{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T16:38:20Z","timestamp":1762101500237},"reference-count":67,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":1025,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Gaussian network model (GNM) is widely adopted to analyze and understand protein dynamics, function and conformational changes. The existing GNM-based approaches require atomic coordinates of the corresponding protein and cannot be used when only the sequence is known.<\/jats:p>\n               <jats:p>Results: We report, first of its kind, GNM model that allows modeling using the sequence. Our linear regression-based, parameter-free, sequence-derived GNM (L-pfSeqGNM) uses contact maps predicted from the sequence and models local, in the sequence, contact neighborhoods with the linear regression. Empirical benchmarking shows relatively high correlations between the native and the predicted with L-pfSeqGNM B-factors and between the cross-correlations of residue fluctuations derived from the structure- and the sequence-based GNM models. Our results demonstrate that L-pfSeqGNM is an attractive platform to explore protein dynamics. In contrast to the highly used GNMs that require protein structures that number in thousands, our model can be used to study motions for the millions of the readily available sequences, which finds applications in modeling conformational changes, protein\u2013protein interactions and protein functions.<\/jats:p>\n               <jats:p>Contact: \u00a0zerozhua@126.com<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt716","type":"journal-article","created":{"date-parts":[[2013,12,14]],"date-time":"2013-12-14T02:19:55Z","timestamp":1386987595000},"page":"497-505","source":"Crossref","is-referenced-by-count":7,"title":["Sequence-based Gaussian network model for protein dynamics"],"prefix":"10.1093","volume":"30","author":[{"given":"Hua","family":"Zhang","sequence":"first","affiliation":[{"name":"1 School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, P.R. China and 2Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lukasz","family":"Kurgan","sequence":"additional","affiliation":[{"name":"1 School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, P.R. China and 2Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,12,12]]},"reference":[{"key":"2023012710424276100_btt716-B1","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res."},{"key":"2023012710424276100_btt716-B2","doi-asserted-by":"crossref","first-page":"3490","DOI":"10.1021\/bm060584i","article-title":"Strong impact of ionic strength on the kinetics of fibrilar aggregation of bovine \u03b2-lactoglobulin","volume":"7","author":"Arnaudov","year":"2006","journal-title":"Biomacromolecules"},{"key":"2023012710424276100_btt716-B3","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/S0006-3495(01)76033-X","article-title":"Anisotropy of fluctuation dynamics of proteins with an elastic network model","volume":"80","author":"Atilgan","year":"2001","journal-title":"Biophys. 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