Transcript Slide 1

Ligand Binding Site Prediction for HIV-1 Protease
using Shape Comparison Techniques
Manasi
1
Jahagirdar ,
Vivek K
2
Jalahalli ,
Sunil
1
Kumar ,
A. Srinivas
3
Reddy ,
Xiaoyu
4
Zhang
and Rajni
5
Garg
1Dept.
of Electrical And Computer Engineering, San Diego State University, CA, 2Dept. of Mathematics and Statistics, San Diego State University, CA
3Molecular Modeling Group, Indian Institute of Chemical Technology, Hyderabad, India
4Chemistry and Biochemistry Dept., California State University, San Marcos, CA, 5Computer Science Dept., California State University, San Marcos, CA
Introduction
3D visualisation of protein, pocket and
ligand and descriptor information
Protein
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MUT-Details
Q7K, L33I, C67ABA, C95ABA, Q107K, L133I, C167ABA, C195ABA
Q7K, L33I, C67ABA, C95ABA
A71V, V82T, I84V
Q7K, D25N, L63P, V82A
-
PDB : 1B6J
Mutation : C67ABA, C95ABA, C167ABA,
C195ABA
1B6J is a HIV protease complexed with
macrocyclic peptidomimetic inhibitor
Pocket
PDB
Ligand
Protein
1B6J
Largest
Pocket
Vol/
Ligand Vol
Dipole
Moment
Len
Inertia
862.77362
11.0749
151.16163
141.32252
27.399763
Quarduple
moment
p_Integral
Residue Interface Propensity Values
Amino Acid
ALA
ARG
ASN
ASP
CYS
GLN
GLU
GLY
HIS
ILE
LEU
LYS
MET
PHE
PRO
SER
THR
TRP
TYR
VAL
PDB
Ligands
1B6P
PI7
1D4K
PI8
1LZQ BOC- POO-pep(FEF)- NH2
1MT8
pep(ARVLAEAM)
1AAQ
PSI
1C70
L75
1DMP
450
1EC2
BEJ
Residue Interface Propensity - 1D4K
123.522423
-22.102247
-101.420181
-2910.009
0.00000
33.238232
30.054001
6.967624
49.356991
-19.902149
-29.454842
354.51690
1D4K
4.18
2.09
0
3.14
0
0
0
1.09
0
1.47
2.09
0
0
0
1.39
0
0.69
0
0
4.18
1LZQ
5.08
2.54
0
2.91
0
0
0
1.27
0
0.78
1.27
0
0
0
2.03
0
1.34
0
0
2.5
1MT8
4.48
1.24
0
0.93
0
0
0.56
1.12
0
1.53
2.4
0
0
1.4
0.93
0
0.93
0
0
5.6
1AAQ
2.13
2.13
0
3.19
0
0
0
1.16
0
1.42
1.42
0
0
0
0.85
0
0.71
0
0
4.26
1C70
4.08
1.02
0
3.06
0
0
0
5.75
0
1.78
1.02
0
0
2.04
1.22
0
0.34
0
0
2.72
1DMP
3.04
1.52
0
3.42
0
0
0
1.24
0
1.71
1.14
0
0
0
0.83
0
0.83
0
0
3.04
1EC2
4.05
2.02
0
3.04
0
0
0
1.05
0
1.52
1.62
0
0
0
1.22
0
0.67
0
0
3.24
141
000
060
11011
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4.5
4
4
3.5
Future Work
3
3
2.5
2
2.5
2
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1.5
1.5
1
1
0.5
0.5
LE
U
LY
S
M
E
T
PH
E
PR
O
SE
R
TH
R
TR
P
TY
R
VA
L
Am ino Acid
Am ino Acid
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Hydrophobic and hydrophilic amino acid distribution at interface for 8
proteins in the dataset
Comparison of residue interface propensity values
7
40
6
35
5
Total Amino acid count
Computational Approach:
Algorithm for extracting and
 Extract binding pockets present in mutated HIV
comparing binding sites:
protease proteins
 Compute
a volumetric pocket
 Assign various descriptors such as area, volume,
function to represent the 3D
inertia, electrostatic potential, Betti numbers,
shapes of protein pockets
residue interface propensity and hydrophobicity  Compute an affine-invariant data
to nodes in the pockets for ‘matching score
structure called Multi-resolution
calculation’ and hence binding site prediction
contour tree (MACT) as a
Residue Interface Propensity and Hydrophobicity:
signature of the pocket function
 Propensity for each amino acid is calculated as a
 Compute and assign geometrical,
fraction of the frequency that the amino acid
topological
and
functional
contributes to the protein-ligand interface
attributes to the MACT and check
compared to the frequency that it contributes to
for compatibility of proteins and
the protein surface
ligands by comparing their
 As per the scale we use, hydrophobic residues
MACTs
are: Ala, Val, Leu, Ile, Pro, Met, Phe, Trp and Gly
