Transcript Unbound Docking of Rigid Molecules

Computer Science meets Biology:
Guiding “in vitro” experiments
with “in silico” predictions.
Gidalevitz T, Biswas C, Ding H, Schneidman D,
Wolfson HJ, Stevens F, Radford S, Argon Y.
Agenda
Introduction to the docking
problem
The PatchDock algorithm
Biological problem
Real experimental results
What is Docking?
• Given two molecules find their correct
association:
T
=
+
Problem Importance
Computer aided drug design – a new drug
should fit the active site of a specific
receptor.
Understanding of biochemical pathways many reactions in the cell occur through
interactions between the molecules.
Despite the advances in the Structural
Genomics initiative, there are no efficient
techniques for crystallizing large complexes
and finding their structure.
Bound Docking
• In the bound docking we are given a complex of 2
molecules.
• After artificial separation the goal is to reconstruct the
native complex.
• No conformational changes are involved.
• Used as a first test of the validity of the algorithm.
Docking
Algorithm
Unbound Docking
• In the unbound docking we are given 2
molecules in their native conformation.
• The goal is to find the correct association.
• Problems: conformational changes (side-chain
and backbone movements), experimental errors
in the structures.
+
= ?
Docking Algorithms
Brute force enumeration of
the transformation space:
Local shape feature
matching:
• FFT – Katchalski-Katzir et al.
• Dock - Kuntz (1982)
• ‘knobs’ and ‘holes’ –
(1992) (Walls & Sternberg,
Vakser, Gabb et al., Camacho
et al., Chen & Weng)
• Soft Docking – Jiang & Kim
(1991), Palma et al.,
• Randomized algorithms: GA,
Monte-Carlo - Jones et al.,
Gardiner et al.
Connolly (1986)
• Geometric Hashing - Norel
et al., Fischer et al. (1994)
• Flexible docking - Sandak
et al.
• FlexX: hydrogen H-bonding
– Rarey et al.
PatchDock …
is an efficient method for unbound docking of rigid
molecules.
The molecular shape is used explicitly avoiding the
exhaustive search of the 6D transformation space.
The algorithm focuses on local surface patches
divided into three shape types: concave, convex and
flat.
The geometric surface complementarity scoring is
extremely fast and accurate. It employs advanced data
structures for molecular representation: Distance
Transform Grid and Multi-resolution Surface.
http://bioinfo3d.cs.tau.ac.il/PatchDock
Duhovny, D., Nussinov, N Wolfson, H.J. Lecture Notes in
Computer Science 2452, pp. 185-200, Springer Verlag, 2002
PatchDock Method
PDB
files
Surface Representation
Patch Detection
Matching Patches
Scoring & Filtering
Candidate
complexes
Surface Representation
• Dense MS surface
(Connolly)
• Sparse surface (Shuo
Lin et al.)
Patch Detection
1.
3.
Connolly
2. Sparse surface [2]:
surface
local minima and
representation maxima of Connolly
surface. The surface
topology graph is
obtained by connecting
neighboring points.
Shape representation
by patches. PatchDock
applies a segmentation
algorithm to divide the
surface into shapebased patches.
PatchDock focuses on sparse surface features, preserving the
quality of shape representation.
The sparse features reduce the complexity of the matching step.
Matching
Patches
Receptor patches
Ligand patches
Transformation
Matching 2 points
and their associated
normals is sufficient
to compute
transformation in 3D
space.
Base: 1 critical point with its normal from one patch and 1 critical
point with its normal from a neighbor patch.
Base signature: distances and angles.
dE, dG, α, β, ω
Match every base from the receptor patches against all the bases
from complementary ligand patches with similar signatures.
Geometric Hashing of base signatures is used to speed up the
search.
Penetrations Filtering
Distance Transform Grid stores the distances from the
surface of the molecule. The distance is negative inside
the molecule and positive outside.
Steric clashes are checked by accessing the receptor
grid with ligand surface points.
