dc.contributor.author
Conev, Anja
dc.contributor.author
Rigo, Mauricio Menegatti
dc.contributor.author
Devaurs, Didier
dc.contributor.author
Fonseca, André Faustino
dc.contributor.author
Kalavadwala, Hussain
dc.contributor.author
de Freitas, Martiela Vaz
dc.contributor.author
Clementi, Cecilia
dc.contributor.author
Zanatta, Geancarlo
dc.contributor.author
Antunes, Dinler Amaral
dc.contributor.author
Kavraki, Lydia E.
dc.date.accessioned
2023-08-10T13:24:58Z
dc.date.available
2023-08-10T13:24:58Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/40433
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-40154
dc.description.abstract
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.
en
dc.format.extent
11 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by-nc/4.0/
dc.subject
conformational ensembles
en
dc.subject
dimensionality reduction
en
dc.subject
molecular dynamics (MD)
en
dc.subject
crystal structure analysis
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie
dc.title
EnGens: a computational framework for generation and analysis of representative protein conformational ensembles
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
bbad242
dcterms.bibliographicCitation.doi
10.1093/bib/bbad242
dcterms.bibliographicCitation.journaltitle
Briefings in Bioinformatics
dcterms.bibliographicCitation.number
4
dcterms.bibliographicCitation.volume
24
dcterms.bibliographicCitation.url
https://doi.org/10.1093/bib/bbad242
refubium.affiliation
Physik
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
1477-4054
refubium.resourceType.provider
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