13th symposium on Discovery, Fusion, Creation of New Knowledge
by Multidisciplinary Computational Sciences
CCS International Symposium 2021
In silico Drug Discovery using Molecular
Modeling and Simulation
Takatsugu Hirokawa
Division of Biomedical Science
Faculty of Medicine
University of Tsukuba
2021/10/08 Takatsugu Hirokawa 1
2021/10/08 Takatsugu Hirokawa 2
Target
identification
Target
validation
Lead
discovery
Lead
optimization
Preclinical
tests
Clinical
trialsDisease-related
genomic
In silico (computer aided) drug discovery
• Bioinformatics
• Reverse docking
• Protein structure
prediction
• Target druggability
• Tool compound
design
• Library design
• Docking
• De novo design
• Pharmacophore
• Target flexibility
• QSAR
• 3D-QSAR
• Structure-based
optimization
• In silico ADMET
prediction
• Physicologically-based
pharmacokinetics
simulation
In silico DD to the various stages of drug development
N
N
NH
Don Acc
Aro2D fingerprint
Substructure search Volume/surface match 3D pharmacophore
docking
Compound library
2D 3D
COCCOc1cc2ncnc(Nc3cccc(c3)C#C)c2cc1OCCOC
2021/10/08 Takatsugu Hirokawa 3
Ligand-based drug discovery Structure-based drug discovery
In silico drug discovery approaches
2021/10/08 Takatsugu Hirokawa 4
Pros Cons Recent topics
Free active ligand information
Hight computational
cost for accurate
simulation
• Long Time
Simulation
• GPCR structures
• Cryo-EM technology
Structural novelty and chemical
diversity
Understanding the energy
contribution of ligand binding
Pros Cons Recent topics
Low computational cost Rely on known active
ligands, narrow
chemical diversity
• Open database and
big data analysis
• Molecular Matched
Pair
• Artificial Intelligence
Do not rely on protein structures
Reasonable Hit rate
Structure-Based Drug Design (SBDD)
Ligand-Based Drug Design (LBDD)
In silico drug discovery approaches
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Protein Structure
Data
Pharmacological &
Drug Discovery
Research
Structures with the target
compounds or proteins
In silico analysisProtein modeling, Protein-ligand docking
Protein-Protein docking, Molecular dynamics
Chemoinformatics
Apo form / Structural artifacts in
X-ray structures / Mutations
Homology Models
Protein-ligand models / Dynamics
/ Virtual screening
Role of in silico analysis for drug discovery
• Protein modeling
• Protein-Ligand docking
– Binding site prediction
– Induced Fit Docking
– Protein-ligand interaction fingerprint
– Virtual screening
• Protein-Protein docking
– PPI interface analysis for drug discovery
• Molecular dynamics simulation
– Conventional MD and biased MD
• Cheminformatics
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in silico strategiesEasy Hard
Lock & key model
Pre-existing equilibrium model
Induced-fitmodel
Cryptic-site binding model
Conventional Docking
Conventional MD &Docking
Conventional / Biased MD &
Advanced Docking
Biased MD &Advanced Docking
PPIInterface binding
PPI DockingConventional / Biased MD &
Advanced Docking and
Scoring
PathwayMetasiteBinding
Biased MD &Advanced Docking
&Chem-
informatics
In silico strategies for protein-ligand interactions
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Outline
Case studies:
1. MetaD simulations of ligand entry into EP4 receptor.
2. Analysis by MetaD simulation of binding pathway of influenza virus M2 channel
blockers.
3. Identification of the druggable Hidden Catalytic Cavity within the CDK9 Molecule
Upon Tat Binding (short topic)
Lock & key model
Pre-existing equilibrium model
Induced-fitmodel
Cryptic-site binding model
PPIInterface binding
PathwayMetasiteBinding
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Outline
Case studies:
1. MetaD simulations of ligand entry into EP4 receptor.
2. Analysis by MetaD simulation of binding pathway of influenza virus M2 channel
blockers.