-1
0
+1
Scoring
The surface of the receptor is divided into five
shells according to the distance function: S1-S5
The number of ligand surface points in every
shell is counted.
The geometric score is a weighted sum of the
number of ligand surface points inside every shell.
Multi-resolution surface data structure was developed to speed
up this stage.
Dataset and Results
Protein-Protein cases from protein-protein docking benchmark [6]:
Enzyme-inhibitor – 22 cases
Antibody-antigen – 16 cases
Protein-DNA docking: 2 unbound-bound cases
Protein-drug docking: tens of bound cases (Estrogen receptor, HIV
protease, COX)
Performance: Several minutes for large protein molecules and seconds for
small drug molecules on standard PC computer.
Estrogen receptor
Estradiol
molecule from
complex
docking solution
DNA
endonuclease
docking solution
Endonuclease I-PpoI (1EVX) with
DNA (1A73). RMSD 0.87Å, rank 2
Estrogen receptor with estradiol (1A52).
RMSD 0.9Å, rank 1, running time: 11
seconds
Results Enzyme-Inhibitor docking
Complex
Description
PDB
receptor/ligand
1ACB
α-chymotrypsin/Eglin C
1AVW
Trypsin/Sotbean Trypsin inhibitor
pen.
res.1
geom score
time
with ACE score
rmsd
rank
min.
rmsd
rank
0,2
2.0
41
9:37
1.8
55
3,4
1.9
913
11:27
1.9
319
1BRC
Trypsin/APPI
0,2
5.0
528
5:20
5.6
66
1BRS
Barnase/Barstar
1,3
3.5
115
5:18
2.7
7
1CGI
α-chymotrypsinogen/trypsin inhibitor
4,2
2.4
114
6:26
3.0
10
1CHO
α-chymotrypsin/ovomucoid 3rd Domain
0,3
3.4
148
5:35
1.2
26
1CSE
Subtilisin Carlsberg/Eglin C
0,2
3.8
166
6:58
2.3
540
1DFJ
Ribonuclease inhibitor/Ribonuclease A
12,8
3.9
1446
11:58
11.9
612
1FSS
Acetylcholinesterase/Fasciculin II
8,3
2.5
296
11:42
2.3
46
1MAH
Mouse Acetylcholinesterase/inhibitor
2,5
2.5
436
14:39
2.3
57
1PPE*
Trypsin/CMT-1
0,0
2.0
1
2:34
2.0
1
1STF*
Papain/Stefin B
0,0
2.2
4
8:15
2.1
13
1TAB*
Trypsin/BBI
0,1
1.4
96
3:41
7.2*
104
1TGS
Trypsinogen/trypsin inhibitor
5,4
2.2
345
5:19
3.6
101
1UDI*
Virus Uracil-DNA glycosylase/inhibitor
4,2
2.6
3
7:40
2.4
1
1UGH
Human Uracil-DNA glycosylase/inhibitor
8,3
2.1
12
5:45
3.8
5
2KAI
Kallikrein A/Trypsin inhibitor
10,7
4.2
126
7:15
4.7
42
2PTC
β-trypsin/ Pancreatic trypsin inhibitor
2,4
4.4
66
5:13
3.4
12
2SIC
Subtilisin BPN/Subtilisin inhibitor
5,3
2.5
129
9:41
4.7
21
2SNI
Subtilisin Novo/Chymotrypsin inhibitor 2
6,7
8.3
1241
5:08
7.3
450
2TEC*
Thermitase/Eglin C
0,1
3.0
66
7:58
1.4
29
4HTC*
α-Thrombin/Hirudin
2,2
3.3
2
3:36
2.8
2
1 Number
of highly penetrating residues in unbound structures superimposed to complex
Results Antibody-Antigen
docking
Complex
Description
PDB
receptor/ligand
1AHW
Antibody Fab 5G9/Tissue factor
1BQL*
pen.
res. 1
geom score
time
ACE score
rmsd
rank
min.
