ESPRESSO (Extremely Speedy PRE-Screening method with Segmented compounds) 1

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1 Vol.2016-MPS-108 o.18 Vol.2016-BI-46 o.18 ESPRESS 1,4,a) 2,4 2,4 1,3 1,3,4 1,3,4 - ESPRESS (Extremely Speedy PRE-Screening method with Segmented cmpounds) 1 Glide HTVS ESPRESS 2, ESPRESS: An ultrafast compound pre-screening method based on compound decomposition Keisuke Yanagisawa 1,4,a) Shunta Komine 2,4 Shogo D. Suzuki 2,4 Masahito hue 1,3 Takashi Ishida 1,3,4 Yutaka Akiyama 1,3,4 Abstract: Recently, the number of available protein tertiary structures and compounds has increased. However, structure-based virtual screening is computationally expensive due to docking simulations. Thus, methods that filter out obviously unnecessary compounds prior to computationally expensive docking simulations have been proposed. However, the calculation speed of these methods is not fast enough to evaluate more than 10 million compounds. In this study, we proposed a novel, docking-based pre-screening protocol named ESPRESS (Extremely Speedy PRE-Screening method with Segmented cmpounds). Partial structures (fragments) are often common among several compounds; therefore, the number of fragment variations needed for evaluation is smaller than that of compounds. ur method increased calculation speeds approximately 200-fold compared to conventional methods. Keywords: computational drug discovery, virtual screening, compound decomposition, pre-screening 1 Department of omputer Science, School of omputing, Tokyo Institute of Technology 2 Department of omputer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology 3 Advanced omputational Drug Discovery Unit (ADD), Institute of Innovative Research, Tokyo Institute of Technology 4 1. ( Finding a needle in a haystack ) Education Academy of omputational Life Sciences (ALS), Tokyo Institute of Technology a) yanagisawa@bi.cs.titech.ac.jp c 2016 Information Processing Society of Japan 1

2 [1] in vitro virtual screening (VS) [2] (structure-based virtual screening, SBVS) SBVS 3 SBVS Protein Data Bank (PDB) % [3] ZI [4] 3,400 SBVS - [5] AutoDock Vina[6] PU [7] 1 ZI 3, PU (pre-screening) [8] (ligand-based) (structure-based) 2 [7], [9] [10] [11] [7] Glide HTVS (high-throughput virtual screening) [12] Panther[13] 3,400 ZI 1 PU F H H l l H 3 1 l H 3 ESPRESS F Glide SP, AutoDock Vina - ESPRESS (Extremely Speedy PRE-Screening method with Segmented cmpounds) - 2. ESPRESS 2.1 ESPRESS Vol.2016-MPS-108 o.18 Vol.2016-BI-46 o.18 [14] ( 1 ) H H l c 2016 Information Processing Society of Japan 2

3 Vol.2016-MPS-108 o.18 Vol.2016-BI-46 o.18 例外ケース 孤 原 の統合 2 4 / ( 2 ) 3 / AutoDock Vina[6] Glide[12] GLD[15] ( 1 ) (SUM) 1 SUM ( 2 ) MAX MAX = max score f (2) f 1 MAX ( 3 ) Generalized Sum, GS GS x = x f (score f ) x (3) GS 1 SUM GS MAX GS SUM MAX GS 2 GS 3 GS score f GS 3 ESPRESS GS DUD-E (Directory of Useful Decoys, Enhanced)[16] DUD-E 102 ZI [4] all purchasable all boutique 28,629,602 SUM = f score f (1) 2.3 ESPRESS Python c 2016 Information Processing Society of Japan 3

4 GPLv3 Glide SP Glide HTVS Glide HTVS 2.4 TSUBAME 2.5 Thin Thin 6 Intel Xeon X5670 PU 2 54 GB 12 PU 2.5 Glide 1 1 PU PU Enrichment Factor (EF)[17] EF x% = Pos x%/all x% Pos 100% /All 100% (4) All x% x% Pos x% x% x% EF 1% EF 2% 2.6 ESPRESS ( 1 ) 2%, 5%, 10% ( 2 ) Glide SP 1%, 2%EF 1% EF 2% 3. ESPRESS 2 Glide HTVS ZI 28,629,602 Glide SP 1 ZI 28,629,602 DUD-E 3 PU Glide HTVS DUD-E [PU hours] ESPRESS-SP ESPRESS-HTVS Glide HTVS 2 AES 42.6( 76.8) 22.8( 143.1) EGFR 38.9( 126.4) 21.5( 229.3) PGH1 41.8( 88.0) 20.9( 175.4) DUD-E 102 ESPRESS ESPRESS-SP Enrichment Factors (EF) 2% 1% 5% 1% 10% 1% 5% 2% 10% 2% SUM MAX GS GS SUM MAX ESPRESS-HTVS GS GS Glide HTVS ( ) a% b% a% Glide SP EF b% ESPRESS-SP 2 PU Glide HTVS ESPRESS-HTVS 1 PU Glide HTVS 140 PU ESPRESS DUD-E 102 EF GS 3 SUM, MAX ESPRESS-SP ESPRESS-HTVS GS 2 GS 3 ESPRESS-SP ESPRESS Glide HTVS Vol.2016-MPS-108 o.18 Vol.2016-BI-46 o ESPRESS 200 ZI 28,629, ,319 c 2016 Information Processing Society of Japan 4

