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A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

Medicine and Health

A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

I. Piazza, N. Beaton, et al.

Discover LiP-Quant, an innovative machine learning-based pipeline that revolutionizes drug target deconvolution using limited proteolysis and mass spectrometry. This groundbreaking research by Ilaria Piazza and colleagues showcases the identification of small-molecule targets, binding sites, and even a novel fungicide target, expanding the horizons of drug development!

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Playback language: English
Abstract
Unraveling the mechanism of action and molecular target of small molecules remains a major challenge in drug development. This paper introduces LiP-Quant, a machine learning-based drug target deconvolution pipeline using limited proteolysis coupled with mass spectrometry. LiP-Quant identifies small-molecule targets, predicts binding sites, and estimates binding affinities across diverse compound classes and species, including human cells. Its effectiveness is demonstrated through target identification for various drugs and the discovery of a novel fungicide target.
Publisher
Nature Communications
Published On
Aug 21, 2020
Authors
Ilaria Piazza, Nigel Beaton, Roland Bruderer, Thomas Knobloch, Crystel Barbisan, Lucie Chandat, Alexander Sudau, Isabella Siepe, Oliver Rinner, Natalie de Souza, Paola Picotti, Lukas Reiter
Tags
machine learning
drug development
mass spectrometry
target identification
binding affinities
small molecules
limited proteolysis

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