rmsd
rank
3,3
2.5
29
10:12
2.5
10
Hyhel - 5 Fab/Lysozyme
0,0
2.5
13
6:21
1.4
7
1BVK
Antibody Hulys11 Fv/Lysozyme
0,0
3.8
1301
6:25
3.5
809
1DQJ
Hyhel - 63 Fab/Lysozyme
18,7
4.3
773
5:30
5.1
953
1EO8*
Bh151 Fab/Hemagglutinin
3,1
1.8
567
9:45
1.6
292
1FBI*
IgG1 Fab fragment/Lysozyme
2,5
5.0
536
10:13
5.0
2416
1IAI*
IgG1 Idiotypic Fab/Igg2A Anti-Idiotypic Fab
5,6
4.8
1302
9:13
3.4
1304
1JHL*
IgG1 Fv Fragment/Lysozyme
0,0
1.6
282
13:15
1.3
143
1MEL*
Vh Single-Domain Antibody/Lysozyme
0,1
1.8
3
2:40
2.0
2
1MLC
IgG1 D44.1 Fab fragment/Lysozyme
8,3
4.0
136
5:29
2.6
123
1NCA*
Fab NC41/Neuraminidase
0,0
2.6
114
17:50
2.8
66
1NMB*
Fab NC10/Neuraminidase
0,0
2.7
2593
28:10
2.4
1734
1QFU*
Igg1-k Fab/Hemagglutinin
0,0
2.7
44
5:42
2.7
23
1WEJ
IgG1 E8 Fab fragment/Cytochrome C
0,0
4.3
232
7:44
2.6
87
2JEL*
Jel42 Fab Fragment/A06 Phosphotransferase
0,2
4.7
114
5:02
4.7
50
2VIR*
Igg1-lamda Fab/Hemagglutinin
0,0
3.1
258
7:34
3.5
306
1 Number
of highly penetrating residues in unbound structures superimposed to complex
The Real Challenge:
Can we help biologists?
+
= ?
Identification of the N-terminal
peptide binding site of GRP94
GRP94 - Glucose
regulated protein 94
VSV8 peptide - derived from
vesicular stomatitis virus
Gidalevitz T, Biswas C, Ding H, Schneidman-Duhovny D, Wolfson HJ,
Stevens F, Radford S, Argon Y. J Biol Chem. 2004
Biological motivation
The complex between the two molecules highly
stimulates the response of the T-cells of the
immune system.
The grp94 protein alone does not have this
property. The activity that stimulates the immune
response is due to the ability of grp94 to bind
different peptides.
Characterization of peptide binding site is highly
important.
GRP94 molecule
There was no structure of grp94 protein. Homology
modeling was used to predict a structure using another
protein with 52% identity.
Recently the structure of grp94 was published. The RMSD
between the crystal structure and the model is 1.3A.
Docking
PatchDock was applied to dock the two molecules,
without any binding site constraints.
Docking results were clustered in the two cavities:
GRP94 molecule
There is a binding site for inhibitors between the helices.
There is another cavity produced by beta sheet on the
opposite side.
Experimental Verification
Goals:
Try to eliminate one of the cavities.
Find the positions of the amino acids which are
important for peptide binding.
Experimental Verification 1
Experimental data shows that inhibitor and peptide can
bind simultaneously.
Two residues in the inhibitor binding site were mutated.
The mutant did not bind inhibitor, however it could still
bind peptide.
The binding sites of the inhibitor and peptide are distinct.
The abolition of the inhibitor does not affect peptide binding.
Experimental Verification 2
The peptide binding was pH
sensitive. Therefore involvement
of His residue was suspected.
His125 was mutated to Asp and
Tyr. The first mutated protein did
not bind the peptide at all and
the second had only partial
activity.
Both mutants were soluble and
could bind the inhibitor.
Computational Verification 2
Conclusions
Computational prediction can help in
guiding “in vitro” experiments.
Further algorithmic improvements will
yield in more reliable predictions.