Sala D, Batebi H, Ledwitch K, Hildebrand PW, Meiler J. Targeting in silico GPCR conformations with ultra-large library screening for hit discovery.
Trends Pharmacol Sci 2023;
44:150-161. [PMID:
36669974 PMCID:
PMC9974811 DOI:
10.1016/j.tips.2022.12.006]
[Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/23/2022] [Accepted: 12/27/2022] [Indexed: 01/20/2023]
Abstract
The use of deep machine learning (ML) in protein structure prediction has made it possible to easily access a large number of annotated conformations that can potentially compensate for missing experimental structures in structure-based drug discovery (SBDD). However, it is still unclear whether the accuracy of these predicted conformations is sufficient for screening chemical compounds that will effectively interact with a protein target for pharmacological purposes. In this opinion article, we examine the potential benefits and limitations of using state-annotated conformations for ultra-large library screening (ULLS) in light of the growing size of ultra-large libraries (ULLs). We believe that targeting different conformational states of common drug targets like G-protein-coupled receptors (GPCRs), which can regulate human physiology by switching between different conformations, can offer multiple advantages.
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