3. Identification of the druggable Hidden Catalytic Cavity within the CDK9 Molecule
Upon Tat Binding (short topic)
Lock & key model
Pre-existing equilibrium model
Induced-fitmodel
Cryptic-site binding model
PPIInterface binding
PathwayMetasiteBinding
In silico analysis to post-structure determination for GPCRs
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Antibody binding contribution
on ligand binding
☟Antibody free simulation
Ligand-protein contacts
Contribution of key residues
for ligand recognition
☟In silico screening
Ligand design
Understanding of ligand
function
Ligand binding pathway
simulation
(cMD or Biased MD)
☟Allosteric or meta site
Mode of action
Diversity of structural
changes (Active/Inactive)
☟Expansion of structural
template in homology
modeling
Toyoda et al., Nat Chem Biol. 2019, 15, 18-26.
EP4 receptor
Antibody
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Classical MD
Metadynamics
A
B
A
B
A
B
DG
kT
Gaussian
potential
Metadynamics (MetaD) simulation of ligand entry
Toyoda et al., Nat Chem Biol. 2019, 15, 18-26.
ONO-AE3-208
interface of the lipid
bilayer, acting as a
‘plug’ to prevent the
entry of agonist
Biasing collective variables (CVs)
as the distance between the
center of mass of the ONO-AE3-
208 molecule and the ligand-
binding residues
CVs
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Pharmacophore features for
binding into membrane
Metadynamics (MetaD) simulation of ligand entry
Toyoda et al., Nat Chem Biol. 2019, 15, 18-26.
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me
mb
ran
eso
lve
nt
folded
extended
semi-folded
EP4 binding
Ligand conformational changes on ligand entry pathway from the membrane bilayer to the
EP4 binding pocket
Start
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Constraints for Glide dockingPositional hydrophobic constraint based
on Fluoronaphthalene functional group
of ligand
With constrained hydrogen bonding
between the compounds and Arg316
44 Known active ligands of
EP4 from ChEMBLGeneration of 2,200 decoys
against 44 actives using
DUD-E % Ranked libgray
% K
now
n ac
tives
random
Arg316
Arg316
Fluoronaphthalene
Evaluation for virtual screening test
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Outline
Case studies:
1. MetaD simulations of ligand entry into EP4 receptor.
2. Analysis by MetaD simulation of binding pathway of influenza virus M2 channel
blockers.
3. Identification of the druggable Hidden Catalytic Cavity within the CDK9 Molecule
Upon Tat Binding (short topic)
Lock & key model
Pre-existing equilibrium model
Induced-fitmodel
Cryptic-site binding model
PPIInterface binding
PathwayMetasiteBinding
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S31
amantadine
adamantyl
bromothiophene
Analysis by metadynamics simulation of binding pathway
of influenza virus M2 channel blockers.
• Proton channel spanning the viral envelope
• Amantadine inhibits M2 proton channel activity
by binding to the channel pore
• Most currently circulating influenza A viruses
are amantadine-resistant.
• The most prevalent resistant mutation is a
substitution from Ser to Asn at position 31 in
M2.
• Adamantyl bromothiophene (ABT) was reported
to be a dual inhibitor, targeting both S31 M2
and N31 M2.
• Solution NMR structures revealed that the
adamantane ring in ABT is oriented up toward
the N-terminus of S31 M2, but down toward the
C-terminus of N31 M2
S31 N31Free energy profiles of the binding kinetics
of M2 channel blockers by MetaD (100ns x
10 replica for each target)
×〇
〇〇
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MetaD simulations of amantadine binding in S31 M2.Distance from amantadine binding site of PDB structure
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MetaD simulations of amantadine binding in N31 M2.
amantadine mainly remains in the N31 M2
channel pore about 2A ̊ from the
amantadine-binding site of PDB structure
The amino group of amantadine was found to form direct hydrogen bonds with the side chains of
Asn31 during the whole simulation time and to be oriented toward the N-terminus of M2
N-ter
Distance from amantadine binding site of PDB structure
L2 state in S31 M2
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Binding kinetics of ABT, which is an amantadine derivative
that inhibits both S31 M2 and N31 M2 proteins.
adamantyl bromothiophene (ABT)
N-ter
C-ter
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MetaD simulations of ABT binding in S31 M2.
Top
Vie
w
ABT binds to the entrance of the channel
pore of S31 M2 through interaction of the
amino group with one of the four Asp24
residues by a salt bridge.
