BL5203: Molecular Recognition & Interaction BL5203: Molecular Recognition & Interaction

Lecture 5: Drug Design Methods Lecture 5: Drug Design Methods Ligand-Protein Docking (part II)Ligand-Protein Docking (part II)

Prof. Chen Yu ZongProf. Chen Yu Zong

Tel: 6874-6877Tel: 6874-6877Email: Email: csccyz@nus.edu.sghttp://xin.cz3.nus.edu.sg

Room 07-24, level 7, SOC1, Room 07-24, level 7, SOC1, National University of SingaporeNational University of Singapore

Conformational Ensembles Conformational Ensembles DockingDocking

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Conformational Ensembles DockingConformational Ensembles Docking

Observations:

1. Generating an orientation of a ligand in a binding site may be separated from calculating a conformation of the ligand in that particular orientation.

2. Multiple conformations of a given ligand usually have some portion in common (internally rigid atoms such as ring systems), and therefore, contain redundancies.

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Conformational Ensemble DockingConformational Ensemble Docking

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Conformational Ensemble DockingConformational 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.

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Overview of the Ligand Ensemble MethodOverview of the Ligand Ensemble Method

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Advantages of Conformational Ensemble Advantages of Conformational Ensemble DockingDocking

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).

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Disadvantages of Conformational Disadvantages of Conformational Ensemble DockingEnsemble 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.

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Pharmacophore-Based Docking

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Pharmacophore-based DockingPharmacophore-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.

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3-D Representation of a Protein Binding Site3-D Representation of a Protein Binding Site

5.2

4.2-4.7

6.7

4.8

5.1-7.1 Distances betweenbinding groupsin Angstroms and the type of interactionis searchable

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Pharmacophore FingerprintPharmacophore 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

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Notes on Pharmacophore FingerprintNotes on Pharmacophore Fingerprint

• Each conformer has pharmacophore fingerprint.

• Different conformers of the same molecule can have identical pharmacophore fingerprints.

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Pharmacophore DOCKPharmacophore DOCK

Prepare target structure

Generate a set ofchemically labeled site

points

Read a 3D pharmacophorefrom the database

Compare distances betweenpharmacophore points andsite points to determine an

orientation matrix

Match?

NoYes

Orientationstries >MAX

Orientationstries >MAX

No No

Yes Yes

Use the transformation matrix todock all conformers associated with

the pharmacophore

Score allconformers

Save the best scoringconformer for each molecule

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Advantages of Pharmacophore-based DockingAdvantages 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.

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Speed Comparison Between Ensemble Speed Comparison Between Ensemble and Pharmacophore-based Docking.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.

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Speed Reduction Cont.Speed Reduction Cont.

• Ensemble docking:the average number of conformers per molecule is 297.

• Pharmacophore-based:50-100 conformers per pharmacophore

Rigid Fragment Conformer 1 Conformer 2 Confomer 297

PharmacophoreFingerprint

Conformer 1 Conformer 2 Conformer 100

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Database PreparationDatabase Preparation• Generating molecular conformations

– Systematic search method with SYBYL.

• Overlaying molecular conformers onto pharmacophores

1. Extract 3D pharmacophore from the first molecule of a cluster

2. Use it to perform a rigid 3D UNITY search of the rest of that cluster to find matches

3. Save the pharmacophore querywith the associated molecules

4. Process until all molecules are

associated with a pharmacophore

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Site Points GenerationSite 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.

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Limitations of Pharmacophore-based Limitations of Pharmacophore-based SearchingSearching

• 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.

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INVDOCK StrategyINVDOCK StrategyExisting M ethods:

G iven a Protein,F ind Puta tive B inding L iga nds

From a C hem ica l D a ta ba se

S uccess fu l ly D ocked C om poundsas Puta tive L igands

Protein

C om pound D a ta ba seC om pound 1

...C om pound n

N ew M ethod:G iven a L iga nd,

F ind Puta tive Protein T a rgetsFrom a Protein D a ta ba se

S uccess ful ly D ocked Prote insas Puta tive T arge ts

Liga nd

Protein D a ta ba seProte in 1

...Prote in n

Science 1992;257: 1078 Proteins 1999; 36:1

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Automated Automated ProteinProtein Target Identification Target Identification Software INVDOCKSoftware INVDOCK

S tep 1 : V ec tor-based dock ing of a l igand to a cavityS tep 2 : L im ited conform ation optim iza tion on the l igand and s ide cha in of b iom oleculeS tep 3 : E nergy m inim iza tion for a l l a tom in the b inding s i teS tep 4 : D ock ing eva lua tion by m olecula r m echanics energy func tions and com parison w ith other l igands

P ro te in fun c tio n , P ro teo m ics , L ig an d tran sp o rt, M e tab o lism

Th erap eutic Ta rg e ts , S id e -E ffec ts , M e tab o lism , To x ic ityFunction in Pathw ays

