GPCR structure modeling and ligand docking: GPCR Dock2008

Transcription

1 KO18/KP32 第 37 回構造活性相関シンポジウム平成 21 年 11 月 20 日北里大学薬学部コンベンションホール GPCR structure modeling and ligand docking: GPCR Dock2008 Kazuhiko Kanou 1, Daisuke Takaya 2, Genki Terashi 1, Mayuko Takeda Shitaka 1,2 and Hideaki Umeyama 1,2 1) School of Pharmacy, Kitasato University, Shirokane, Minato ku, Tokyo , Japan 2) RIKEN Systems and Structural Biology Center, Suehiro cho, Tsurumi ku, Yokohama , Japan 1

2 Protein structure prediction contests to establish current capabilities and limitations of protein structure prediction CAPRI start protein protein docking 2008 GPCR Dock 2008 GPCR modeling & docking of antagonist CASP start protein structure prediction 2

3 Critical Assessment of the technique of protein Structure Prediction (CASP) LRDLMSQSRTREILTKTTVDHMAIIKK YTSGRQEKNPALRMKWMMAMRYPI TADKRIMDMIPERNEQGQTLWSKTN DAGSDRVMVSPLAVTWWNRNGPTT STVHYPKVYKTYFEKVERLKHGTFGPV HFRNQVKIRRRVDTNPGHADLSAKEA QDVIMEVVFPNEVGARILTSESQLAIT Sequence 1994 Predict CASP start 3D Model 2001 CAPRI start protein protein docking 2008 GPCR Dock 2008 GPCR modeling & docking of antagonist protein structure prediction 3

4 Critical Assessment of the Prediction of protein protein Interaction (CAPRI) dock CAPRI start protein protein docking 2008 GPCR Dock 2008 GPCR modeling & docking of antagonist CASP start protein structure prediction 4

5 Protein structure prediction contests 1994 CASP start 2001 CAPRI start 2008 GPCR Dock 2008 GPCR modeling & docking of ligand GPCR Dock 2008 HomePage 5

6 Protein structure prediction contests Predictors were asked to blindly predict models of the human A2A Adenosine receptor in complex with the ligand ZM (inverse agonist) 1994 CASP start 2001 CAPRI start 2008 GPCR Dock 2008 GPCR modeling & docking of antagonist GPCR Dock 2008 HomePage 6

7 Organizers GPCR Dock 2008 rule query Sequence of human A2A receptor MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFV VSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLVLTQS SIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAI GLTPMLGWNNCGQPKEGKNHSQGCGEGQVACLFEDVVPMNYMVYF NFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTL QKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDCSHAPLWLM YLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFK AAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHPERRPNG YALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLA QDGAGV Protein modeling Ligand docking 31days 2D structure of ZM Ligand Docking submit predictors submit up to ten ranked models Organizers

8 Methods Sequence FAMSD SKE CHIMERA Alignment Template:2vt4 model model model model model model Modeling FAMS Ligand&Complex 10 times model model model model 10 models Docking GENIUS 10 docking10 10 pose docking10 pose docking10pose docking10pose docking10 pose docking10 pose docking10pose docking10 pose docking pose docking pose Clustering 100 docking models Submit

9 10 submitted models 9

10 Ligand docking GENIUS poteintial Utotal = Usar + Uhbond + Uhydro + Ustack + Uvdw + Uinternal Key residues : ASN 181, ASN 253 and TYR 271 TYR 271 ASN 181 ASN

11 Ligand docking GENIUS poteintial Utotal = Usar + Uhbond + Uhydro + Ustack + Uvdw + Uinternal Key residues : ASN 181, ASN 253 and TYR 271 TYR 271 ASN 181 ASN

12 Results Assessment by organizers Community wide blind assessment of methods for GPCR structure modeling and docking Mayako Michino, Enrique Abola, GPCR Assessment Participants*, Charles L. Brooks III, J. Scott Dixon, John Moult, Raymond C. Stevens Nat Rev Drug Discov Jun;8(6): *GPCR Assessment Participants: Kazuhiko Kanou 1, Daisuke Takaya 2, Genki Terashi 1, Mayuko Takeda Shitaka 1,2 and Hideaki Umeyama 1,2 ( 1 School of Pharmacy, Kitasato University, Tokyo, Japan; 2 RIKEN Systems and Structural Biology Center, Yokohama, Japan) 12

13 206 models submitted by 29 different teams. Assessment criteria LigandRMSD Number of Correct Contacts 13

14 Assessment criteria (1) Ligand RMSD for ligand conformation Model RMSD Xray superimposing the protein Cα atoms of the model and the crystal structure. 14

15 Assessment criteria (2) Number of correct contacts for protein ligand interaction Xray Model 15

16 Assessment criteria Z combined = ( Z LigandRMSD + Z N_CorrectContacts )/2 LigandRMSD Number of Correct Contacts Ligand Conformation Protein Ligand interaction Community wide blind assessment of methods for GPCR structure modeling and docking Nat Rev Drug Discov Jun;8(6):

17 Summary of results for the best models from the top ranking groups. Sorted by Combined Z score 17 Community wide blind assessment of methods for GPCR structure modeling and docking Nat Rev Drug Discov Jun;8(6):

18 Results for the ligand docking Our model Ligand RMSD(A) Number of Correct Contacts Average ９ ５Å ４ Green & cyan : X Ray structure Magenta & yellow : our model ASN 181 ASN 253 TYR 271 TM7 18

19 Summary of results for the best models from the top ranking groups. Sorted by Combined Z score 29 groups Community wide blind assessment of methods for GPCR structure modeling and docking 19 Nat Rev Drug Discov Jun;8(6):

20 Summary of results for the best models from the top ranking groups. Sorted by Combined Z score 29 groups Community wide blind assessment of methods for GPCR structure modeling and docking 20 Nat Rev Drug Discov Jun;8(6):

21 Results for the protein modeling Protein RMSD_CA 3.5 TM I VII Ca RMSD

22 Helical shifts in the TM region Red : Xray Blue : Our Model TM7 TM6 TM1 TM5 TM3 TM4 TM2 22

23 Results for the ligand docking Ligand RMSD(A) Number of Correct Contacts Green & cyan : X Ray structure Magenta & yellow : our model ASN 253 TYR 271 TM7 23

24 Difficulty in loop modeling Red : Xray Blue : Our Model RMSD for ECL2 residues : 7.1 A ECL2 ECL3 24

25 Summary of results for the best models from the top ranking groups. Sorted by Combined Z score 29 groups Community wide blind assessment of methods for GPCR structure modeling and docking 25 Nat Rev Drug Discov Jun;8(6):

26 Difficulty in loop modeling Red : Xray Blue : Our Model RMSD for ECL2 residues : 7.1 A ECL2 ECL3 F168 26

27 Conclusion Our original programs of FAMSD, SKE CHIMERA, FAMS Ligand&Compelx, GENIUS were used. Our model was ranked in the 7th in the assessment. Requirements of improvement For modeling 1. Accurate modeling of the structurally divergent regions (such as extracellular loops), 2. refinement and improvement over the best available template (such as helical shifts in the TM region) For docking Automatically identification of key residues such as ASN for protein ligand interaction are required. 27