Journal Article

FZJ-2021-00763

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1

Ferraro, M. ; Moroni, E. ; Ippoliti, E.FZJ* ; Rinaldi, S. ; Sanchez-Martin, C. ; Rasola, A. ; Pavarino, L. F. (Corresponding author) ; Colombo, G. (Corresponding author)

2021
Soc. Washington, DC

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Please use a persistent id in citations: http://hdl.handle.net/2128/27291  doi:10.1021/acs.jpcb.0c09742

Abstract: Allosteric molecules provide a powerful means to modulate protein function. However, the effect of such ligands on distal orthosteric sites cannot be easily described by classical docking methods. Here, we applied machine learning (ML) approaches to expose the links between local dynamic patterns and different degrees of allosteric inhibition of the ATPase function in the molecular chaperone TRAP1. We focused on 11 novel allosteric modulators with similar affinities to the target but with inhibitory efficacy between the 26.3 and 76%. Using a set of experimentally related local descriptors, ML enabled us to connect the molecular dynamics (MD) accessible to ligand-bound (perturbed) and unbound (unperturbed) systems to the degree of ATPase allosteric inhibition. The ML analysis of the comparative perturbed ensembles revealed a redistribution of dynamic states in the inhibitor-bound versus inhibitor-free systems following allosteric binding. Linear regression models were built to quantify the percentage of experimental variance explained by the predicted inhibitor-bound TRAP1 states. Our strategy provides a comparative MD–ML framework to infer allosteric ligand functionality. Alleviating the time scale issues which prevent the routine use of MD, a combination of MD and ML represents a promising strategy to support in silico mechanistic studies and drug design.

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Contributing Institute(s):

1. Computational Biomedicine (IAS-5)
2. Computational Biomedicine (INM-9)

Research Program(s):

1. 524 - Molecular and Cellular Information Processing (POF4-524) (POF4-524)

Appears in the scientific report 2021

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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