and the rest as hydrophilic
IL
E
HI
S
P
CY
S
G
LN
G
LU
G
LY
AS
AL
A
AR
G
R
TR
P
TY
R
VA
L
R
TH
E
T
O
SE
PR
PH
M
E
LE
U
LY
S
IL
E
IS
H
G
LY
LU
G
LN
G
YS
P
C
AS
N
AS
N
0
0
AS
Method
4
3
2
1
0
ALA ARG ASN ASP CYS GLN GLU GLY HIS ILE LEU LYS MET PHE PRO SER THR TRP TYR VAL
30
25
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20
15
10
Ala
Amino Acid
Val Leu
Ile
Pro Met Phe Trp Gly
Ser
Thr Cys Asn Gln Tyr Asp Glu
Lys Arg
Amino acid
1B6P
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1D4K
1LZQ
1MT8
1AAQ
1C70
1DMP
Research and statistical results has proved the importance of utilizing a combination of
descriptors in predicting binding sites of proteins. In the future, we plan to extend the
algorithm to include more shape descriptors like tightness of fit, curvature in fine tuning the
binding site prediction
We plan to study the alternative sites for binding and the role of the attributes like volume,
dipole moment, moment of inertia, quadruple moment, hydrophobicity, residue interface
propensity, integral of properties, and, Betti numbers in the alternate binding site prediction
This study can be extended for other HIV targets namely reverse transcriptase, integrase,
gp41 and their inhibitors
5
0
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The dataset for the algorithm for binding site prediction and extraction : 90 HIV protease
protein (21 wild type, and 69 mutated) PDBs
The descriptors such as volume, dipole moment, moment of inertia, quadruple moment,
hydrophobicity, residue interface propensity, integral of properties, and, Betti numbers are
used for predicting the binding site
The largest pocket of the protein is invariably the binding site for the ligand and hence
residue interface propensity and hydrophobicity values are calculated for this pocket
Predicted interface residues are residues with propensity >= 1.5. A propensity of 0 indicates
that the amino acid has the same frequency in the interface and surface area
For this dataset, ALA, ASP, ARG and VAL have high preference in the interface
Predicted interface residues are distinctly hydrophobic.
Residue Interface Propensity - 1DMP
Propensity
Binding Site
354.51686
1B6P
3
1.63
0
3.37
0
0
0
1.17
0
1.23
1.28
0
0
1.125
1.125
0
0.75
0
0
4.5
Betti Numbers
3.5
Ligand
PI1
Discussion
Residue propensity and Hydrophobicity results for protein
pocket
AL
A
AR
G
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Dataset: Mutated and Wild Proteins
Propensity
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Effective binding site prediction is a primary step in the molecular recognition mechanism
and function of a protein with an application in discovery of new HIV protease inhibitors
that are active against mutant viruses
Accuracy of binding-site prediction can be improved using a combination of shape
descriptors for the interfaces
We use geometrical, topological and functional descriptors in combination for ligand
binding site prediction of HIV-1 protease
Propensity
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Description
His
References
1EC2
Ligands are commonly found to bind with one of the strongest hydrophobic
clusters on the surface of the target protein molecule
If the distribution of residues occurring in the interface is compared with the
distribution of residues occurring on the protein surface as a whole (residue
interface propensity), a general indication of the hydrophobicity is obtained
Combination of these two features appears to be a powerful tool for fine
tuning the binding pocket surface area to be considered for binding site
prediction of proteins
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