2014/10/20

in silico 創薬法の開発とその応用例

東海大学・糖鎖科学研究所
平山 令明

in silicoin vitroin vivo2

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desireddrug action

undesired
non-target moleculetarget molecule
biological systemdrug

a drug has multiple properties and actions

disease geneproteinactive sitetargetmoleculediseasedrug moleculetargetmolecule

cure the disease
control the functionof the target molecula22014/10/20

interaction
drugtarget molecule
simulation
docking

Strategies for in silico drug discovery

targetmolecule

targetmolecule?

?
?

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drug-likeness or what drugs look like ?

molecular weight250

frequency

200150
100
50
0
次の級

1000
9 50
9 00
8 50
8 00
7 50
7 00
6 50
6 00
5 50
5 00
4 50
4 00
3 50
3 00
2 50
2 00
1 50
1 00

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Molecular Descriptors
numerical values that characterize properties of molecule• physicochemical properties
e.g. hydrophobicity, molar refractivity, volume, surface• numerical values derived by applying algorithmic techniques tothe molecular structurese.g. topological indices,hydrophobicity : logP

CH3HN
O

NH-amide fragment-1.5102 aliphatic isolating carbons
0.390
CH3 6 aromatic isolating carbons
0.780
10 hydrogens on isolating carbons 2.270
1 chain bond
-0.120
1 benzyl bond
-0.150ortho substituent-0.760
total
0.900

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Distributions of some 2D descriptors
descriptor
weight
SlogP
SMR
TPSA
density
vdw_areavdw_vol
a_acc
a_don
a_hyd
KierA1
KierA2
KierA3
KierFlexrange
165.236-1.1843
4.336
12.96
0.726
164.684
180.587106
7.82267
3.125
1.478021.68402554.735.2955
14.4609165.150.992
497.032622.5587626
26.29311.80317.322728.81841

K.Horio,H.Muta,J.Goto, and N.Hirayama,“A Simple Method to Improve the Odds in Finding‘Lead-like’ Compounds from a Chemical Library,” Chem. Pharm.Bull.,55, 980(2007)

chemical space

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duglikeness?

Where do drugs bind ?

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Probing the protein
surface by α spheres
red: hydrophilic
white: hydrophobic

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Is there any specific amino acid composition at theligand binding site ?

amino acids observed atligand binding sites
residues whose non-H atom exists within4.5Å of non-H atoms of a ligand areconsidered

amino acids observed on thesurface of protein in general
residues whose non-H atom contacts witha probe sphere with a radius of 1.4Å areconsidered

Preference factors for the 20 standard amino acids

RA(x)preference factor

3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

P K Q A T G N S V R E I D L C Y M F H W
RA(x)x:amino acidRA(x) = CA(x) / SA(x)92014/10/20

Performance of PLB in predicting the binding sitesof drug-like molecules

true concavity(%)

top PLB

top two PLB’s

total

110(87%)120(95%)126(100%)“Use of Amino Acid Composition to Predict Ligand-Binding Sites”
S. Soga, H. Shirai, M. Kobori and N. Hirayama
J.Chem.Inf.Model., 47, 400-406 (2007)10

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？

？
？

？

？

？
？

What kind of molecules can bind to the supposedbinding site?
How can we select the relevant molecules from theimmense chemical space?
Quick search for a reasonable number of promising
molecules compatible with the binding site！

dimension, volume, surface,
lipophilicity, electronic character,…

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Making the most of alpha spheres
compoundlibrary

Alpha Site Filter“Alpha sphere filter method: Application of pseudomolecular descriptors in
virtual screening of 2D chemical structures”
H.Muta and N.Hirayama, J.Comput.Chem., Published Online Mar.25,2010

interaction
targetmolecule

simulation

drug

docking

ASEDock
J.Goto, R.Kataoka, H.Muta, and N.Hirayama, “ASEDock-Docking Based on
Alpha Spheres and Excluded Volumes,” J.Chem.Inf.Model., 48, 583(2008)12

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the shapes of ligand and thecorresponding concavity are highly
complementary to each other

Conformation SearchASE Model Generation

Rigid Body Alignment

Structure Optimization13

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R292K Mutant Influenza Virus Neuraminidase
in Complex with BCX-1812 :1L7H
Rfree = 0.177

DPI=0.171Å

OBCX-1812

OH
OON
H
NH
H2N
NH2

DPI=0.171Å
(rmsd)0.484Å
rmsd 0.881Å

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Strategies for in silico drug discovery

targetmolecule

targetmolecule?

