title: Panel docking of small-molecule libraries - Prospects to improve efficiency of lead compound discovery.
abstract: 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.
authors.affiliation: Eskitis Institute for Drug Discovery, Griffith University, Nathan, Qld, Australia.
authors.forename: Pakornwit
authors.initials: P
authors.lastname: Sarnpitak
authors.affiliation: Eskitis Institute for Drug Discovery, Griffith University, Nathan, Qld, Australia.
authors.forename: Prashant
authors.initials: P
authors.lastname: Mujumdar
authors.affiliation: School of Biological Sciences, The University of Edinburgh, Scotland, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Taylor
authors.affiliation: Eskitis Institute for Drug Discovery, Griffith University, Nathan, Qld, Australia.
authors.forename: Megan
authors.initials: M
authors.lastname: Cross
authors.affiliation: Eskitis Institute for Drug Discovery, Griffith University, Nathan, Qld, Australia.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Coster
authors.affiliation: QFABBioinformatics, Institute for Molecular Bioscience, University of Queensland, St Lucia, Qld, Australia.
authors.forename: Alain-Dominique
authors.initials: AD
authors.lastname: Gorse
authors.affiliation: Eskitis Institute for Drug Discovery, Griffith University, Nathan, Qld, Australia. Electronic address: m.krasavin@spbu.ru.
authors.forename: Mikhail
authors.initials: M
authors.lastname: Krasavin
authors.affiliation: Eskitis Institute for Drug Discovery, Griffith University, Nathan, Qld, Australia; Faculty of Veterinary Science, The University of Melbourne, Parkville, Victoria, Australia; Queensland Tropical Health Alliance, Smithfield, Australia. Electronic address: a.hofmann@griffith.edu.au.
authors.forename: Andreas
authors.initials: A
authors.lastname: Hofmann
mesh_terms: D055808:Drug Discovery; D006801:Humans; D062105:Molecular Docking Simulation; D011485:Protein Binding; D047428:Protein Kinase Inhibitors; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D047428:Protein Kinase Inhibitors; D054852:Small Molecule Libraries
keywords: Drug discovery; Molecular docking; Protein kinase inhibitors