{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:14:38Z","timestamp":1758273278893,"version":"3.40.5"},"reference-count":49,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3400501"],"award-info":[{"award-number":["2022YFC3400501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Proteins"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS\u2010CoV\u20102) spike protein must undergo a crucial conformational transition to invade human cells. It is intriguing that this transition is accompanied by a synchronized movement of the entire spike protein. Therefore, it is possible to design allosteric regulators targeting non\u2010RBD but hindering the conformational transition of RBD. To understand the allosteric mechanism in detail, we establish a computational framework by integrating coarse\u2010grained molecular dynamic simulations and a state\u2010of\u2010the\u2010art neural network model called neural relational inference. Leveraging this framework, we have elucidated the allosteric pathway of the SARS\u2010CoV\u20102 spike protein at the residue level and identified the molecular mechanisms involved in the transmission of allosteric signals. The movement of D614 is coupled with that of Q321. This interaction subsequently influences the movement of K528\/K529, ultimately coupling with the movement of RBD during conformational changes. Mutations that weaken the interactions within this pathway naturally block the allosteric signal transmission, thereby modulating the conformational transitions. This observation also offers a rationale for the distinct allosteric patterns observed in the SARS\u2010CoV spike protein. Our result provides a useful method for analyzing the dynamics of potential viral variants in the future.<\/jats:p>","DOI":"10.1002\/prot.26678","type":"journal-article","created":{"date-parts":[[2024,3,9]],"date-time":"2024-03-09T04:12:00Z","timestamp":1709957520000},"page":"865-873","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Allosteric pathways of <scp>SARS<\/scp> and <scp>SARS\u2010CoV<\/scp>\u20102 spike protein identified by neural relational inference"],"prefix":"10.1002","volume":"92","author":[{"given":"Yao","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Physics University of Science and Technology of China  Hefei China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingwei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Physics University of Science and Technology of China  Hefei China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9908-4317","authenticated-orcid":false,"given":"Qian","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Physics University of Science and Technology of China  Hefei China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_9_2_1","DOI":"10.1016\/S0140-6736(20)30185-9"},{"doi-asserted-by":"publisher","key":"e_1_2_9_3_1","DOI":"10.1056\/NEJMoa2001017"},{"doi-asserted-by":"publisher","key":"e_1_2_9_4_1","DOI":"10.1080\/0739110220211894986"},{"doi-asserted-by":"publisher","key":"e_1_2_9_5_1","DOI":"10.1056\/NEJMoa2034577"},{"doi-asserted-by":"publisher","key":"e_1_2_9_6_1","DOI":"10.1056\/NEJMoa2035389"},{"doi-asserted-by":"publisher","key":"e_1_2_9_7_1","DOI":"10.1126\/science.abc6952"},{"doi-asserted-by":"publisher","key":"e_1_2_9_8_1","DOI":"10.1038\/s41586-020-2571-7"},{"doi-asserted-by":"publisher","key":"e_1_2_9_9_1","DOI":"10.1038\/s41586-020-2577-1"},{"doi-asserted-by":"publisher","key":"e_1_2_9_10_1","DOI":"10.1002\/minf.202000028"},{"doi-asserted-by":"publisher","key":"e_1_2_9_11_1","DOI":"10.1038\/s41579-021-00573-0"},{"doi-asserted-by":"publisher","key":"e_1_2_9_12_1","DOI":"10.1073\/pnas.2003138117"},{"doi-asserted-by":"publisher","key":"e_1_2_9_13_1","DOI":"10.1006\/viro.2000.0757"},{"doi-asserted-by":"publisher","key":"e_1_2_9_14_1","DOI":"10.1038\/s41586-020-2179-y"},{"doi-asserted-by":"publisher","key":"e_1_2_9_15_1","DOI":"10.1126\/science.abb2762"},{"doi-asserted-by":"publisher","key":"e_1_2_9_16_1","DOI":"10.1016\/j.celrep.2020.108630"},{"doi-asserted-by":"publisher","key":"e_1_2_9_17_1","DOI":"10.1021\/acs.jmedchem.1c00320"},{"doi-asserted-by":"publisher","key":"e_1_2_9_18_1","DOI":"10.1073\/pnas.2100943118"},{"doi-asserted-by":"publisher","key":"e_1_2_9_19_1","DOI":"10.1021\/jacs.1c00556"},{"doi-asserted-by":"publisher","key":"e_1_2_9_20_1","DOI":"10.1021\/acs.jpcb.0c10637"},{"doi-asserted-by":"publisher","key":"e_1_2_9_21_1","DOI":"10.1128\/JVI.00127-20"},{"doi-asserted-by":"publisher","key":"e_1_2_9_22_1","DOI":"10.1038\/s41586-020-2012-7"},{"doi-asserted-by":"publisher","key":"e_1_2_9_23_1","DOI":"10.1038\/s41401-020-0485-4"},{"key":"e_1_2_9_24_1","first-page":"2688","article-title":"Proceedings of the 35th International Conference on Machine Learning","volume":"80","author":"Kipf T","year":"2018","journal-title":"PMLR"},{"doi-asserted-by":"publisher","key":"e_1_2_9_25_1","DOI":"10.1006\/jmbi.2000.3693"},{"doi-asserted-by":"publisher","key":"e_1_2_9_26_1","DOI":"10.1021\/jp300852d"},{"doi-asserted-by":"publisher","key":"e_1_2_9_27_1","DOI":"10.1093\/nar\/gkq498"},{"doi-asserted-by":"publisher","key":"e_1_2_9_28_1","DOI":"10.1002\/jcc.20291"},{"doi-asserted-by":"publisher","key":"e_1_2_9_29_1","DOI":"10.1038\/s41467-022-29331-3"},{"key":"e_1_2_9_30_1","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky AEA","year":"2012","journal-title":"Adv Neural Inform Process Syst"},{"unstructured":"DiederikJB KingmaP.Adam: a method for stochastic optimization. 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