A probabilistic model for detecting rigid domains in protein structures | Zendy

Thach Ngoc Nguyen | Zendy; Michael Habeck | Zendy

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A probabilistic model for detecting rigid domains in protein structures

Author(s) -

Thach Ngoc Nguyen,

Michael Habeck

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw442

Subject(s) - python (programming language) , computer science , gibbs sampling , probabilistic logic , rigid body , markov chain , markov chain monte carlo , algorithm , bayesian probability , protein structure , inference , translation (biology) , artificial intelligence , machine learning , physics , programming language , biochemistry , chemistry , classical mechanics , nuclear magnetic resonance , messenger rna , gene

Large-scale conformational changes in proteins are implicated in many important biological functions. These structural transitions can often be rationalized in terms of relative movements of rigid domains. There is a need for objective and automated methods that identify rigid domains in sets of protein structures showing alternative conformational states.

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