{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:49:22Z","timestamp":1742971762070,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031077494"},{"type":"electronic","value":"9783031077500"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-07750-0_1","type":"book-chapter","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T14:23:03Z","timestamp":1654870983000},"page":"3-14","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hot Spots & Hot Regions Detection Using Classification Algorithms in\u00a0BMPs Complexes at\u00a0the\u00a0Protein-Protein Interface with\u00a0the\u00a0Ground-State Energy Feature"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3250-330X","authenticated-orcid":false,"given":"O.","family":"Chaparro-Amaro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4806-1390","authenticated-orcid":false,"given":"M.","family":"Mart\u00ednez-Felipe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2824-3544","authenticated-orcid":false,"given":"J.","family":"Mart\u00ednez-Castro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,11]]},"reference":[{"issue":"mb981843","key":"1_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1006\/jmbi.1998.1843","volume":"280","author":"AA Bogan","year":"1998","unstructured":"Bogan, A.A., Thorn, K.S.: Anatomy of hot spots in protein interfaces. J. Mol. Biol. 280(mb981843), 1\u20139 (1998)","journal-title":"J. Mol. Biol."},{"key":"1_CR2","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1093\/nar\/gkw408","volume":"44","author":"H Ashkenazy","year":"2016","unstructured":"Ashkenazy, H., et al.: ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, 344\u2013350 (2016). https:\/\/doi.org\/10.1093\/nar\/gkw408","journal-title":"Nucleic Acids Res."},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"D301","DOI":"10.1093\/nar\/gkl971","volume":"35","author":"H Berman","year":"2007","unstructured":"Berman, H., Henrick, K., Nakamura, H., Markley, J.: The worldwide protein data bank (wwPDB): ensuring a single, uniform archive of PDB data. Nucleic Acids Res. 35, D301\u2013D303 (2007)","journal-title":"Nucleic Acids Res."},{"issue":"6","key":"1_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0177678","volume":"12","author":"S Boughorbel","year":"2017","unstructured":"Boughorbel, S., Jarray, F., El-Anbari, M.: Optimal classifier for imbalanced data using Matthews correlation coefficient metric. PLoS ONE 12(6), 1\u201317 (2017). https:\/\/doi.org\/10.1371\/journal.pone.0177678","journal-title":"PLoS ONE"},{"issue":"61","key":"1_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-018-2083-8","volume":"19","author":"O Carugo","year":"2018","unstructured":"Carugo, O.: How large b-factors can be in protein crystal structures. BMC Bioinf. 19(61), 1\u20139 (2018). https:\/\/doi.org\/10.1186\/s12859-018-2083-8","journal-title":"BMC Bioinf."},{"issue":"4","key":"1_CR6","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1080\/08977190412331279890","volume":"22","author":"D Chen","year":"2004","unstructured":"Chen, D., Zhao, M., Mundy, G.R.: Bone morphogenetic proteins. Growth Factors 22(4), 233\u2013241 (2004)","journal-title":"Growth Factors"},{"issue":"22080558","key":"1_CR7","first-page":"829","volume":"40","author":"E Cukuroglu","year":"2011","unstructured":"Cukuroglu, E., Gursoy, A., Keskin, O.: HotRegion: a database of predicted hot spot clusters. Nucleic Acids Res. 40(22080558), 829\u2013833 (2011)","journal-title":"Nucleic Acids Res."},{"key":"1_CR8","volume-title":"Neural Networks and Learning Machines","author":"S Haykin","year":"2009","unstructured":"Haykin, S., Haykin, S.: Neural Networks and Learning Machines, vol. 10. Prentice Hall, New York (2009)"},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1002\/prot.25039","volume":"84","author":"BJ Hintze","year":"2016","unstructured":"Hintze, B.J., et al.: MolProbity ultimate rotamer-library distributions for model validation. Proteins Struct. Funct. Bioinf. 84, 1177\u20131189 (2016)","journal-title":"Proteins Struct. Funct. Bioinf."