{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T05:46:00Z","timestamp":1772257560802,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"publisher","award":["2021\u201303979"],"award-info":[{"award-number":["2021\u201303979"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004063","name":"Knut and Alice Wallenberg Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004063","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004063","name":"Knut and Alice Wallenberg Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004063","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SNIC"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Understanding metal\u2013protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal-binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (pLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the pLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal binding.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall\u2009=\u200984.6%) in distinguishing metal binding from non-binding proteins compared to AUROC of 0.74 (recall = 61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca2+, Mg2+, Mn2+, Zn2+), M-Ionic provides binding probabilities for six additional ions (i.e. Cu2+, Po43\u2212, So42\u2212, Fe2+, Fe3+, Co2+). We show that the pLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>M-Ionic can be used on your protein of interest using a Google Colab Notebook (https:\/\/bit.ly\/40FrRbK). The GitHub repository (https:\/\/github.com\/TeamSundar\/m-ionic) contains all code and data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad782","type":"journal-article","created":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T13:01:32Z","timestamp":1704373292000},"source":"Crossref","is-referenced-by-count":24,"title":["M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7748-2501","authenticated-orcid":false,"given":"Aditi","family":"Shenoy","sequence":"first","affiliation":[{"name":"Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University , Solna 17121, Sweden"}]},{"given":"Yogesh","family":"Kalakoti","sequence":"additional","affiliation":[{"name":"Department of Biochemical Engineering & Biotechnology, Indian Institute of Technology (IIT) Delhi , New Delhi 110016, 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