{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T20:01:30Z","timestamp":1760731290919,"version":"3.37.3"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"S15","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average <jats:italic>I<\/jats:italic><jats:sub><jats:italic>rmsd<\/jats:italic><\/jats:sub> value of 3.28\u00c5 and overall <jats:italic>F<\/jats:italic><jats:sub><jats:italic>nat<\/jats:italic><\/jats:sub> value of 63<jats:italic>%<\/jats:italic>, which improves upon <jats:italic>I<\/jats:italic><jats:sub><jats:italic>rmsd<\/jats:italic><\/jats:sub> of 3.89\u00c5 and <jats:italic>F<\/jats:italic><jats:sub><jats:italic>nat<\/jats:italic><\/jats:sub> of 49<jats:italic>%<\/jats:italic> for ZRANK, and <jats:italic>I<\/jats:italic><jats:sub><jats:italic>rmsd<\/jats:italic><\/jats:sub> of 3.99\u00c5 and <jats:italic>F<\/jats:italic><jats:sub><jats:italic>nat<\/jats:italic><\/jats:sub> of 46<jats:italic>%<\/jats:italic> for ClusPro. On CAPRI targets, our method achieves average <jats:italic>I<\/jats:italic><jats:sub><jats:italic>rmsd<\/jats:italic><\/jats:sub> value of 3.45\u00c5 and overall <jats:italic>F<\/jats:italic><jats:sub><jats:italic>nat<\/jats:italic><\/jats:sub> value of 46<jats:italic>%<\/jats:italic>, which improves upon <jats:italic>I<\/jats:italic><jats:sub><jats:italic>rmsd<\/jats:italic><\/jats:sub> of 4.18\u00c5 and <jats:italic>F<\/jats:italic><jats:sub><jats:italic>nat<\/jats:italic><\/jats:sub> of 40<jats:italic>%<\/jats:italic> for ZRANK, and <jats:italic>I<\/jats:italic><jats:sub><jats:italic>rmsd<\/jats:italic><\/jats:sub> of 5.12\u00c5 and <jats:italic>F<\/jats:italic><jats:sub><jats:italic>nat<\/jats:italic><\/jats:sub> of 32<jats:italic>%<\/jats:italic> for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5<jats:italic>%<\/jats:italic>, 29.0<jats:italic>%<\/jats:italic>, 29.4<jats:italic>%<\/jats:italic> and 37.0<jats:italic>%<\/jats:italic>, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-019-3048-2","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T09:02:35Z","timestamp":1577178155000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Identifying protein-protein interface via a novel multi-scale local sequence and structural representation"],"prefix":"10.1186","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8346-0798","authenticated-orcid":false,"given":"Fei","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jijun","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhai","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"issue":"17","key":"3048_CR1","doi-asserted-by":"publisher","first-page":"2203","DOI":"10.1093\/bioinformatics\/btm323","volume":"23","author":"H Zhou","year":"2007","unstructured":"Zhou H, Qin S. 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