also be docked into the

productive site of 4K9T, which interacts with the phenylalanine cluster on the

top of the CYP3A4 productive site. The allosteric site characterized by the

structural work of CYP3A4 (PDB: 1W0F) was also considered in this study.

Docking experiment indicates that the MDZ molecule can fit this site better

than the co-crystallized progesterone. The MDZ molecule in the allosteric site

mainly forms π-π interactions with Phe219 and Phe213. Interestingly, Phe213

is also interacting with the second docked MDZ.

Figure 5.5. Binding modes of MDZ predicted by molecular dockings. (A).

Self-docking of MDZ (colored in marine blue) into the 5TE8 (colored in

white); (B). Comparison of the top 1 ranked pose in 4K9T (denoted as MDZP1)

and 5TE8 (colored in white); (C). Comparison of MDZP1 and the 2TSH41

structures (colored in magenta); (D) The binding mode of the second MDZ

(denoted as MDZP2) with 4K9T (colored in marine blue); (E), (F) Docking of

the third MDZ molecule (denoted as MDZA1) into the allosteric site. The

MDZP1, MDZP2, and MDZA1 are depicted in marine blue, yellow, and cyan

sticks, respectively. The progesterone is colored in orange.

MD simulations of the docked complex systems are summarized in Table

5.3. For the P1 system, the conformation of MDZP1 in the representative


57

snapshot of the major cluster was closer to that in the 5TE8 structure. In 5TE8,

the fluorophenyl ring of MDZ interacts with the backbone of the sank F-F'

loop. In our MD simulations, the F-F' loop did not sink into the productive

site and the fluorophenyl ring of MDZ is interacting with the Phe108 in the B-

C loop, the side chain of which has a great shift from outward to inward. For

the P1P2 system, the existence of the second MDZ, MDZP2, affects the

dynamics of MDZP1, leading to C1 being far away from the oxo. The MDZP2

also repels the B-C loop to move outward. The distance analysis of both

systems could explain the experimental results. It is also found that the

mobility of MDZP1 in the P1P2 system is less significant than in the P1

system by RMSD analysis.

Table 5.3. MD systems in this work

System Ligand(s)a cMD cMD2b GaMD

P1 MDZP1 1.5 μs \ 1.5 μs

P1P2 MDZP1+MDZP2 1.5 μs \ 1,5 μs

A1 MDZA1 750ns 1.0 μs \

A1P1 MDZA1 + MDZP1 750ns 1.0 μs \

A1P2 MDZA1+MDZP2 750ns 1.0 μs \

A1P1P2 MDZA1+MDZP1+MDZP2 750ns 1.0 μs \

a. See Figure 5.5 for the positions of different MDZ molecules in the

productive and allosteric sites of CYP3A4 (4K9T) b. Unbiased MD simulation without the membrane and TMH

However, MDZA1 is unstable in all the A1, A1P1, A1P2, and A1P1P2

systems, though for the docking ranked top-1 pose, MDZA1 was found to fit

well to the allosteric site. In these systems, MDZA1 was found to move deep

into the membrane, indicating that this allosteric site is a temporary binding

site for MDZ.

The energy profiles for the homotropic MDZ binding were further

evaluated by the GaMD simulations for the P1 and P1P2 systems (Figure 5.6).

The distances C1'-oxo and C4-oxo were selected as CVs for analysis and

energy reweighting. The reweighted PMF indicates that there are two major

local minimums for the P1 system, which reflects that the hydroxylation


58

favors at sites C1' and C4, respectively. For the P1P2 system, only an

expanded local minimum was found on the PMF surface. The representative

structure for this local minimum exhibit similar values for the C1'-oxo and

C4-oxo distances.

Figure 5.6. Reweighted PMF surfaces projected onto the selected CVs for the

P1 (A) and P1P2 (B) systems.

Chapter 6 Summary

59

Chapter 6 Summary

The P450 family represents one of the most versatile oxidative enzymes,

which catalyze the oxidation of a broad range of substances. Because of their

important roles in chemistry, biology, biophysics, and medicinal science,

enormous efforts have been made to understand the relationships between

structures, mechanisms, and functions of P450s. The catalytic selectivity of

P450 mediated reactions plays vital roles in drug metabolism. It is recognized

that the substrate reactivity and the active site environment, or accessibility,

are decisive factors governing the selectivity for the P450 catalytic C–H

hydroxylation, which takes place via the HAT and “radical rebound”

mechanisms. To study the catalytic selectivity of P450s, multiple theoretical

techniques have been applied in this thesis, including homology modeling,

molecular docking, MD simulation, QM, and ONIOM. In paper I, the ω-

hydroxylation of vitamin K1 by CYP4F2 was investigated in detail. For the

hydroxylation reactions mediated by CYP4F2, the active site environment is

more decisive than reactivity for determining the selectivity. In paper II, more

hydroxylation reactions were studied. The active site environments of

CYP3A4 and CYP19A1 favor the 19-hydroxylation for TES and DHT. And

the 19-hydroxylation is more favorable in the active site of CYP19A1.

However, the 6β site of TES is highly reactive, which is more decisive in the

relatively large active site of CYP3A4 than in the active site of CYP19A1.

Therefore, both the reactivity and the enzyme active site environment

(accessibility) are important for the regio- and stereoselectivity of P450

mediated hydroxylation.

Thanks to the development of X-ray crystallography, a number of P450

structures with and without co-crystallized ligands are available. These

structures indicate that P450s are highly flexible. The structural flexibility is

fundamental for P450s to catalyze a broad range of substrates as well as for

the ligand cooperativity. In paper III, the flexibility and dynamics of the open

and closed structures of CYP2B4 were explored by MD simulations. The role

of membrane embedding and the key residues in the conformational dynamics

were identified. In paper IV, the homotropic cooperativity of MDZ binding to

CYP3A4 was studied by the QM calculations and MD simulations. QM

Chapter 6 Summary

60

calculations indicates an interesting chirality inversion in the rebound step of

the C4 hydroxylation. MD simulations demonstrated that the allosteric size

was found to be a temporary site for the MDZ binding. The binding of the

second MDZ molecule was found to restrict the mobility of the first MDZ and

change of the active site conformation, which leads to the occurrence of the

conformation that favors the C4 hydroxylation.

References

61

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