Predicting activity using the electrostatic ...

Predicting activity using the electrostatic

complementarity of protein-ligand complexes

Matthias Bauer

© CressetCONFIDENTIAL © CressetCONFIDENTIAL

> Introduction

> Theoretical background of

computing electrostatic

complementarity

> Case studies

> Conclusion and Outlook

Overview


Why electrostatic scoring?

> Electrostatic interactions between small molecules and their respective receptors are a key contributor to the binding free energy of binding ΔG

(enthalpic component)

> Assessing the electrostatic match between ligands and binding pockets

provides important insights into why ligands bind and what could be changed

to improve binding

> Informs design of polar / enthalpic binders, which have typically better

selectivity and pharmacokinetic parameters than entropic binders and were

suggested to be ‘better’ drugsHann et al. Nat Rev Drug Discov 2012; Med Chem Comm 2011

> Our clients are interested in electrostatic scoring


> Electrostatic interactions types> Hydrogen bonding (classical and weak)

> Ionic

> Cation-π

> π-π

> Lone-pair sigma-hole (e.g., halogen bonding)

> Multipolar (e.g., fluorine bonding)

> Directionality of some electrostatic interactions can

not be properly described by atom-centered

charges used in classical force fields, e.g., halogen

bonding, charge anisotropy of carbonyl / backbone

amides, hydroxyl groups, aromatic rings

> Quantum-mechanical calculations of ESP surfaces

possible but slow / expensive

Electrostatic Interactions

Adapted from Wilcken et al. JMC 2013


Anisotropic charge distribution with XED force field

> The polarizable XED force-field is an excellent base for calculating

electrostatic properties

> Description of anisotropic atomic charge distributions at relatively modest

computational costs

Strongly negative

XED charges

Neutral XED

charge


XED charges more negative

in proximity to oxygen lone

pair orientation

Anisotropic charge distribution with XED force field

> The polarizable XED force-field is an excellent base for calculating

electrostatic properties

> Description of anisotropic atomic charge distributions at relatively modest

computational costs


> Electrostatic potential and complementarity calculations are an extension of protein interaction potential function in Flare™ which calculates protein fields

> Flooding with probe atoms (vdW parameters of oxygen atom) and calculation of interaction potentials at each point

Coulomb force

Interaction potentials

Cheeseright, Mackey et al. JCIM 2006


> Calculation of electrostatic potential only (without vdW term)

> More detailed and distance-dependent dielectric function needed for proteins

(contain more charges) using dielectric function described by Mehler

Coulomb force

Electrostatic potential

Lorentz-Debye-Sack theory

Mehler et al. Theor. and Comput. Chemistry 1996

𝐷𝑀𝐸 𝑟 = 𝐴 +𝐷0 − 𝐴

1 + 𝑘𝑒−λ 𝐷0−𝐴 𝑟

Cheeseright, Mackey et al. JCIM 2006

A= -8.5525; k= 7.7839; λ = 0.003627; D0 = 78.4 at 25 ˚C

Angstrom

DM

E


Electrostatic complementarity

> First a surface is placed on the ligand

> For each vertex on the surface, electric potential due to the ligand and to the protein are computed

> Values clipped to a maximum (roughly corresponding to the maximum potential of a water molecule) and added together

> Complementarity score at vertex = (1 −𝐸𝑆𝑃𝑙𝑖𝑔𝑎𝑛𝑑 + 𝐸𝑆𝑃𝑝𝑟𝑜𝑡𝑒𝑖𝑛

𝑀𝐴𝑋 (𝐸𝑆𝑃𝑙𝑖𝑔𝑎𝑛𝑑,𝑝𝑟𝑜𝑡𝑒𝑖𝑛))

perfect electrostatic complementarity = 1 (green)

both potentials zero = 0 (white color)

perfect electrostatic clash = -1 (red color)

> Solvent-exposed portions of the ligand contribute little information resulting values are scaled down at points on the ligand surface which are too far away from any protein atom (linear downweightingfrom ≥ 3 Å)


Biotin-Streptavidin example

> Visual inspection of electrostatic potential (Biotin-Strepavidin)

red = positive potential and blue = negative potential

180˚ flip

XED ESP surface of Streptavidin XED ESP surface of Biotin


Biotin-Streptavidin example

> Visualization of electrostatic complementarity (Biotin-Strepavidin) green = good complementarity and red = bad complementarity

