دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.
المؤلفون: 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.
المصدر: Briefings in bioinformatics [Brief Bioinform] 2023 Jul 20; Vol. 24 (4).
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
أسماء مطبوعة: Publication: Oxford : Oxford University Press
Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-
مواضيع طبية MeSH: Molecular Dynamics Simulation* , Proteins*/chemistry, Protein Conformation
مستخلص: 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.)
التعليقات: Update of: bioRxiv. 2023 Apr 28;:. (PMID: 37163076)
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معلومات مُعتمدة: MC_UU_00009/2 United Kingdom MRC_ Medical Research Council; U01 CA258512 United States CA NCI NIH HHS
فهرسة مساهمة: Keywords: clustering; conformational ensembles; crystal structure analysis; dimensionality reduction; molecular dynamics (MD); proteins
المشرفين على المادة: 0 (Proteins)
تواريخ الأحداث: Date Created: 20230707 Date Completed: 20230724 Latest Revision: 20240313
رمز التحديث: 20240313
مُعرف محوري في PubMed: PMC10359083
DOI: 10.1093/bib/bbad242
PMID: 37418278
قاعدة البيانات: MEDLINE
الوصف
تدمد:1477-4054
DOI:10.1093/bib/bbad242