From genome to drug lead: Identification of a small-molecule inhibitor of the SARS virus

Abstract

Virtual screening, a fast, computational approach to identify drug leads [Perola, E.; Xu, K.; Kollmeyer, T. M.; Kaufmann, S. H.; Prendergast, F. G. J. Med. Chem.2000, 43, 401; Miller, M. A. Nat. Rev. Drug Disc.2002, 1 220], is limited by a known challenge in crystallographically determining flexible regions of proteins. This approach has not been able to identify active inhibitors of the severe acute respiratory syndrome-associated coronavirus (SARS-CoV) using solely the crystal structures of a SARS-CoV cysteine proteinase with a flexible loop in the active site [Yang, H. T.; Yang, M. J.; Ding, Y.; Liu, Y. W.; Lou, Z. Y. Proc. Natl. Acad. Sci. U.S.A.2003, 100, 13190; Jenwitheesuk, E.; Samudrala, R. Bioorg. Med. Chem. Lett.2003, 13, 3989; Rajnarayanan, R. V.; Dakshanamurthy, S.; Pattabiraman, N. Biochem. Biophys. Res. Commun.2004, 321, 370; Du, Q.; Wang, S.; Wei, D.; Sirois, S.; Chou, K. Anal. Biochem.2005, 337, 262; Du, Q.; Wang, S.; Zhu, Y.; Wei, D.; Guo, H. Peptides2004, 25, 1857; Lee, V.; Wittayanarakul, K.; Remsungenen, T.; Parasuk, V.; Sompornpisut, P. Science (Asia)2003, 29, 181; Toney, J.; Navas-Martin, S.; Weiss, S.; Koeller, A. J. Med. Chem.2004, 47, 1079; Zhang, X. W.; Yap, Y. L. Bioorg. Med. Chem.2004, 12, 2517]. This article demonstrates a genome-to-drug-lead approach that uses terascale computing to model flexible regions of proteins, thus permitting the utilization of genetic information to identify drug leads expeditiously. A small-molecule inhibitor of SARS-CoV, exhibiting an effective concentration (EC50) of 23 microM in cell-based assays, was identified through virtual screening against a computer-predicted model of the cysteine proteinase. Screening against two crystal structures of the same proteinase failed to identify the 23-microM inhibitor. This study suggests that terascale computing can complement crystallography, broaden the scope of virtual screening, and accelerate the development of therapeutics to treat emerging infectious diseases such as SARS and Bird Flu.

EUDOC on the IBM Blue Gene/L system: Accelerating the transfer of drug discoveries from laboratory to patient

“…EUDOC was originally devised to perform on a commodity computing cluster of loosely connected Intel Xeon** processors [2]. It has shown success in predicting drug-bound protein complexes, identifying drug leads, and reproducing crystal structures of small-molecule complexes [3,4,[6][7][8][9][10][11].…”

“…We explain, for a general audience, the computing algorithms used by EUDOC and the significance of the project. Thus, attention is given to hardware and programming details specific to EUDOC on the BG/L system, while information on the validation of the EUDOC program and its application to VS is left to other publications [2][3][4][5][6][7][8][9][10][11].…”

“…During the outbreak of severe acute respiratory syndrome (SARS) in 2003, the EUDOC program was used to identify anti-SARS drug candidates by docking 361,413 chemicals against a computer-generated 3D model of a chymotrypsin-like cysteine proteinase (CCP) from a SARS-associated coronavirus [9,25]. CCP is an ideal drug target for treating SARS viral infection because it is required for viral replication and transcription.…”

Structure-Based Drug Design and Structural Biology Study of Novel Nonpeptide Inhibitors of Severe Acute Respiratory Syndrome Coronavirus Main Protease

“…Moreover, although a number of nonpeptide inhibitors of SARS-CoV M pro have been discovered, such as bifunctional arylboronic acids, 22 isatin derivatives, 23 polyphenols, 24 etacrynic acid analogues, 25 cinanserin, 26 and other chemically diverse small molecules, 15,27,28 the lack of structure biology information on these compounds and their interactions with SARS-CoV M pro further makes the design more difficult. All the published structures up-to-date are complexed with peptidyl inhibitors through covalent bonding to SARS-CoV M pro .…”

“…[13][14][15][16][17][18][19] For examples, Liu et al 14 and Dooley et al 15 identified the inhibitors using 3D structure derived from molecular dynamic simulation of SARS-CoV M pro as a virtual screening target structure, while others used the pharmacophore model to predict potential inhibitors. 20,21 The discovery efforts by computer-aided drug design showed only a few cases of SARS-CoV M pro inhibition potency at micromolar range as confirmed by bioassay.…”