Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

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dc.citation.articleNumbere2410662121en_US
dc.citation.issueNumber35en_US
dc.citation.journalTitleProceedings of the National Academy of Sciencesen_US
dc.citation.volumeNumber121en_US
dc.contributor.authorGuan, Xingyueen_US
dc.contributor.authorTang, Qian-Yuanen_US
dc.contributor.authorRen, Weitongen_US
dc.contributor.authorChen, Mingchenen_US
dc.contributor.authorWang, Weien_US
dc.contributor.authorWolynes, Peter G.en_US
dc.contributor.authorLi, Wenfeien_US
dc.contributor.orgCenter for Theoretical Biological Physicsen_US
dc.date.accessioned2024-09-10T19:29:03Zen_US
dc.date.available2024-09-10T19:29:03Zen_US
dc.date.issued2024en_US
dc.description.abstractProteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.en_US
dc.identifier.citationGuan, X., Tang, Q.-Y., Ren, W., Chen, M., Wang, W., Wolynes, P. G., & Li, W. (2024). Predicting protein conformational motions using energetic frustration analysis and AlphaFold2. Proceedings of the National Academy of Sciences, 121(35), e2410662121. https://doi.org/10.1073/pnas.2410662121en_US
dc.identifier.digitalguan-et-al-2024en_US
dc.identifier.doihttps://doi.org/10.1073/pnas.2410662121en_US
dc.identifier.urihttps://hdl.handle.net/1911/117864en_US
dc.language.isoengen_US
dc.publisherNational Academy of Sciencesen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) 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-nd/4.0/en_US
dc.titlePredicting protein conformational motions using energetic frustration analysis and AlphaFold2en_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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