1. 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.

2. Agenda Introduction to the docking problem The PatchDock algorithm
Biological problem
Real experimental results

3. What is Docking? Given two molecules find their correct association:
Complex T
Receptor
Ligand =
We can describe the problem as assembling of a jigsaw puzzle. Given two molecules, referred to as the receptor and the ligand, we attempt to predict the shape of their complex as it appears in nature.
From the mathematical view point we are looking for a transformation (rotation and translation) in 3D space that aligns best the surfaces of the molecules without causing collisions. +

4. 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.
Docking is widely used in drug design.
It is also important in the understanding of biochemical pathways within the cell.
With the growing number of single structures due to the SG initiative, the docking task becomes of significant
practical value. Yet, the cost of determining the structures of protein complexes is still huge.

5. 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.
There are two main classes of docking problem: test docking, called bound and real docking, called unbound. …
Docking Algorithm

6. 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.
Unbound docking is a “real life” problem. We are given two molecules that were separately crystallized.
We look for their conformation as it appears in the natural complex. This is especially difficult due to
many conformational changes such as side-chain and backbone flexibility. +
= ?

7. Docking Algorithms
Brute force enumeration of the transformation space:
FFT – Katchalski-Katzir et al. (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.
Local shape feature matching:
Dock - Kuntz (1982)
‘knobs’ and ‘holes’ – Connolly (1986)
Geometric Hashing - Norel et al., Fischer et al. (1994)
Flexible docking - Sandak et al.
FlexX: hydrogen H-bonding – Rarey et al.
Docking algorithms can be classified into two broad categories by the way they explore the transformation space. Brute force algorithms perform an entire 6-dimensional search of all possible rotations and translations.
While local shape feature matching algorithms align local shape features, reducing the complexity of the conformational search.

8. PatchDock … http://bioinfo3d.cs.tau.ac.il/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.
Duhovny, D., Nussinov, N Wolfson, H.J. Lecture Notes in Computer Science 2452, pp , Springer Verlag, 2002

9. Surface Representation
PatchDock Method
PDB
files
Surface Representation
Patch Detection
Matching Patches
Scoring & Filtering
Candidate
complexes

10. Surface Representation
Dense MS surface (Connolly)
Sparse surface (Shuo Lin et al.)
The most common surface representation is the Connolly surface. The probe ball is rolled over the molecule,
creating 3 types of points: caps (yellow points – belong to one atom), belts (red points – lie between two atoms),
pits (green points – belong to the patches where the probe touches the 3 atoms).
This representation is very dense and can be reduced to local minima or maxima of the point patches.
This is sparse surface representation by Lin.

11. Patch Detection Connolly surface representation
Sparse surface [2]: local minima and 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 shape- based patches.
PatchDock focuses on sparse surface features, preserving the quality of shape representation.
The sparse features reduce the complexity of the matching step.

12. Matching Patches
Receptor patches
Ligand patches
Matching 2 points and their associated normals is sufficient to compute transformation in 3D space.
Transformation
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.
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.
dE, dG, α, β, ω

13. 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 -1

14. 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.

15. 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
Estrogen receptor with estradiol (1A52). RMSD 0.9Å, rank 1, running time: 11 seconds
docking solution
Endonuclease I-PpoI (1EVX) with DNA (1A73). RMSD 0.87Å, rank 2

16. Results Enzyme-Inhibitor docking
Complex
Description
pen. res.1
geom score
time
with ACE score
PDB
receptor/ligand
rmsd
rank
min.
1ACB
α-chymotrypsin/Eglin C
0,2
2.0 41
9:37
1.8 55
1AVW
Trypsin/Sotbean Trypsin inhibitor
3,4
1.9
913
11:27
319
1BRC
Trypsin/APPI
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
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 46
1MAH
Mouse Acetylcholinesterase/inhibitor
2,5
436
14:39 57
1PPE*
Trypsin/CMT-1
0,0 1
2:34
1STF*
Papain/Stefin B
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
345
5:19
3.6
101
1UDI*
Virus Uracil-DNA glycosylase/inhibitor
2.6 3
7:40
1UGH
Human Uracil-DNA glycosylase/inhibitor 12
5:45 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
5:13
2SIC
Subtilisin BPN/Subtilisin inhibitor
5,3
129
9:41 21
2SNI
Subtilisin Novo/Chymotrypsin inhibitor 2
6,7
8.3
1241
5:08
7.3
450
2TEC*
Thermitase/Eglin C
7:58 29
4HTC*
α-Thrombin/Hirudin
2,2
3.3 2
3:36
2.8
1 Number of highly penetrating residues in unbound structures superimposed to complex

17. Results Antibody-Antigen docking
Complex
Description
pen. res. 1
geom score
time
ACE score
PDB
receptor/ligand
rmsd
rank
min.
1AHW
Antibody Fab 5G9/Tissue factor
3,3
2.5 29
10:12 10
1BQL*
Hyhel - 5 Fab/Lysozyme
0,0 13
6:21
1.4 7
1BVK
Antibody Hulys11 Fv/Lysozyme
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
2416
1IAI*
IgG1 Idiotypic Fab/Igg2A Anti-Idiotypic Fab
5,6
4.8
1302
9:13
3.4
1304
1JHL*
IgG1 Fv Fragment/Lysozyme
282
13:15
1.3
143
1MEL*
Vh Single-Domain Antibody/Lysozyme
0,1 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
114
17:50
2.8 66
1NMB*
Fab NC10/Neuraminidase
2.7
2593
28:10
2.4
1734
1QFU*
Igg1-k Fab/Hemagglutinin 44
5:42 23
1WEJ
IgG1 E8 Fab fragment/Cytochrome C
232
7:44 87
2JEL*
Jel42 Fab Fragment/A06 Phosphotransferase
0,2
4.7
5:02 50
2VIR*
Igg1-lamda Fab/Hemagglutinin
3.1
258
7:34
306
1 Number of highly penetrating residues in unbound structures superimposed to complex

18. The Real Challenge: Can we help biologists?+
= ?

19. 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

20. 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.

21. 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.

22. Docking
PatchDock was applied to dock the two molecules, without any binding site constraints.
Docking results were clustered in the two cavities:

23. GRP94 molecule
There is a binding site for inhibitors between the helices.
There is another cavity produced by beta sheet on the opposite side.

24. Experimental Verification
Goals:
Try to eliminate one of the cavities.
Find the positions of the amino acids which are important for peptide binding.

25. 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.

26. 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.

27. Computational Verification 2

28. Conclusions
Computational prediction can help in guiding “in vitro” experiments.
Further algorithmic improvements will yield in more reliable predictions.