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Cosolvent Analysis Toolkit (CAT): a robust hotspot identification platform for cosolvent simulations of proteins to expand the druggable proteome

Lookup NU author(s): Quico Sabanes Zariquiey, Joao Victor de De Souza Cunha, Dr Agnieszka Bronowska

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2019, The Author(s).Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterization of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands. Despite their conceptual simplicity and effectiveness, the analysis of cosolvent MD trajectories relies on pocket volume data, which requires a high level of manual investigation and may introduce a bias. In this work, we present CAT (Cosolvent Analysis Toolkit): an open-source, freely accessible analytical tool, suitable for automated analysis of cosolvent MD trajectories. CAT is compatible with commonly used molecular graphics software packages such as UCSF Chimera and VMD. Using a novel hybrid empirical force field scoring function, CAT accurately ranks the dynamic interactions between the macromolecular target and cosolvent molecules. To benchmark, CAT was used for three validated protein targets with allosteric and orthosteric binding sites, using five chemically distinct cosolvent molecules. For all systems, CAT has accurately identified all known sites. CAT can thus assist in computational studies aiming at identification of protein “hotspots” in a wide range of systems. As an easy-to-use computational tool, we expect that CAT will contribute to an increase in the size of the potentially ‘druggable’ human proteome.


Publication metadata

Author(s): Sabanes Zariquiey F, de Souza JV, Bronowska AK

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2019

Volume: 9

Issue: 1

Online publication date: 13/12/2019

Acceptance date: 23/11/2019

Date deposited: 09/01/2020

ISSN (electronic): 2045-2322

Publisher: Nature Publishing Group

URL: https://doi.org/10.1038/s41598-019-55394-2

DOI: 10.1038/s41598-019-55394-2

PubMed id: 31836830


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