P o ten tia l Ap p lica tio n s :\|/

S uccess fu l ly D ocked Prote ins and Nuc le ic A c idsas Puta tive T arge ts of a L igand

|\|/

A utom ated Process to inverse ly dock the L ignad to each entry ina Buil t-In B iom olecula r C avity D a tabase (10 ,000 Prote in and Nuc le ic A c id E ntr ies )

\|/

L igand\|/

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INVDOCK Test on Drug Target PredictionINVDOCK Test on Drug Target Prediction Anticancer Drug Tamoxifen

PDB Id Protein Experimental Findings 1a25 Protein Kinase C Secondary Target1a52 Estrogen Receptor Drug Target1bhs 17beta Hydroxysteroid dehydragenase Inhibitor1bld Basic Fibroblast Growth Factor Inhibitor1cpt Cytochrome P450-TERP Metabolism1dmo 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.

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INVDOCK Test on Drug Target PredictionINVDOCK 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 ConfirmedTumor cell invasion and cancer metastasis

38 

1az1 Aldose Reductase        

1bnt Carbonic Anhydrase        

1boz Dihydrofolate ReductaseDecreased level  Implicated

Combination therapy for cancer 43

1dht,1fdt 17 -Hydroxysteroid Dehydrogenase

Inhibitor 

Confirmed Implicated

Promotion of tumor regression 

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1gsd,3ljr

Glutathione Transferase A1-1,Glutathione S-Transferase

Suppressed enzyme and activity   Genotoxicity and carcinogenicity

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1mch 

Immunoglobulin Light Chain  Temerarily enhanced Ig

level

 Implicated Modulation of immune response

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1p1g  Macrophage Migration Inhibitory factor        

1ulb Purine Nucleoside Phosphorylase        

1zqf DNA Polymerase        

2nll Retinoic Acid Receptor        

1a25 Protein Kinase C Inhibition Confirmed Anticancer 37

1aa8 D-Amino Acid Oxidase    Implicated

   1afs  3 -Hydroxysteroid Dehydrogenase

Effect on androgen induced activity  

Hepatic steroid metabolism 

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1pth Prostaglandin H2 Synthase-1 Direct inhibition Confirmed Prevention of vasoconstriction 40

1sep Sepiapterin Reductase        

2toh Tyrosine 3-Monooxygenase        

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INVDOCK Test on Drug Target PredictionINVDOCK 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 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

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Results of Docking StudiesResults 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.

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Protein-Protein cases from protein-protein docking benchmark [6]:Enzyme-inhibitor – 22 casesAntibody-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.

Dataset and Testing ResultsDataset and Testing Results

Endonuclease I-PpoI (1EVX) with DNA (1A73). RMSD 0.87Å, rank 2

DNAendonucleasedocking solution

Estrogen receptor

Estradiol molecule from complex

docking solution

Estrogen receptor with estradiol (1A52). RMSD 0.9Å, rank 1, running time: 11 seconds

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Results Enzyme-Inhibitor Results Enzyme-Inhibitor dockingdockingComplex Description

pen. res.1

geom score time with ACE score

PDB receptor/ligand rmsd rank min. rmsd rank

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 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 21 Number of highly penetrating residues in unbound structures superimposed to complex

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Results Antibody-Antigen dockingResults Antibody-Antigen docking

Complex Description pen. res. 1

geom score time ACE score

PDB receptor/ligand rmsd rank min. rmsd rank

1AHW Antibody Fab 5G9/Tissue factor 3,3 2.5 29 10:12 2.5 10

1BQL* 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

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Quality of INVDOCK AlgorithmQuality of INVDOCK Algorithm Proteins. 1999; 36:1Proteins. 1999; 36:1

Molecule Docked Protein PDB Id

RMSDDescription of Docking Quality Energy

(kcal/mol)

Indinavir HIV-1 Protease 1hsg 1.38 Match -70.25

Xk263 Of Dupont Merck

HIV-1 Protease 1hvr 2.05 Match -58.07

Vac HIV-1 Protease 4phv 0.80 Match -88.46

Folate 

Dihydrofolate Reductase 1dhf 6.55 One end match, the other in different orientation -46.02

5-Deazafolate Dihydrofolate Reductase 2dhf 1.48 Match -65.49

Estrogen Estrogen Receptor 1a52 1.30 Match -45.86

4-Hydroxytamoxifen Estrogen Receptor

 3ert 

5.45 

Complete overlap, flipped along short axis -55.15

Guanosine-5'-[B,G-Methylene] Triphosphate

H-Ras P21 

121p 

0.94 Match-80.20

Glycyl-*L-Tyrosine 

Carboxypeptidase A 3cpa 3.56 Overlap, flipped along short axis-40.63

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Identification of the N-terminal Identification of the N-terminal peptide binding site of GRP94peptide 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

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Biological motivationBiological 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.

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GRP94 moleculeGRP94 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.

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DockingDocking

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

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GRP94 moleculeGRP94 molecule There is a binding site for inhibitors between the helices. There is another cavity produced by beta sheet on the opposite side.