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Allosteric mechanism of the circadian protein Vivid resolved through Markov state model and machine learning analysis.


ABSTRACT: The fungal circadian clock photoreceptor Vivid (VVD) contains a photosensitive allosteric light, oxygen, voltage (LOV) domain that undergoes a large N-terminal conformational change. The mechanism by which a blue-light driven covalent bond formation leads to a global conformational change remains unclear, which hinders the further development of VVD as an optogenetic tool. We answered this question through a novel computational platform integrating Markov state models, machine learning methods, and newly developed community analysis algorithms. Applying this new integrative approach, we provided a quantitative evaluation of the contribution from the covalent bond to the protein global conformational change, and proposed an atomistic allosteric mechanism leading to the discovery of the unexpected importance of A'?/A? and previously overlooked E?/F? loops in the conformational change. This approach could be applicable to other allosteric proteins in general to provide interpretable atomistic representations of their otherwise elusive allosteric mechanisms.

SUBMITTER: Zhou H 

PROVIDER: S-EPMC6396943 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Allosteric mechanism of the circadian protein Vivid resolved through Markov state model and machine learning analysis.

Zhou Hongyu H   Dong Zheng Z   Verkhivker Gennady G   Zoltowski Brian D BD   Tao Peng P  

PLoS computational biology 20190219 2


The fungal circadian clock photoreceptor Vivid (VVD) contains a photosensitive allosteric light, oxygen, voltage (LOV) domain that undergoes a large N-terminal conformational change. The mechanism by which a blue-light driven covalent bond formation leads to a global conformational change remains unclear, which hinders the further development of VVD as an optogenetic tool. We answered this question through a novel computational platform integrating Markov state models, machine learning methods,  ...[more]

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