},{"issue":"22","key":"1_CR10","doi-asserted-by":"publisher","first-page":"14116","DOI":"10.1073\/pnas.202485799","volume":"99","author":"T Kortemme","year":"2002","unstructured":"Kortemme, T., Baker, D.: A simple physical model for binding energy hot spots in protein-protein complexes. PNAS 99(22), 14116\u201314121 (2002). https:\/\/doi.org\/10.1073\/pnas.202485799","journal-title":"PNAS"},{"key":"1_CR11","doi-asserted-by":"publisher","unstructured":"Kortemme, T., Kim, D.E., Baker, D.: Computational alanine scanning of protein-protein interfaces. Sci. STKE Protoc. 1\u20138 (2004). https:\/\/doi.org\/10.1126\/stke.2192004pl2","DOI":"10.1126\/stke.2192004pl2"},{"issue":"157","key":"1_CR12","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/0022-2836(82)90515-0","volume":"5","author":"J Kyte","year":"1982","unstructured":"Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 5(157), 105\u2013132 (1982). https:\/\/doi.org\/10.1016\/0022-2836(82)90515-0","journal-title":"J. Mol. Biol."},{"issue":"365","key":"1_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-10-365","volume":"10","author":"S Lise","year":"2009","unstructured":"Lise, S., et al.: Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. BMC Bioinf. 10(365), 1\u201317 (2009). https:\/\/doi.org\/10.1186\/1471-2105-10-365","journal-title":"BMC Bioinf."},{"issue":"10","key":"1_CR14","doi-asserted-by":"publisher","first-page":"2535","DOI":"10.3390\/molecules23102535","volume":"23","author":"S Liu","year":"2018","unstructured":"Liu, S., Liu, C., Deng, L.: Machine learning approaches for protein-protein interaction hot spot prediction: progress and comparative assessment. MDPI Mol. 23(10), 2535 (2018). https:\/\/doi.org\/10.3390\/molecules23102535","journal-title":"MDPI Mol."},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"McKerns, M.M., et al.: Building a framework for predictive science. In: Proceedings of the 10th Python in Science Conference, vol. 1, pp. 1\u201311 (2011). https:\/\/doi.org\/10.48550\/arXiv.1202.1056","DOI":"10.25080\/Majora-ebaa42b7-00d"},{"issue":"189","key":"1_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.12688\/f1000research.7931.1","volume":"5","author":"S Mitternacht","year":"2016","unstructured":"Mitternacht, S.: FreeSASA: an open source C library for solvent accessible surface area calculations. F1000 Res. 5(189), 1\u201310 (2016). https:\/\/doi.org\/10.12688\/f1000research.7931.1","journal-title":"F1000 Res."},{"key":"1_CR17","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.2174\/138161212799436412","volume":"18","author":"JK Morrow","year":"2012","unstructured":"Morrow, J.K., Zhang, S.: Computational prediction of hot spot residues. Curr. Pharm. Des. 18, 1255\u20131265 (2012). https:\/\/doi.org\/10.2174\/138161212799436412","journal-title":"Curr. Pharm. Des."},{"key":"1_CR18","unstructured":"Muller, R.: PyQuante2. PyQuante Sourceforge Project Page (2013). https:\/\/github.com\/rpmuller\/pyquante2"},{"issue":"20444871","key":"1_CR19","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1093\/nar\/gkq323","volume":"38","author":"N Tuncbag","year":"2010","unstructured":"Tuncbag, N., Keskin, O., Gursoy, A.: Hotpoint: hot spot prediction server for protein interfaces. Nucleic Acids Res. 38(20444871), 402\u2013406 (2010). https:\/\/doi.org\/10.1093\/nar\/gkq323","journal-title":"Nucleic Acids Res."},{"issue":"11","key":"1_CR20","doi-asserted-by":"publisher","first-page":"2993","DOI":"10.1109\/TBME.2011.2161306","volume":"60","author":"QT Nguyen","year":"2013","unstructured":"Nguyen, Q.T., Fablet, R., Pastor, D.: Protein interaction hotspot identification using sequence-based frequency-derived features. IEEE Trans. Biomed. Eng. 60(11), 2993\u20133002 (2013). https:\/\/doi.org\/10.1109\/TBME.2011.2161306","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1_CR21","doi-asserted-by":"publisher","DOI":"10.1201\/9781420070071","volume-title":"Computational Protein-Protein Interactions","author":"R Nussinov","year":"2009","unstructured":"Nussinov, R., Schreiber, G.: Computational Protein-Protein Interactions. CRC Press, Boca Raton (2009). https:\/\/doi.org\/10.1201\/9781420070071"},{"key":"1_CR22","unstructured":"NVIDIA, Vingelmann, P., Fitzek, F.H.: CUDA, release. Accessed 10 Feb 1989 (2020). https:\/\/developer.nvidia.com\/cuda-toolkit"},{"key":"1_CR23","unstructured":"PDBremix: Calculating the solvent accessible surface area (2014)"},{"issue":"19","key":"1_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-018-2009-5","volume":"14","author":"Y Qiao","year":"2018","unstructured":"Qiao, Y., et al.: Protein-protein interface hot spots prediction based on a hybrid feature selection strategy. BMC Bioinf. 