180˚ flip

EC surface of Streptavidin EC surface of Biotin


Electrostatic complementarity scores

> Complementarity score (-1,1)

> Normalized surface integral of the complementarity score described previously (effectively the average value of

that score over the surface of the ligand)

> Used for visualization of complementarity on ligand or protein surface

> Includes some compensation for desolvation effects (capping of electrostatic potential values), and so may be

more robust when these are significant

> Complementarity r (-1,1)

> Pearson correlation coefficient of protein and ligand electrostatic potentials sampled on the surface vertices

> Can provide a better indication of ligand activity in some cases but is more susceptible to noise (more than rho)

> Complementarity rho (-1,1)

> Spearman rank correlation coefficient of protein and ligand electrostatic potentials sampled on the surface

vertices

> Can provide a better indication of ligand activity in some cases

> More robust against background electric fields (useful if the computed protein electric potential is being biased

by a large net charge on the protein)


> Application to several data sets

Case studies

Halogen Bonding XIAP DPP4 MCL1

0.08

0.13

0.18

0.23

0.28

0.33

0.38

1 10 100

Ele

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rity

IC50 (nM)

R² = 0.5402

R² = 0.5677

R² = 0.6913

0.15

0.2

0.25

0.3

0.35

0.4

0.45

1 100

Ele

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IC50 (µM)

R² = 0.3652

R² = 0.5685

R² = 0.4157

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

6 7 8 9

Ele

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pIC50

R² = 0.3215

R² = 0.5598

R² = 0.65830

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

-11 -9 -7 -5

Ele

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dG (kcal /mol)

SCI; Pearson; Rho


Halogen Bonding example

> Concept

Wilcken et al. JMC 2013

Driven by the σ-hole

(anisotropy of electron

density on halogen)

C6H5F C6H5Cl

C6H5Br C6H5I

Hal as Lewis acid

Electron donor

> Interesting case for electrostatic interactions

> Sigma hole increases from CI to I

> Halogen bond strength increases from CI to I



> MEK1

0.08

0.13

0.18

0.23

0.28

0.33

0.38

1 10 100

Com

ple

menta

rity

score

IC50 (nM)

Electrostatic complementarity

SCI

Pearson R

Spearman rho rank

Hardegger et al. ChemMedChem 2011

I Br

Cl F

SCI Pearson RSpearman

rho rank

3EQB F 0.207 0.084 0.31

3EQB Cl 26 0 0.219 0.098 0.325

3EQB Br 7.7 -3.8 0.241 0.117 0.348

3EQB I 2 -7.4 0.248 0.127 0.361

Electrostatic complementarityΔΔG

(kJ/mol)

IC50

(nM)Subst.Receptor


> p53-Y220C example


> Cathepsin-L example

0.25

0.3

0.35

0.4

0.45

0.5

3 30Com

ple

menta

rity

score

IC50 (nM)

Hardegger et al. ChemMedChem 2011

0.3

0.35

0.4

0.45

0.5

0.55

100 1000 10000

Com

ple

menta

rity

score

KD (µM)

Wilcken et al. JMC Perspective 2013


Halogen bioisosterism

> p53-Y220C: Ethynyl as halogen bioisostere

XED ESP surface

QM ESP surface

Similar electrostatic complementarity

I Ethynyl I Ethynyl

Wilcken et al. ACS Chem Biol 2015


PDB 5C7D

XIAP data set

> Inhibitors against IAP proteins (PPI target)

reported by Chessari et al. (JMC 2015)

> Electrostatic optimization of indoline scaffold

(QM ESP analysis and Cresset field points)

> Using 5C7D as receptor and modeling of ligands


XIAP case study

Ele

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IC50 (µM)