5 Vol.2016-MPS-108 o.18 Vol.2016-BI-46 o.18 4 DUD-E AES ZI (logp) (A) ZI 0.1% ESPRESS-SP 0.1% (B) (A) () DUD-E AES (A) (B) GS 3 MAX GS 3 3 ZI drugs now 10,639, hembl (version 21) [18] 1,583, ,360 Pubhem [19] ,527,810 2,082, GS 3 SUM Drwal [7] 3 ESPRESS DUD-E AES ZI ESPRESS-SP SVM Glide HTVS 3 0.1% ESPRESS-SP DUD-E 4 Tanimoto 5 4 ESPRESS [20] c 2016 Information Processing Society of Japan 5

6 Vol.2016-MPS-108 o.18 Vol.2016-BI-46 o.18 ( 3 ) V c > kv p k 1 k k = 1 k = 1 k k k = ESPRESS ligand efficiency[23] 5 DUD-E AES Tanimoto ZI 0.1% ZISVM 0.1% SVMGlide HTVS 0.1% Glide HTVSESPRESS-SP 0.1% ESPRESS 5 SVM Tanimoto ESPRESS ( 1 ) V c Zhao [21] V c = V a 5.92 B 14.7R A 3.8R A (5) a c V a B R A R A ( 2 ) V p Sitemap[22] 4.5 ESPRESS DUD-E AES 4.4 ESPRESS-SP ZI ZI (A) logp 1.84 [10] 6(B) 6() ESPRESS 6() 5. ESPRESS Glide HTVS 2, GS c 2016 Information Processing Society of Japan 6

7 Vol.2016-MPS-108 o.18 Vol.2016-BI-46 o.18 H 2 H 2 6 (A)ESPRESS-SP AES ZI (B)ZI () Glide SP (A) (B) () JSPS (A) ( ) JST REST EBD: [1] Klon A.E. et al.: Finding more needles in the haystack: a simple and efficient method for improving highthroughput docking results, J. Med. hem. 47, , [2] Sliwoski G. et al.: omputational methods in drug discovery, Pharmacol. Rev. 66, , [3] Rose P.W. et al.: The RSB Protein Data Bank: views of structural biology for basic and applied research and education, ucleic Acids Res. 43, D345 D356, [4] Irwin J.J. et al.: ZI: a free tool to discover chemistry for biology, J. hem. Inf. Model. 52, , [5] Meng X. et al.: Molecular docking: a powerful approach for structure-based drug discovery, urr. omput. Aided Drug Des. 7, , [6] Trott. et al.: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient otimization, and multithreading, J. omput. hem. 31, , [7] Drwal M.. et al.: ombination of ligand- and structure-based methods in virtual screening, Drug Discov. Today Technol. 10, e395 e401, [8] Kumar A. et al.: Hierarchical virtual screening approaches in small-molecule drug discovery, Methods 71, 26 37, [9] Ferreira L.G. et al.: Molecular docking and structurebased drug design strategies, Molecules 20, , [10] Lipinski.A. et al.: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Deliv. Rev. 23, 3 25, [11] Ripphausen P. et al.: State-of-the-art in ligand-based virtual screening, Drug Discov. Today 16, , [12] Friesner R.A. et al.: Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy, J. Med. hem. 47, , [13] iinivehmas S.P. et al.: Ultrafast protein structurebased virtual screening with Panther, J. omput. Aided Mol. Des. 29, , [14] : -, 2015-BI-42, 1 8, [15] Verdonk M.L. et al.: Virtual screening using proteinligand docking: avoiding artificial enrichment, J. hem. Inf. omput. Sci. 44, , [16] Mysinger M.M. et al.: Directory of Useful Decoys, Enhanced (DUD-E): better ligands and decoys for better benchmarking, J. Med. hem. 55, , [17] Hamza A. et al.: Ligand-based virtual screening approach using a new scoring function, J. hem. Inf. Model. 52, , [18] Bento A.P. et al.: The hembl bioactivity database: an update, ucleic Acids Res. 42, , [19] Kim S. et al.: Pubhem substance and compound databases, ucleic Acids Res. 44, D , [20] Verdonk M.L. et al.: Virtual screening using proteinligand docking: avoiding artificial enrichment, J. hem. Inf. omput. Sci. 44, , [21] Zhao Y.H. et al.: Fast calculation of van der Waals volume as a sum of atomic and bond contribu-tions and its application to drug compounds, J. rg. hem. 68, , [22] Halgren T.A.: Identifying and characterizing binding sites and assessing druggability, J. hem. Inf. Model. 49, , [23] Shultz M.D.: Setting expectations in molecular optimizations: Strengths and limitations of commonly used composite parameters, Bioorg. Med. hem. Lett. 23, , c 2016 Information Processing Society of Japan 7

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