Conformational Ensembles Docking

1. BL5203: Molecular Recognition & Interaction Lecture 5: Drug Design Methods Ligand-Protein Docking (part II)Prof. Chen Yu ZongTel: 6874-6877Email: csccyz@nus.edu.sghttp://xin.cz3.nus.edu.sgRoom 07-24, level 7, SOC1, National University of Singapore

2. Conformational Ensembles Docking

3. Conformational Ensembles Docking Observations: • Generating an orientation of a ligand in a binding site may be separated from calculating a conformation of the ligand in that particular orientation. • Multiple conformations of a given ligand usually have some portion in common (internally rigid atoms such as ring systems), and therefore, contain redundancies.

4. Conformational Ensemble Docking

5. Conformational Ensemble Docking • Conformational ensembles are generated by overlaying all conformations of a given molecule onto its largest rigid fragment. • Only atoms within this largest rigid fragment are used during the distance matching step. The RT matrix is defined. • Each of the conformers is oriented into the site and scored. The score measures steric and electrostatic complementarity. • One matching steps - all the conformers are docked and scored in the selected orientation.

6. Overview of the Ligand Ensemble Method

7. Advantages of Conformational Ensemble Docking Speed increase due to: • One matching step for all of the conformers. • The largest rigid fragment usually has fewer atoms (less potential matches are examined).

8. Disadvantages of Conformational Ensemble Docking • Loss of information when the orientations are guided only by a subset of the atoms in molecule. Orientations may be missed because potential distance matches from non-rigid portions of the molecule are not considered. • The ensemble method will fail for ligands that lack internally rigid atoms. • The use of chemical matching and critical clusters is limited.

9. Pharmacophore-Based Docking

10. Pharmacophore-based Docking Basic idea: • Appropriate spatial disposition of a small number of functional groups in a molecule is sufficient for achieving a desired biological effect. • The ensemble formation will be guided by these functional groups.

11. 6.7 4.2-4.7 4.8 5.2 5.1-7.1 3-D Representation of a Protein Binding Site Distances between binding groups in Angstroms and the type of interaction is searchable

12. Pharmacophore Fingerprint • Pharmacophore fingerprint- a set of pharmacophore features and their relative position. • Typical pharmacophore features: • Hydrogen-bond donors and acceptors • Positive and negative ionizable atoms/groups • Hydrophobes and ring centroids • Implemented in DOCK 4.0.1 • Hydrogen-bond donors • Hydrogen-bond acceptors • Dual hydrogen-bond donor and acceptor • 5 or 6 membered ring centroids

13. Notes on Pharmacophore Fingerprint • Each conformer has pharmacophore fingerprint. • Different conformers of the same molecule can have identical pharmacophore fingerprints.

14. Pharmacophore DOCK

15. Advantages of Pharmacophore-based Docking • Rapid elimination of ligands containing functional groups which would interfere with binding. • Speed increase over docking of individual molecules. • More information pertaining to the entire molecule is retained (no rigid portions). • Chemical matching and critical clusters are encouraged.

16. Speed Comparison Between Ensemble and Pharmacophore-based Docking. Pharmacophore-based advantage: • Reduced number of ligand points considered during distance matching. Ensemble docking advantage: • The average number of conformers per molecule is higher than the average number of conformers per fingerprint. The one step matching speed reduction is slightly higher.

17. Speed Reduction Cont. • Ensemble docking:the average number of conformers per molecule is 297. • Pharmacophore-based:50-100 conformers per pharmacophore

18. Database Preparation • Generating molecular conformations • Systematic search method with SYBYL. • Overlaying molecular conformers onto pharmacophores • Extract 3D pharmacophore from the first molecule of a cluster • Use it to perform a rigid 3D UNITY search of the rest of that cluster to find matches • Save the pharmacophore querywith the associated molecules • Process until all molecules are associated with a pharmacophore

19. Site Points Generation • Chemically labeled site point are generated in an automated fashion using the script MCSS2SPTS . • The script runs a series of MCSS(MultipleCopy Simultaneous Searches) calculations. • MCSS – methodology for finding energetically favorable positions and orientations of small functional group in a binding site. • Uses CHARMM potential energy function to determine the preferred locations or potential energy minima simultaneously for thousands of copies of a given chemical group.

20. Limitations of Pharmacophore-based Searching • A limited subset of key interactions (typically 4-6) which must be extracted from the target site with dozens of potential interactions. • Complex queries are extremely slow. • The majority of the information contained in the target structure is not considered during the search. There is no scoring function beyond the binary (match/no match). Any steric or electronic constraints imposed by the target, but not defined by the target are ignored.

