Neuramindase (NA) and replication of virions

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

2. R’ Oseltamivir R=H R’=amine Zanamivir R=guanidine R Neuraminidase Inhibitors : Predicted mutation site by structure overlay and sequence alignment : Reported mutation site Peramivir R=H

3. Drug discovery at initial step “A needle in a haystack” Screening is the first measure to take for the biological activity of each compound in a large compound collection against an disease target. HTS: 104 – 105 cpd/day uHTS: >105 cpd/day How to reduced pre-screening cost$ ?

4. screening focused library  hit rate *  cost Modeling as a complement to HTS in drug discovery To improve hit rate$ focused library Modified from DDT vol. 3, 4, 160-178(1998)

5. Can large-scale “screening” be deployed on a Grid platform? ModelingInteracting Complexes

6. Challenges of large scale in-silico screening Application Characteristics 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)

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

8. Modeling Complex Targets Compound 2D compound library Lipinski’s RO5 “drug-like” structure generation energy minimization 8 structures 3D structure Wisdom < 6 weeks ionization tautermization translation / step=2.0 Å quaternion / step =20 degree torsion / step= 20 degree number of energy evaluation =1.5 X 106 max. number of generation =2.7 X 104 run number =50 3D structure library selection 308,585

9. GNA 2.4% Enrichment of primary in silico HTS Original Type: T06 Global effectiveness: (Hitssampled/Nsampled)/(Hitstotal/Ntotal) Pearlman & Charifson, JMC, 2001 Pre-sceening (AUTODOCK) over collection and sample first 15% EF1 = (5/6)/15% = 5.5 Re-ranking (SDDB) first 15% and sample first 5% EF2 = (5/6)/(5%*15%) = 111 DAN 35% pKd=5.3 4AM 13% pKd=7.3 pKd=7.5 Dna 4AM GNA Ki=4uM Ki=150nM Ki=1nM 15% cut off GNA=zanamivir

10. Assay results of first 5% ranked n=123 [sub]=100uM T06 NA+ NA-

11. T01:E119A T05:R293K Can point mutation to inhibitory effectiveness be predicted ? Effects of point mutation potential hits T01 E119A

12. Any additional information for medchem in hits optimization? Popular rings and groups within hits 44 32 38 43 150 44 53 82 93 151 -NO2 -CO2 -PO3 -SO2 38 41 46 80

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

14. Assay results of beta-lactam based compounds Z’=0.72 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.

15. Summary • 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 308585 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.

16. Acknowledgments Academia Sinica: Target and docking preparation, grid deployment, output analysis Genomics Research CenterYing-Ta Wu Grid Computing Team Hurng-Chun Lee Li-Yung Ho Hsin-Yen Chen Simon C. Lin Eric Yen TWGrid: infrastructure support of Taiwan 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 Massimo Lamanna Jakub Moscicki EMBRACE european network of excellence: project support BioinfoGRID european project: project support a world-wide infrastructure providing over than 5,000 CPUs AUVERGRID : Infrastructure support