R² = 0.5402

0.15

0.17

0.19

0.21

0.23

0.25

0.27

0.29

1 10 100 1000

Complementarity SCI

R² = 0.5677

0.22

0.27

0.32

0.37

0.42

0.47

1 10 100 1000

ComplementarityPearson R

R² = 0.6913

0.2

0.25

0.3

0.35

0.4

0.45

1 10 100 1000

ComplementaritySpearman rho rank

> Bioactivity correlates with electrostatic complementarity

R IC50 (µM)Complementarity

SCI

Complementarity

Pearson R

Complementarity

Spearman rho

rank

CF3 + N (5C7D x-ray lig) 7.7 0.257 0.369 0.417

NH2 1000 0.178 0.253 0.24

CF3 5.9 0.269 0.392 0.376

Br 9.8 0.229 0.377 0.366

Cl 13 0.255 0.414 0.395

Me 46 0.204 0.332 0.331

F 51 0.248 0.392 0.394

OMe 160 0.176 0.318 0.319

H (5C7A lig) 500 0.207 0.343 0.333

iPr 59 0.217 0.33 0.297

SO2Me 4.1 0.217 0.36 0.424


XIAP case study

Changes in ring electrostatics

Contact to Lys297


R IC50 (µM)Complementarity

SCI

Complementarity

Pearson R

Complementarity

Spearman rho

rank

CF3 + N (5C7D x-ray lig) 7.7 0.257 0.369 0.417

NH2 1000 0.178 0.253 0.24

CF3 5.9 0.269 0.392 0.376

Br 9.8 0.229 0.377 0.366

Cl 13 0.255 0.414 0.395

Me 46 0.204 0.332 0.331

F 51 0.248 0.392 0.394

OMe 160 0.176 0.318 0.319

H (5C7A lig) 500 0.207 0.343 0.333

iPr 59 0.217 0.33 0.297

SO2Me 4.1 0.217 0.36 0.424


> Attractive (Cl or F) or

repulsive (NH2 or Me)

electrostatic interactions

with charged lysine 297

side chain

XIAP – electrostatic match with lysine side chain

NH2

F Me

Cl


> Repulsive electrostatics

between aromatic ring and

carbonyl oxygen of Gly 306

(and phenolic oxygen of Tyr

324 – not shown)

> Electron withdrawing groups

decrease electrostatic clash

XIAP – electrostatic clash with backbone carbonyl

NH2

OMe

CF3

F Me

Cl + N


DPP4 data set

Tyr547

‘Pendant’

> Data extracted from bindingDB(Kim et al. JMC 2008 and Kowalchick et al. Bioorg and Med Chem Letters 2007)

> Using 1X70 X-ray structure as receptor

> Subset of ligands that make contact to Tyr 547 and alignment using a modified reference ligand


