TY - JOUR

T1 - Panel docking of small-molecule libraries - Prospects to improve efficiency of lead compound discovery

AU - Sarnpitak, Pakornwit

AU - Mujumdar, Prashant

AU - Taylor, Paul

AU - Cross, Megan

AU - Coster, Mark J.

AU - Gorse, Alain Dominique

AU - Krasavin, Mikhail

AU - Hofmann, Andreas

N1 - Funding Information: We thank David Camp for helpful discussions and critical reading of the manuscript. AH gratefully acknowledges funding by the National Health and Medical Research Council (APP 1044022 ) and the Australian Research Council (DP 140100599 ). AH and DG also gratefully acknowledge funding provided by ARC LIEF grant LE120100071 . Publisher Copyright: © 2015 Elsevier Inc. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.

PY - 2015/11/1

Y1 - 2015/11/1

N2 - Computational docking as a means to prioritise small molecules in drug discovery projects remains a highly popular in silico screening approach. Contemporary docking approaches without experimental parametrisation can reliably differentiate active and inactive chemotypes in a protein binding site, but the absence of a correlation between the score of a predicted binding pose and the biological activity of the molecule presents a clear limitation.Several novel or improved computational approaches have been developed in the recent past to aid in screening and profiling of small-molecule ligands for drug discovery, but also more broadly in developing conceptual relationships between different protein targets by chemical probing. Among those new methodologies is a strategy known as inverse virtual screening, which involves the docking of a compound into different protein structures. In the present article, we review the different computational screening methodologies that employ docking of atomic models, and, by means of a case study, present an approach that expands the inverse virtual screening concept.By computationally screening a reasonably sized library of 1235 compounds against a panel of 48 mostly human kinases, we have been able to identify five groups of putative lead compounds with substantial diversity when compared to each other. One representative of each of the five groups was synthesised, and tested in kinase inhibition assays, yielding two compounds with micro-molar inhibition in five human kinases.This highly economic and cost-effective methodology holds great promise for drug discovery projects, especially in cases where a group of target proteins share high structural similarity in their binding sites.

AB - Computational docking as a means to prioritise small molecules in drug discovery projects remains a highly popular in silico screening approach. Contemporary docking approaches without experimental parametrisation can reliably differentiate active and inactive chemotypes in a protein binding site, but the absence of a correlation between the score of a predicted binding pose and the biological activity of the molecule presents a clear limitation.Several novel or improved computational approaches have been developed in the recent past to aid in screening and profiling of small-molecule ligands for drug discovery, but also more broadly in developing conceptual relationships between different protein targets by chemical probing. Among those new methodologies is a strategy known as inverse virtual screening, which involves the docking of a compound into different protein structures. In the present article, we review the different computational screening methodologies that employ docking of atomic models, and, by means of a case study, present an approach that expands the inverse virtual screening concept.By computationally screening a reasonably sized library of 1235 compounds against a panel of 48 mostly human kinases, we have been able to identify five groups of putative lead compounds with substantial diversity when compared to each other. One representative of each of the five groups was synthesised, and tested in kinase inhibition assays, yielding two compounds with micro-molar inhibition in five human kinases.This highly economic and cost-effective methodology holds great promise for drug discovery projects, especially in cases where a group of target proteins share high structural similarity in their binding sites.

KW - Drug discovery

KW - Molecular docking

KW - Protein kinase inhibitors

UR - http://www.scopus.com/inward/record.url?scp=84939264266&partnerID=8YFLogxK

U2 - 10.1016/j.biotechadv.2015.05.006

DO - 10.1016/j.biotechadv.2015.05.006

M3 - Review article

C2 - 26025037

VL - 33

SP - 941

EP - 947

JO - Biotechnology Advances

JF - Biotechnology Advances

SN - 0734-9750

IS - 6

ER -