21. INVDOCK Strategy Science 1992;257: 1078 Proteins 1999; 36:1

22. Automated Protein Target Identification Software INVDOCK

23. INVDOCK Test on Drug Target PredictionAnticancer Drug Tamoxifen PDB Id Protein Experimental Findings 1a25 Protein Kinase C Secondary Target 1a52 Estrogen Receptor Drug Target 1bhs 17beta Hydroxysteroid dehydragenase Inhibitor 1bld Basic Fibroblast Growth Factor Inhibitor 1cpt Cytochrome P450-TERP Metabolism 1dmo Calmodulin Secondary Target Proteins. 1999; 36:1 Tamoxifen is a famous anticancer drug for treatment ofbreast cancer. It was approved by FDA in1998 as the 1st cancer preventivedrug. 30 million people are expected to use it.

24. PDB Putative Protein Target Experimental Finding Clinical Implication 1a52 Estrogen Receptor Drug target Confirmed Treatment of breast cancer 36 1akz Uracil-DNA Glycosylase 1ayk Collagenase Inhibited activity Confirmed Tumor cell invasion and cancer metastasis 38 1az1 Aldose Reductase 1bnt Carbonic Anhydrase 1boz Dihydrofolate Reductase Decreased level Implicated Combination therapy for cancer 43 1dht, 1fdt 17b -Hydroxysteroid Dehydrogenase Inhibitor Confirmed Implicated Promotion of tumor regression 39 1gsd, 3ljr Glutathione Transferase A1-1, Glutathione S-Transferase Suppressed enzyme and activity Genotoxicity and carcinogenicity 41 1mch Immunoglobulin l Light Chain Temerarily enhanced Ig level Implicated Modulation of immune response 44 1p1g Macrophage Migration Inhibitory factor 1ulb Purine Nucleoside Phosphorylase 1zqf DNA Polymerase b 2nll Retinoic Acid Receptor 1a25 Protein Kinase C Inhibition Confirmed Anticancer 37 1aa8 D-Amino Acid Oxidase Implicated 1afs 3a -Hydroxysteroid Dehydrogenase Effect on androgen induced activity Hepatic steroid metabolism 42 1pth Prostaglandin H2 Synthase-1 Direct inhibition Confirmed Prevention of vasoconstriction 40 1sep Sepiapterin Reductase 2toh Tyrosine 3-Monooxygenase INVDOCK Test on Drug Target PredictionTargets of 4H-tamoxifen(Proteins. 1999; 36:1)

25. Compound Number of experimentally confirmed or implicated toxicity targets Number of toxicity targets predicted by INVDOCK Number of toxicity targets missed by INVDOCK Number of toxicity targets without structure or involving covalent bond Number of INVDOCK predicted toxicity targets without experimental finding Aspirin 15 9 2 4 2 Gentamicin 17 5 2 10 2 Ibuprofen 5 3 0 2 2 Indinavir 6 4 0 2 2 Neomycin 14 7 1 6 6 Penicillin G 7 6 0 1 8 Tamoxifen 2 2 0 0 4 Vitamin C 2 2 0 0 3 Total 68 38 5 25 29 INVDOCK Test on Drug Target PredictionDrug Toxicity Targets(J. Mol. Graph. Mod. 2001, 20, 199)

26. Results of Docking Studies The docked (blue) and crystal (yellow) structure of ligands in some PDB ligand-protein complexes. The PDB Id of each structure is shown.

27. Dataset and Testing Results Protein-Proteincases from protein-protein docking benchmark [6]: Enzyme-inhibitor – 22 cases Antibody-antigen – 16 cases Protein-DNAdocking: 2 unbound-bound cases Protein-drugdocking: 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

28. Results Enzyme-Inhibitor docking 1 Number of highly penetrating residues in unbound structures superimposed to complex

29. Results Antibody-Antigen docking 1 Number of highly penetrating residues in unbound structures superimposed to complex

30. Description of Docking Quality Molecule Docked Protein PDB Id RMSD Energy (kcal/mol) Match Indinavir HIV-1 Protease 1hsg 1.38 -70.25 Match Xk263 Of Dupont Merck HIV-1 Protease 1hvr 2.05 -58.07 Match Vac HIV-1 Protease 4phv 0.80 -88.46 One end match, the other in different orientation Folate Dihydrofolate Reductase 1dhf 6.55 -46.02 Match 5-Deazafolate Dihydrofolate Reductase 2dhf 1.48 -65.49 Match Estrogen Estrogen Receptor 1a52 1.30 -45.86 Complete overlap, flipped along short axis 4-Hydroxytamoxifen Estrogen Receptor 3ert 5.45 -55.15 Match Guanosine-5'-[B,G-Methylene] Triphosphate H-Ras P21 121p 0.94 -80.20 Overlap, flipped along short axis Glycyl-*L-Tyrosine Carboxypeptidase A a 3cpa 3.56 -40.63 Quality of INVDOCK AlgorithmProteins. 1999; 36:1

31. 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

32. 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.

33. 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.

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

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