?
?

Biological response (A) is determined by many
parameters(p1,…,pn)

A  f ( p1, p2 ,......pn )pn : geometric and chemical characteristics[ log(1/C) = k1logP + k2 + k3 ]If we know the function type and parameters, we can predict
the biological response of new compounds. The function isusually so complex, and it is almost impossible to count out
all the parameters. Only approximate function and a handful
parameters can be deduced
And yet, it is very(!) valuable to use such function in
drug discovery process, especially in optimization process.

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Experimentally measured biological activity

?
?

?

?

correlation?

Predicted biological activity by QSAR

a set of biologically
active compoundstraditional
QSAR(similar structures)

3D QSAR(different skeletons)

structure-activity
relationships

AutoGPAHow to find theactive conformations(structures)?

designing of better molecules
N.Asakawa,S.Kobayashi,J.Goto, and N.Hirayama,” AutoGPA : An Automated
3D-QSAR Method Based on Pharmacophore Alignment and Grid Potential
Analysis,” Int.J.Med.Chem.,2012,1(2012)doi:10.1155/2012/498931

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Step 1 : Alignment of conformers• Pharmacophore Elucidation implemented in MOE.
• Enumerate alignments by superposing the common
pharmacophoric features.

Step 2 :Running Grid Potential Analysis (GPA)
3.PLS(Partial Least-Squares) is used to derive 3D-QSAR models
based on grid potentials.
LPi

jactivity   Cij Gij
i : grid point
j: probe
cmpdactivity
10.95

2

1.2

3

2.4

40.63

G001 G001 G002 G002 G003 G003
stel
stel
stel

…

…

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AutoGPA modelGreen:
sterically favoredPharmacophore
Yellow:sterically disfavoredRed:
electronegativefavored

Blue: electropositive favored
Gray: the shape of the allowable space

Screening by AutoGPA modelBumpedconformers

Align input molecules based on
the pharmacophore model

Reject conformers which bumpagainst the receptor region
Predict activity by use of theAutoGPA model
98.5
87.5
76.5
65.5
55

5.5

66.5

7

7.5

8

8.5

918

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in silico drug discovery
unknown
target moleculetarget molecule
1D structure

3D structure
binding site

homology
modeling

unknownbinding sitea set of biologically
active molecules
druglikeness

PLB prediction ofindex
binding site

QSAR

selection of
ASF possible candidates

DRF

AutoGPA

compoundlibraryDRF druglikenesscandidates

virtuallibrary

ASEDock docking simulations

active (required) compounds

EXAMPLE 1(structure-based in silico drug discovery)
inhibitors ofplasminogen activator inhibitor 1(PAI-1)
Y.Izuhara et al., J.Cereb. Blood Flow Metab, 30, 904(2010)19

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EXAMPLE 2(ligand-based in silico drug discovery)anti-amyotrophic lateral sclerosis (ALS) drugs
K.Tanaka et al., PLOS ONE 9: 1-17 (2014)

Acknowledgements
Methodology Developments
Mr.Junichi Goto (Ryoka Systems Inc.)Dr.Ryoichi Kataoka(Ryoka Systems Inc.)
Mr.Naoyuki Asakawa (Ryoka Systems Inc.)
Mr.Seiichi Kobayashi (Ryoka Systems Inc.)
Mr.Kouhei Horio (Tokai University School of Medicine)Dr.Hajime Muta (Tokai University School of Medicine)
Drug Discovery Projects
Prof.Toshio Miyata (Tohoku University)
Prof.Joh-e Ikeda (Tokai University School of Medicine)20