14(19), 1\u201316 (2018). https:\/\/doi.org\/10.1186\/s12859-018-2009-5","journal-title":"BMC Bioinf."},{"issue":"79","key":"1_CR25","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/0022-2836(73)90011-9","volume":"2","author":"A Shrake","year":"1973","unstructured":"Shrake, A., Rupley, J.A.: Environment and exposure to solvent of protein atoms. lysozyme and insulin. J. Mol. Biol. 2(79), 351\u2013371 (1973). https:\/\/doi.org\/10.1016\/0022-2836(73)90011-9","journal-title":"J. Mol. Biol."},{"issue":"12","key":"1_CR26","doi-asserted-by":"publisher","first-page":"3335","DOI":"10.1002\/prot.24686","volume":"82","author":"F Stephen","year":"2014","unstructured":"Stephen, F., et al.: Density functional theory calculations on entire proteins for free energies of binding: application to a model polar binding site. Proteins Struct. Funct. Bioinf. 82(12), 3335\u20133346 (2014). https:\/\/doi.org\/10.1002\/prot.24686","journal-title":"Proteins Struct. Funct. Bioinf."},{"issue":"12","key":"1_CR27","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1093\/bioinformatics\/btp240","volume":"25","author":"N Tuncbag","year":"2009","unstructured":"Tuncbag, N., Gursoy, A., Keskin, O.: Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. J. Bioinf. 25(12), 1513\u20131520 (2009). https:\/\/doi.org\/10.1093\/bioinformatics\/btp240","journal-title":"J. Bioinf."},{"key":"1_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/978-3-030-91814-9_13","volume-title":"Advances in Bioinformatics and Computational Biology","author":"JPU Cavalcante","year":"2021","unstructured":"Cavalcante, J.P.U., Gon\u00e7alves, A.C., Bonidia, R.P., Sanches, D.S., de Carvalho, A.C.P.L.F.: MathPIP: classification of proinflammatory peptides using mathematical descriptors. In: Stadler, P.F., Walter, M.E.M.T., Hernandez-Rosales, M., Brigido, M.M. (eds.) BSB 2021. LNCS, vol. 13063, pp. 131\u2013136. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-91814-9_13"},{"issue":"3","key":"1_CR29","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1093\/protein\/gzr066","volume":"25","author":"L Wang","year":"2012","unstructured":"Wang, L., et al.: Prediction of hot spots in protein interfaces using a random forest model with hybrid features. Protein Eng. Des. Sel. 25(3), 119\u2013126 (2012). https:\/\/doi.org\/10.1093\/protein\/gzr066","journal-title":"Protein Eng. Des. Sel."},{"issue":"11","key":"1_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-11-174","volume":"174","author":"JF Xia","year":"2010","unstructured":"Xia, J.F., et al.: APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility. BMC Bioinf. 174(11), 1\u201314 (2010). https:\/\/doi.org\/10.1186\/1471-2105-11-174","journal-title":"BMC Bioinf."},{"issue":"1","key":"1_CR31","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s10930-007-9108-x","volume":"27","author":"C Yan","year":"2008","unstructured":"Yan, C., et al.: Characterization of protein-protein interfaces. Protein J. 27(1), 59\u201370 (2008). https:\/\/doi.org\/10.1007\/s10930-007-9108-x","journal-title":"Protein J."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-07750-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T14:24:16Z","timestamp":1654871056000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-07750-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031077494","9783031077500"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-07750-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ciudad Ju\u00e1rez","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mcpr22022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ccc.inaoep.mx\/~mcpr\/index.html#","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.75","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.66","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}