Enters the channel pore through
hydrophobic interactions between the
adamantane ring and Val27.
After ABT enters the channel pore, the
bromothiophene group oriented toward the N-
terminus of S31 M2 and ABT remains stable in the
channel pore by means of water-bridged hydrogen
bonds with the carbonyl oxygen atoms of Ala30.
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MetaD simulations of ABT binding in N31 M2.
ABT was found to enter the channel pore
through a halogen bond with Asn31 of
N31 M2
The amino group of ABT then interacts with the side chain of Asn31 through a transient hydrogen bond for 30–40 ns The hydrogen
bond between the amino group and Asn31 is relatively unstable because of the lack of a high free energy barrier, there is repeated
binding and unbinding of ABT with Asn31. In addition, the bromothiophene group is oriented toward the C-terminus of N31 M2
ABT forms stable bonds in the channel
pore through hydrophobic interactions
between the bromothiophene group and
Ala30 and the hydrogen bond between
the amino group and Asn31
Top
Vie
w
2021/10/08 Takatsugu Hirokawa 23
Outline
Case studies:
1. MetaD simulations of ligand entry into EP4 receptor.
2. Analysis by MetaD simulation of binding pathway of influenza virus M2 channel
blockers.
3. Identification of the druggable Hidden Catalytic Cavity within the CDK9 Molecule
Upon Tat Binding (short topic)
Lock & key model
Pre-existing equilibrium model
Induced-fitmodel
Cryptic-site binding model
PPIInterface binding
PathwayMetasiteBinding
Identification of the druggable Hidden Catalytic
Cavity within the CDK9 Molecule Upon Tat Binding
2021/10/08 Takatsugu Hirokawa 24
Transcription from the integrated proviral DNA of
human immune-deficiency virus type 1 (HIV-1) is
tightly regulated by a virus-encoded transcription
factor Tat.
Asamitsu K, Hirokawa T, Okamoto T. PLoS One. 2017 Feb 8;12(2):e0171727.
CycT1/CDK9
CycT1/CDK9/Tat
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ATP
#127
PITALRE
P60
I61
T62
A63 L64
R65
E66
T186
R172
Y185
Cyclin T1
R148
V189
L170
K48
D167
T29
D149
K151
N154
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Summary
▪ Molecular simulation tell us
– Conformational payment or behavior to the environment in ligand
pathway
– Conformational changes of ligand in binding pathway simulation
– Providing new approaches to designing further ligands for resistant
mutations
– Finding the novel hot spot for ligand binding
– and more ..
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high accurate ligand design and virtual screening
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Collaborators and Publications
▪ Toyoda Y, Morimoto K, Suno R, Horita S, Yamashita K, Hirata K, Sekiguchi Y, Yasuda S, Shiroishi M,
Shimizu T, Urushibata Y, Kajiwara Y, Inazumi T, Hotta Y, Asada H, Nakane T, Shiimura Y, Nakagita T,
Tsuge K, Yoshida S, Kuribara T, Hosoya T, Sugimoto Y, Nomura N, Sato M, Hirokawa T, Kinoshita M,
Murata T, Takayama K, Yamamoto M, Narumiya S, Iwata S, Kobayashi T. “Ligand binding to human
prostaglandin E receptor EP4 at the lipid-bilayer interface” Nat Chem Biol. 2019 Jan;15(1):18-26.
▪ Sakai Y, Kawaguchi A, Nagata K, Hirokawa T. “Analysis by metadynamics simulation of binding
pathway of influenza virus M2 channel blockers” Microbiol Immunol. 2018 Jan;62(1):34-43.
▪ Asamitsu K, Hirokawa T, Okamoto T. “MD simulation of the Tat/Cyclin T1/CDK9 complex revealing
the hidden catalytic cavity within the CDK9 molecule upon Tat binding” PLoS One. 2017 Feb
8;12(2):e0171727.
Acknowledgment▪ Platform for Drug Discovery, Informatics, and Structural Life Science from the Ministry of Education,
Culture, Sports, Science and Technology, Japan.
▪ This research was supported by MEXT as “Priority Issue on Post-K computer” (Building Innovative Drug Discovery Infrastructure Through Functional Control of Biomolecular Systems).(HP160213)
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