Drug Discovery using Grid Technologies Yuichiro Inagaki - PowerPoint PPT Presentation

Drug Discovery using Grid Technologies Yuichiro Inagaki Biotechnology division Fuji Research Institute co.

Outline � Needs for grid technologies in drug discovery � g-Drug Discovery system � Test calculation results

Needs for grid technology in drug discovery � Increse in number of both drug candidate compounds and target � 10 7 molecules × 10 3 conformations � screening throughout a family: Kinases, GPCRs, … � Various type of calculation � Druglikeness screening � ADME/Tox screening More CPU Power � Conformational search Seemless connection � Pharmacophore screening � Docking � Molecular Orbital methods

“ g-Drug Discovery ” � Funded by Japan Science and Technology Agency (JST) � Components Filtering / Data mining Filtering / Data mining Phamacophore Phamacophore Phamacophore Phamacophore Phamacophore Phamacophore Phamacophore Phamacophore � DB system Overlap scoring / Clustering Overlap scoring / Clustering Overlap scoring / Clustering Overlap scoring / Clustering Mapping / Docking Mapping Mapping Mapping / Docking Docking Docking Kohonen mapping Kohonen mapping calculations calculations calculations calculations calculations calculations calculations calculations Drug - Drug-likeness Drug-likeness Drug- � Conflex-G Candidates Candidates Candidates Candidates for Docking for Docking for Docking for Docking � Xsi-G Scoring based on ∆ G Scoring based on ∆ G Scoring based on ∆ G Scoring based on ∆ G Exhaustive Exhaustive Exhaustive Exhaustive conformational conformational conformational conformational analysis analysis analysis analysis � REMD Hitlist Hitlist Hitlist Hitlist 3D-Drug 3D-Drug 3D-Drug 3D-Drug 2D-Drug 2D-Drug 2D-Drug 2D-Drug Libraries Libraries Libraries Libraries Libraries Libraries Libraries Libraries � FMO = = Data Grid Data Grid Data Grid Data Grid = = DrugML DrugML DrugML DrugML FMO(-MD) FMO(-MD) FMO(-MD) FMO(-MD) Conflex-G, Xsi-G Conflex-G, Xsi-G, Conflex-G, Xsi-G Conflex-G, Xsi-G, AbinitMP-G AbinitMP-G REMD REMD REMD REMD Grid environment Grid environment Grid environment Grid environment

DrugML a XML Schema for drug design • Use tags of CML as much as possible • Conformers • Complex • Descriptors universeList universe molecule Any tag of cml:atomArray drugml Any tag of atomArray Descriptor1D cml:molecule conformation molecule Any tag of bondArray conformationList Descriptor3D cml:bondArray DescriptorWHIM descriptor2D

Structure of DB system Xsi Browser Application Schema DrugML or CML General ID Operation General DB Operations Interface HTTP XML-RPC Interface DB HTTP (XML-RPC) Connection Servlet XML-RPC Interface XML:DB Xindice ID Server Database

Omni-RPC a Grid RPC system for Parallel Programming Supports typical master-worker grid applications such as � docking simulation. Users can use the same program for both clusters and grids. � Supports a local environment with "rsh", a grid environment � with Globus, and remote hosts with "ssh". OmniRPC inherits its API from Ninf, the programmer can use � OpenMP for easy-to- use parallel programming because the API is designed to be thread-safe. For a cluster over a private network, an agent process running � the server host functions as a proxy to relay communications between the client and the remote executables.

Xsi 2.0 � Combines Ligand Based Drug Design and Structure Based Drug Design � Montecarlo, minimization, docking by MMFF94s force field � 2D & 3D descriptors � Statistics,Clustering,Similarity � Machine Learning by support vector machine

LigandAlignment • Optimizes similarity between pharmacophore map and ligand • Pharamacophore map can be defined by physico-chemical properties and voids • VDW,hydrophobicity,HD,HA,arom aticity,electrostatic … • 0.6 sec/1 alignment (viracept)

Map of binding site HIV protease and inhibitor (DMP323)

Alignment onto binding site Alignment of JG-365 Binding Site Map

Pharmacophore screening by LigandAlignment 97 random compounds + 5 known HIV protease inhibitors 7 . 5 7 r a n d o m 6 . 5 h i t 6 e r o c 5 . 5 S 5 R a n k 1 J G - 3 6 5 4 . 5 R a n k 2 v i r a c e p t R a n k 3 L 7 3 5 5 2 4 R a n k 4 S B 2 0 3 3 8 6 4 R a n k 5 D M P 3 2 3 3 . 5 0 2 0 4 0 6 0 8 0 1 0 0 R a n k � Hit rate (10% ranked DB) ～ 50 %

Docking flow ligand Receptor MonteCarlo Finding binding site O H O H O H H H H Calculate WHIMs Calculate WHIMs C H 3 C H 3 C H 3 O H O H O H H H H Master Calculate similarities H H H N N N N N N Between ligand and pocket O O O Sort ligands by similatits O O O N H N H N H S S S Aliginment by using WHIMs C H 3 C H 3 C H 3 H 3 C H 3 C H 3 C C H 3 C H 3 C H 3 Viracept Workers Docking Docking Docking Docking

Calculation Environment n u m b e r l o c a t i o n C P U sR T T ( m s ) o f n o d e f s l i n F u j i - R I C ( T o k y o ) D u a l X e o n 2 . 4 G H z 5 0 . 0 1 9 D u a l X e o n 2 . 4 G H z 1 0 s T s u k u b a u n i v e r s i t y d e n n i 2 7 . 2 ( T s u k u b a ) D u a l X e o n 3 . 0 G H z 5 T s u k u b a u n i v e r s i t y a l i c e D u a l A t h l o n 1 8 0 0 + 1 6 2 7 . 2 ( T s u k u b a ) Total : 1 master + 71 workers

Speedup of calculations 80.0 70.0 60.0 50.0 Speedup 40.0 30.0 20.0 10.0 0.0 ) ) ) ) ) ) ) ) 0 2 1 2 0 2 0 2 ( 1 7 3 3 4 4 6 ( ( n ( ( ( ( ( l i n e s e s l e l i i c i c s c a l n n f i i s i l l n n l f a a a e e + + d d n + s i i n l n s i n f l s e f d

Docking results X-ray (yellow) Comp.(white) RMSD:1.77 Å

Summary � Hit rate more than 50% can be achieved � Protein family screening � LigandAlignment on grid necessary

Acknowledgements � Hiroshi Chuman (Tokushima Univ.) � Umpei Nagashima (AIST) � Hitoshi Goto (Toyohashi Univ. of Technology) � Mitsuhisa Sato (Tsukuba Univ.)

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LigandAlignment (r) (c1) (c2) リガンドアライメントによるファーマコフォアマップの最適化の様子。 (r) 参照分子 ( ベンゼン ) の MS( 原子質量 ) のマップ (c1) 候補分子 ( トルエン ) の最適化前の MS( 原子質量 ) のマップ (c2) 候補分子 ( トルエン ) の最適化後の MS( 原子質量 ) のマップ トルエン分子のベンゼン環の配置がベンゼン分子のベンゼン環の配置に近くな るように最適化されている。図はマップの等数値面を描いたもの。格子点数は 32*32*32 。