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: 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. +

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.

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

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. + = ?

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.

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. 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

Surface Representation 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.) 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.

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.

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

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

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 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

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

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

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.