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Therefore, knowledge of these interactions provides invaluable insights into all cellular processes, functional mechanisms, and drug discovery. Protein\u2013peptide interactions can be analyzed by studying the structures of protein\u2013peptide complexes. However, only a small portion has known complex structures and experimental determination of protein\u2013peptide interaction is costly and inefficient. Thus, predicting peptide-binding sites computationally will be useful to improve efficiency and cost effectiveness of experimental studies. Here, we established a machine learning method called SPRINT-Str (Structure-based prediction of protein\u2013Peptide Residue-level Interaction) to use structural information for predicting protein\u2013peptide binding residues. These predicted binding residues are then employed to infer the peptide-binding site by a clustering algorithm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>SPRINT-Str achieves robust and consistent results for prediction of protein\u2013peptide binding regions in terms of residues and sites. Matthews\u2019 Correlation Coefficient (MCC) for 10-fold cross validation and independent test set are 0.27 and 0.293, respectively, as well as 0.775 and 0.782, respectively for area under the curve. The prediction outperforms other state-of-the-art methods, including our previously developed sequence-based method. A further spatial neighbor clustering of predicted binding residues leads to prediction of binding sites at 20\u2013116% higher coverage than the next best method at all precision levels in the test set. The application of SPRINT-Str to protein binding with DNA, RNA and carbohydrate confirms the method\u2018s capability of separating peptide-binding sites from other functional sites. More importantly, similar performance in prediction of binding residues and sites is obtained when experimentally determined structures are replaced by unbound structures or quality model structures built from homologs, indicating its wide applicability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>http:\/\/sparks-lab.org\/server\/SPRINT-Str<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx614","type":"journal-article","created":{"date-parts":[[2017,9,25]],"date-time":"2017-09-25T11:09:52Z","timestamp":1506337792000},"page":"477-484","source":"Crossref","is-referenced-by-count":102,"title":["Structure-based prediction of protein\u2013 peptide binding regions using Random Forest"],"prefix":"10.1093","volume":"34","author":[{"given":"Ghazaleh","family":"Taherzadeh","sequence":"first","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaoqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia"},{"name":"Institute for Glycomics, Griffith University, Parklands Drive, Southport, QLD, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan Wee-Chung","family":"Liew","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6782-2813","authenticated-orcid":false,"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia"},{"name":"Institute for Glycomics, Griffith University, Parklands Drive, Southport, QLD, Australia"},{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,9,26]]},"reference":[{"key":"2023012712321503700_btx614-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":"2023012712321503700_btx614-B2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0006-3495(04)74086-2","article-title":"Small-world communication of residues and significance for protein dynamics","volume":"86","author":"Atilgan","year":"2004","journal-title":"Biophys. 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