EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.

Autor: Conev A; Department of Computer Science, Rice University, Houston 77005, TX, USA., Rigo MM; Department of Computer Science, Rice University, Houston 77005, TX, USA., Devaurs D; MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK., Fonseca AF; Department of Biology and Biochemistry, University of Houston, Houston 77004, TX, USA., Kalavadwala H; Department of Biology and Biochemistry, University of Houston, Houston 77004, TX, USA., de Freitas MV; Department of Biology and Biochemistry, University of Houston, Houston 77004, TX, USA., Clementi C; Department of Physics, Freie Universität Berlin, Berlin 14195, Germany., Zanatta G; Department of Biophysics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91501-970, Brazil., Antunes DA; Department of Biology and Biochemistry, University of Houston, Houston 77004, TX, USA., Kavraki LE; Department of Computer Science, Rice University, Houston 77005, TX, USA.
Jazyk: angličtina
Zdroj: Briefings in bioinformatics [Brief Bioinform] 2023 Jul 20; Vol. 24 (4).
DOI: 10.1093/bib/bbad242
Abstrakt: Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.
(© The Author(s) 2023. Published by Oxford University Press.)
Databáze: MEDLINE
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