DPP4 case study

compound ID pIC50 Complementarity

SCI

Complementarity

Pearson R

Complementarity

Spearman rho

rank

73 6.66 0.291 0.38 0.411

69 7.18 0.288 0.388 0.425

71 6.89 0.303 0.396 0.472

72 6.8 0.25 0.356 0.359

70 7.18 0.292 0.391 0.433

65 7.15 0.31 0.405 0.455

62 8.7 0.334 0.415 0.476

68 8.46 0.334 0.451 0.477

64 8.4 0.319 0.455 0.46

74 8.4 0.335 0.476 0.474

66 8.4 0.328 0.473 0.476

46 7.24 0.302 0.409 0.449

75 6.53 0.34 0.218 0.451

pIC50

Ele

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R² = 0.3652

0.24

0.26

0.28

0.3

0.32

0.34

0.36

6 7 8 9

Complementarity SCI

R² = 0.5685

0.2

0.3

0.4

0.5

0.6

6 7 8 9

ComplementarityPearson R

R² = 0.4157

0.34

0.36

0.38

0.4

0.42

0.44

0.46

0.48

0.5

6 7 8 9

Complementa…



DPP4 case study

CF3 group orientation

Electrostatics

toward Tyr547

compound ID pIC50 Complementarity

SCI

Complementarity

Pearson R

Complementarity

Spearman rho

rank

73 6.66 0.291 0.38 0.411

69 7.18 0.288 0.388 0.425

71 6.89 0.303 0.396 0.472

72 6.8 0.25 0.356 0.359

70 7.18 0.292 0.391 0.433

65 7.15 0.31 0.405 0.455

62 8.7 0.334 0.415 0.476

68 8.46 0.334 0.451 0.477

64 8.4 0.319 0.455 0.46

74 8.4 0.335 0.476 0.474

66 8.4 0.328 0.473 0.476

46 7.24 0.302 0.409 0.449

75 6.53 0.34 0.218 0.451



> Orientation of CF3 group

leads to attractive or

partially repulsive

electrostatic interactions in

proximity to Arg 358

DPP4 – orientation of trifluoromethyl group

Compound 74

pIC50 = 8.4

SCI = 0.343

Pearson = 0.479

Compound 46

pIC50 = 7.24

SCI = 0.302

Pearson = 0.409

Arg 358 Arg 358


> Repulsive electrostatics

between Methoxy group and

phenol ring of Tyr 547

> F substituent shows a

smaller electrostatic clash

than OMe group

DPP4 – electrostatic match with Tyrosine

Compound 72

pIC50 = 6.8

SCI = 0.25

Pearson = 0.356

Compound 70

pIC50 = 7.18

SCI = 0.292

Pearson = 0.391

Tyr 547 Tyr 547


C-H…π

interactions with

Tyr547

DPP4 – orientation of imidazole group

Alt. Cmpd. 62

SCI = 0.334

Rho = 0.476

Compound 62

SCI = 0.323

Rho = 0.456

> Reference alignment does

not provide information for

orientation of imidazole

> Orientation of imidazole

ring changes

complementarity score

> electrostatic

complementarity can help

to identify the most likely

rotamer / binding

conformation


MCL1 data set

> MCL1 data set part of Schrodinger FEP benchmark (Wang et al. JACS 2015)

> 6B4L X-ray structure as receptor and X-ray ligand used as reference for Forge™ alignment


MCL1 case study

R² = 0.1817

0.3

0.32

0.34

0.36

0.38

0.4

0.42

0.44

0.46

-10.5 -9.5 -8.5 -7.5 -6.5 -5.5

Ele

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dG (kcal /mol)

Complementarity SCI

R² = 0.162

0.1

0.15

0.2

0.25

0.3

0.35

-10.5 -9.5 -8.5 -7.5 -6.5 -5.5

Ele

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dG (kcal /mol)

Complementarity Pearson R

R² = 0.0856

0.09

0.14

0.19

0.24

0.29

0.34

0.39

-10.5 -9.5 -8.5 -7.5 -6.5 -5.5

Ele

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dG (kcal /mol)

Complementarity Spearman rho rank

> Bioactivity does not correlate well with electrostatic complementarity


MCL1 case study

Mainly hydrophobic space

exploration

Cl scan

Halogen Bond

with carbonyl

Make subset for Cl

scan on main

scaffold

> Hydrophobic space filling in subpocket not well recognized by method


MCL1 – Cl scan subset correlates with ΔG

> Cl scan subset

R² = 0.3215

0.25

0.3

0.35

0.4

0.45

-10.5 -9.5 -8.5 -7.5 -6.5

R² = 0.5598

0.05

0.15

0.25

0.35

-10.5 -9.5 -8.5 -7.5 -6.5

> 6-Cl substitution most

favourable (good

geometry for halogen

bond with Ala227 BB)

> 4 and 5-Cl substitution

do not significantly

change potency and

electrostatic

complementarity

Compound 68 Compound 54Compound 26

Compound 64 Compound 62

26

68

64 62

54

R² = 0.6583

0.09

0.19

0.29

0.39

-10.5 -9.5 -8.5 -7.5 -6.5


> Application to additional data sets

Case studies

TYK2 RPA70N PERK

SCI; Pearson; RhoΔG (kcal/mol) KD (µM) IC50 (nM)

R² = 0.7937

0.29

0.31

0.33

0.35

0.37

0.39

0.1 1 10 100

R² = 0.3588

0.29

0.34

0.39

0.44

0.49

0.54

0.1 1 10 100

R² = 0.1919

0.36

0.41

0.46

0.51

0.1 1 10 100

R² = 0.277

0.44

0.46

0.48

0.5

0.52

-12 -11 -10 -9

R² = 0.5933

0.52

0.57

0.62

0.67

-12 -11 -10 -9

R² = 0.277

0.62

0.67

0.72

0.77

-12 -11 -10 -9

R² = 0.2373

0.24

0.29

0.34

40 400

R² = 0.3301

0.2

0.25

0.3

0.35

40 400

R² = 0.0858

0.2

0.25

0.3

0.35

0.4

40 400


> Meaningful assessment of electrostatic complementarity at low computational costs (< 1 second per molecule on a desktop workstation)

> Possible to rank bioactivities of ligands (provided electrostatics play a main role in affinity changes)

> Caveats: does not calculate free energy of binding ΔG (desolvation, cavity term and space filling, entropic contributions, conformational effects missing); orthogonal multipolar interactions (fluorine bonding)

> Additional validation and future research: Improved handling of solvent exposed areas, rescoring of docking results, evaluation of and comparison to ab initio approaches

Conclusion and Outlook


cressetgroup

[email protected]

Thank you for your attention

Acknowledgements:

Mark Mackey

Paolo Tosco

Giovanna Tedesco

Tim Cheeseright

Andy Vinter

Predicting activity using the electrostatic ...

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