1. CZ3253: Computer Aided Drug design Lecture 9: Drug Design Methods III Ligand-Protein Docking (part II) Prof. Chen Yu Zong Tel: Room 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
A: The largest group of internally rigid atoms is fixed in position and the conformational space of the rest of the molecule is systematically sampled at 60° or 120° increments. B: The rigid fragment common to all conformations of the molecule is oriented in the binding site. C: All flexible fragments of the molecule are scored in the orientation of the rigid fragment.

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.
For chemical matching the anchor must be selected manually.

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. 3-D Representation of a Protein Binding Site
5.2
6.7
4.8
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
The number of features was 4 to allow substantial numbers of molecules to be represented by each pharmacophore fingerprint.

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: 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 query with the associated molecules
Process until all molecules are
associated with a pharmacophore
Expantion:
bond rotation increment - 120, 180 for bonds like amide bonds
Degenerate conformations due to symmetry are not allowed
No energy minimization, but the maximum of 130kcal/mol is imposed

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 (Multiple Copy 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.
The target flexibility must be considered.
Active site must be known

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

22. Automated Protein Target Identification Software INVDOCK

23. INVDOCK Test on Drug Target Prediction Anticancer Drug Tamoxifen
PDB Id Protein Experimental Findings
1a Protein Kinase C Secondary Target
1a Estrogen Receptor Drug Target
1bhs beta 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 of breast cancer.
It was approved by FDA in 1998 as
the 1st cancer preventive drug.
30 million people are expected to
use it.

24. Putative Protein Target
INVDOCK Test on Drug Target Prediction Targets of 4H-tamoxifen (Proteins. 1999; 36:1)
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
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
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
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
Anticancer 37
1aa8
D-Amino Acid Oxidase
1afs
3a -Hydroxysteroid Dehydrogenase
Effect on androgen induced activity
Hepatic steroid metabolism 42
1pth
Prostaglandin H2 Synthase-1
Direct inhibition
Prevention of vasoconstriction 40
1sep
Sepiapterin Reductase
2toh
Tyrosine 3-Monooxygenase

25. INVDOCK Test on Drug Target Prediction Drug Toxicity Targets (J. Mol
INVDOCK Test on Drug Target Prediction Drug Toxicity Targets (J. Mol. Graph. Mod. 2001, 20, 199)
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
Gentamicin 17 5 10
Ibuprofen 3
Indinavir 6
Neomycin 14 7 1
Penicillin G 8
Tamoxifen
Vitamin C
Total 68 38 25 29

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-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

28. 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

29. 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

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

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.