HA Neuramindase (NA) and replication of virions A enzyme, cleaves host receptors help release of new virions NA Modeling HTS against Inf-A NA on Grid Ying-Ta Wu* Academia Sinica, Genomics Research Center

Neuraminidase Inhibitors Zanamivir R=guanidine Oseltamivir R=H R’=amine R’ R Peramivir R=H : Predicted mutation site by structure overlay and sequence alignment : Reported mutation site MutationN1N2 R292K oseltamivir Zanamivir H274Y(F)oseltamivir N294Soseltamivir?oseltamivir E119Voseltamivir?oseltamivir E119(G;A;D)oseltamivir?Zanamivir

Drug discovery at initial step Screening is the first measure to take for the biological activity of each compound in a large compound collection against an disease target. HTS: 10 4 – 10 5 cpd/day uHTS: >10 5 cpd/day “A needle in a haystack” How to reduced pre-screening cost$ ?

Modified from DDT vol. 3, 4, (1998) Modeling as a complement to HTS in drug discovery focused library screening focused library  hit rate *  cost To improve hit rate $

Can large-scale “screening” be deployed on a Grid platform? Modeling Interacting Complexes

Virtual screening based on molecular docking is the most time consuming part in structure-based drug design workflow Problem size: Number of docking tasks = N x M –8 predicted possible variants of Influenza A neuraminidase N1 as targets –300 K compound structures –  2.4M docking jobs Computing challenge: CPU-bound application –Each Autodock docking requires ~ 30 mins CPU time –Required computing power in total ~ 137 CPU years (a rough measurement based on Xeon 2.8 GHz) Storage requirement: huge amount of output –Each docking produces results with the size of 130 KByte –Required storage space in total ~ 600 GByte (with 1 back-up) Challenges of large scale in-silico screening Application Characteristics

Evaluate potential targets and model their 3D structures Prepare the large-scale docking using Autodock3. Development of the grid environment for a large-scale deployment. The deployment H5N1

translation / step=2.0 Å quaternion / step =20 degree torsion / step= 20 degree number of energy evaluation =1.5 X 10 6 max. number of generation =2.7 X 10 4 run number =50 translation / step=2.0 Å quaternion / step =20 degree torsion / step= 20 degree number of energy evaluation =1.5 X 10 6 max. number of generation =2.7 X 10 4 run number =50 2D compound library 3D structure “drug-like” Lipinski’s RO5 ionization tautermization 3D structure library structure generation energy minimization 308,585 8 structures Modeling Complex Targets Compound selection Wisdom < 6 weeks

Enrichment of primary in silico HTS GNA 2.4% 15% cut off GNA=zanamivir Original Type: T06 DAN 35% 4AM 13% pKd=5.3 pKd=7.3 pKd=7.5 Ki=4uM Ki=150nM Ki=1nM Dna 4AM GNA Global effectiveness: (Hits sampled /N sampled )/(Hits total /N total ) Pearlman & Charifson, JMC, 2001 Pre-sceening (AUTODOCK) over collection and sample first 15% EF 1 = (5/6)/15% = 5.5 Re-ranking (SDDB) first 15% and sample first 5% EF 2 = (5/6)/(5%*15%) = 111

01H ± 14.8 A B C D ± 0.1 E F G H H A B C D E ±0.9 F G ±0.1 H [sub]=100uM Assay results of first 5% ranked NA+ NA- T06 n=123

Can point mutation to inhibitory effectiveness be predicted ? T01 E119A T01:E119AT05:R293K Effects of point mutation potential hits

Any additional information for medchem in hits optimization? Popular rings and groups within hits -NO 2 -CO 2 -PO 3 -SO 2

beta-lactams Examples Arg_371 Tyr_347 Ser_246 Arg_118 Arg_156 Arg_152 Glu_119 Russell et al, NATURE, 443, 45-49, 2006

NA-H00045NANA+ 0 ± ± Z’=0.72 Assay results of beta-lactam based compounds A fluorometric assay was used to determine the NA activity with the fluorogenic substrate 2’-(4-methylumbelliferyl)-a-D- N-acetylneuraminic acid (MUNANA; Sigma). The fluorescence of the released 4-methylumbelliferone was measured.

–We demonstrated that huge compound collection can be effectively enriched by executing docking tasks on Grid. A estimated 105 year molecular docking process was shorten to 6 weeks by using WISDOM and DIANE frameworks –A set of “potential hits” ( interacting complexes with higher affinities and proper docked poses) was selected in first 5% re-ranked, which covered 2250 compound out of initial compounds (enrichment = 111). Experimental assay confirms 7 actives out of 123 purchased “potential hits”, which proved the usefulness of our work. –Mutation effects to compound activity may be predicted with similar method. Among the modeled 8 targets, the variants, T01(E119A) and T05(R293K) had greater impacts on the activities of “potential hits” and known drug, such zanamivir. The unique residue, Tyr344 also had effects on the compound binding and should be included in future drug design. –A workflow that mimic real HTS procedures with integration of chemical information and tools for automating post-analysis is expected. Summary

Academia Sinica: Target and docking preparation, grid deployment, output analysis Genomics Research Center Ying-Ta Wu Grid Computing Team Hurng-Chun Lee Li-Yung Ho Hsin-Yen Chen Simon C. Lin Eric Yen LPC (CNRS/IN2P3): Grid application development and deployment PCSV : Plate-forme de Calcul pour les Siences de la Vie Vincent Breton Nicolas Jacq Jean Salzemann Yannick Legre IT SERVICE Matthieu Reichstadt Emmanuel Medernach Institute for Biomedical Technologies (CNR): docking preparation, grid deployment Luciano Milanesi Ermanna Rovida Pasqualina D'Ursi Ivan Merelli ARDA: DIANE support TWGrid: infrastructure support of Taiwan EMBRACE european network of excellence: project support BioinfoGRID european project: project support AUVERGRID : Infrastructure support Massimo Lamanna Jakub Moscicki Acknowledgments a world-wide infrastructure providing over than 5,000 CPUs