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

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dc.citation.articleNumberbbad242en_US
dc.citation.issueNumber4en_US
dc.citation.journalTitleBriefings in Bioinformaticsen_US
dc.citation.volumeNumber24en_US
dc.contributor.authorConev, Anjaen_US
dc.contributor.authorRigo, Mauricio Menegattien_US
dc.contributor.authorDevaurs, Didieren_US
dc.contributor.authorFonseca, André Faustinoen_US
dc.contributor.authorKalavadwala, Hussainen_US
dc.contributor.authorde Freitas, Martiela Vazen_US
dc.contributor.authorClementi, Ceciliaen_US
dc.contributor.authorZanatta, Geancarloen_US
dc.contributor.authorAntunes, Dinler Amaralen_US
dc.contributor.authorKavraki, Lydia Een_US
dc.date.accessioned2023-08-01T17:29:48Zen_US
dc.date.available2023-08-01T17:29:48Zen_US
dc.date.issued2023en_US
dc.description.abstractProteins 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.en_US
dc.identifier.citationConev, Anja, Rigo, Mauricio Menegatti, Devaurs, Didier, et al.. "EnGens: a computational framework for generation and analysis of representative protein conformational ensembles." <i>Briefings in Bioinformatics,</i> 24, no. 4 (2023) Oxford University Press: https://doi.org/10.1093/bib/bbad242.en_US
dc.identifier.digitalbbad242en_US
dc.identifier.doihttps://doi.org/10.1093/bib/bbad242en_US
dc.identifier.urihttps://hdl.handle.net/1911/115042en_US
dc.language.isoengen_US
dc.publisherOxford University Pressen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial (CC BY-NC) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of Fair Use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_US
dc.titleEnGens: a computational framework for generation and analysis of representative protein conformational ensemblesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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