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.
%0 Journal Article
%1 Sala2023-go
%A Sala, D
%A Batebi, H
%A Ledwitch, K
%A Hildebrand, P W
%A Meiler, J
%D 2023
%I Elsevier BV
%J Trends Pharmacol. Sci.
%K AlphaFold; GPCR; biased discovery; drug library ligands; structure-based topic_lifescience ultra-large
%N 3
%P 150--161
%T Targeting in silico GPCR conformations with ultra-large library screening for hit discovery
%V 44
%X 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.
@article{Sala2023-go,
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.},
added-at = {2024-09-10T10:41:24.000+0200},
author = {Sala, D and Batebi, H and Ledwitch, K and Hildebrand, P W and Meiler, J},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/285b398ae5325520dd6822280e582285c/scadsfct},
interhash = {9c62017527f3cb9e62fc9e7cafa5cd72},
intrahash = {85b398ae5325520dd6822280e582285c},
journal = {Trends Pharmacol. Sci.},
keywords = {AlphaFold; GPCR; biased discovery; drug library ligands; structure-based topic_lifescience ultra-large},
language = {en},
month = mar,
number = 3,
pages = {150--161},
publisher = {Elsevier BV},
timestamp = {2024-09-10T12:00:15.000+0200},
title = {Targeting in silico {GPCR} conformations with ultra-large library screening for hit discovery},
volume = 44,
year = 2023
}
