{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T11:04:00Z","timestamp":1782126240799,"version":"3.54.5"},"reference-count":31,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T00:00:00Z","timestamp":1619395200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100010803","name":"Department of Biotechnology, Government of West Bengal","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010803","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Proteins"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Metal ions are central to the molecular function of many proteins. Thus their knowledge in experimentally determined structure is important; however, such structures often lose bound metal ions during sample preparation. Identification of these metal\u2010binding site(s) becomes difficult when the receptor is novel and\/or their conformations differ in the bound\/unbound states. Locating such sites in theoretical models also poses a challenge due to the uncertainties with side\u2010chain modeling. We address the problem by employing the Geometric Hashing algorithm to create a template library of functionally important binding sites and match query structures with the available templates. The matching is done on the structure ensemble obtained from coarse\u2010grained molecular dynamics simulation, where metal\u2010specific amino acids are screened to infer the true site. Test on 1347 non\u2010redundant monomer protein structures show that Ca\n                    <jats:sup>2+<\/jats:sup>\n                    , Zn\n                    <jats:sup>2+<\/jats:sup>\n                    , Mg\n                    <jats:sup>2+<\/jats:sup>\n                    , Cu\n                    <jats:sup>2+<\/jats:sup>\n                    , and Fe\n                    <jats:sup>3+<\/jats:sup>\n                    binding site residues can be classified at 0.92, 0.95, 0.80, 0.90, and 0.92 aggregate performance (out of 1) across all possible thresholds. The performance for Ca\n                    <jats:sup>2+<\/jats:sup>\n                    and Zn\n                    <jats:sup>2+<\/jats:sup>\n                    is notably superior in comparison to state\u2010of\u2010the\u2010art methods like IonCom and MIB. Specific case studies show that additionally predicted metal\u2010binding site residues in proteins have features necessary for ion binding. These include new sites not predicted by other methods. The use of coarse\u2010grained dynamics thus provides a generalized approach to improve metal\u2010binding site prediction. The work is expected to contribute to improving our ability to correctly predict protein molecular function where knowledge of metal binding is a key requirement.\n                  <\/jats:p>","DOI":"10.1002\/prot.26085","type":"journal-article","created":{"date-parts":[[2021,4,17]],"date-time":"2021-04-17T07:07:31Z","timestamp":1618643251000},"page":"1125-1133","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Inferring metal binding sites in flexible regions of proteins"],"prefix":"10.1002","volume":"89","author":[{"given":"Aditi","family":"Garg","sequence":"first","affiliation":[{"name":"Department of Computational and Data Sciences Indian Institute of Science  Bengaluru India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3591-5978","authenticated-orcid":false,"given":"Debnath","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of Computational and Data Sciences Indian Institute of Science  Bengaluru India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,4,26]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkx989"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.21587"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bth095"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1107\/S090744490706595X"},{"key":"e_1_2_10_6_1","first-page":"465","article-title":"Predicting the geometry of metal binding sites from protein sequence","volume":"21","author":"Frasconi P","year":"2008","journal-title":"Adv Neural Info Process 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