{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T19:42:54Z","timestamp":1777923774452,"version":"3.51.4"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"suppl_2","license":[{"start":{"date-parts":[[2016,11,14]],"date-time":"2016-11-14T00:00:00Z","timestamp":1479081600000},"content-version":"vor","delay-in-days":2002,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,7,1]]},"DOI":"10.1093\/nar\/gkr284","type":"journal-article","created":{"date-parts":[[2011,5,25]],"date-time":"2011-05-25T00:30:30Z","timestamp":1306283430000},"page":"W385-W390","source":"Crossref","is-referenced-by-count":137,"title":["Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence"],"prefix":"10.1093","volume":"39","author":[{"given":"H. B.","family":"Rao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. B.","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Z. R.","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Y. Z.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2011,5,23]]},"reference":[{"key":"key\n\t\t\t\t20170720162144_B1","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1093\/bioinformatics\/18.1.147","article-title":"Classifying G-protein coupled receptors with support vector machines","volume":"18","author":"Karchin","year":"2002","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B2","doi-asserted-by":"crossref","first-page":"3692","DOI":"10.1093\/nar\/gkg600","article-title":"SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence","volume":"31","author":"Cai","year":"2003","journal-title":"Nucleic Acids Res."},{"key":"key\n\t\t\t\t20170720162144_B3","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1002\/(SICI)1097-0134(19990601)35:4<401::AID-PROT3>3.0.CO;2-K","article-title":"Recognition of a protein fold in the context of the Structural Classification of Proteins (SCOP) classification","volume":"35","author":"Dubchak","year":"1999","journal-title":"Proteins"},{"key":"key\n\t\t\t\t20170720162144_B4","doi-asserted-by":"crossref","first-page":"4023","DOI":"10.1002\/pmic.200500938","article-title":"Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity","volume":"6","author":"Han","year":"2006","journal-title":"Proteomics"},{"key":"key\n\t\t\t\t20170720162144_B5","doi-asserted-by":"crossref","first-page":"3149","DOI":"10.1093\/nar\/gkq061","article-title":"Boosting the prediction and understanding of DNA-binding domains from sequence","volume":"38","author":"Langlois","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"key\n\t\t\t\t20170720162144_B6","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1186\/1471-2105-10-416","article-title":"DescFold: a web server for protein fold recognition","volume":"10","author":"Yan","year":"2009","journal-title":"BMC Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B7","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1124\/jpet.108.149955","article-title":"What are next generation innovative therapeutic targets? Clues from genetic, structural, physicochemical, and systems profiles of successful targets","volume":"330","author":"Zhu","year":"2009","journal-title":"J. Pharmacol. Exp. Ther."},{"key":"key\n\t\t\t\t20170720162144_B8","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1093\/bioinformatics\/17.5.455","article-title":"Predicting protein\u2013protein interactions from primary structure","volume":"17","author":"Bock","year":"2001","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B9","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1002\/pmic.200401118","article-title":"Effect of training datasets on support vector machine prediction of protein-protein interactions","volume":"5","author":"Lo","year":"2005","journal-title":"Proteomics"},{"key":"key\n\t\t\t\t20170720162144_B10","doi-asserted-by":"crossref","first-page":"e1000054","DOI":"10.1371\/journal.pcbi.1000054","article-title":"Predicting co-complexed protein pairs from heterogeneous data","volume":"4","author":"Qiu","year":"2008","journal-title":"PLoS Comput. Biol."},{"key":"key\n\t\t\t\t20170720162144_B11","doi-asserted-by":"crossref","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","article-title":"Prediction of drug-target interaction networks from the integration of chemical and genomic spaces","volume":"24","author":"Yamanishi","year":"2008","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B12","doi-asserted-by":"crossref","first-page":"S6","DOI":"10.1186\/1752-0509-4-S2-S6","article-title":"Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces","volume":"4","author":"Xia","year":"2010","journal-title":"BMC Syst. Biol."},{"key":"key\n\t\t\t\t20170720162144_B13","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1093\/bioinformatics\/btq267","article-title":"Prediction of protease substrates using sequence and structure features","volume":"26","author":"Barkan","year":"2010","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B14","doi-asserted-by":"crossref","first-page":"e1000636","DOI":"10.1371\/journal.pcbi.1000636","article-title":"Combining structure and sequence information allows automated prediction of substrate specificities within enzyme families","volume":"6","author":"Rottig","year":"2010","journal-title":"PLoS Comput. Biol."},{"key":"key\n\t\t\t\t20170720162144_B15","doi-asserted-by":"crossref","first-page":"W243","DOI":"10.1093\/nar\/gkl298","article-title":"BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences","volume":"34","author":"Wang","year":"2006","journal-title":"Nucleic Acids Res."},{"key":"key\n\t\t\t\t20170720162144_B16","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1261\/rna.2197306","article-title":"Prediction of RNA binding sites in proteins from amino acid sequence","volume":"12","author":"Terribilini","year":"2006","journal-title":"RNA"},{"key":"key\n\t\t\t\t20170720162144_B17","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1093\/bioinformatics\/btq253","article-title":"Prediction of protein-RNA binding sites by a random forest method with combined features","volume":"26","author":"Liu","year":"2010","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B18","doi-asserted-by":"crossref","first-page":"W431","DOI":"10.1093\/nar\/gkq361","article-title":"NAPS: a residue-level nucleic acid-binding prediction server","volume":"38","author":"Carson","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"key\n\t\t\t\t20170720162144_B19","doi-asserted-by":"crossref","first-page":"W412","DOI":"10.1093\/nar\/gkq474","article-title":"PiRaNhA: a server for the computational prediction of RNA-binding residues in protein sequences","volume":"38","author":"Murakami","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"key\n\t\t\t\t20170720162144_B20","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1093\/bioinformatics\/btn538","article-title":"Protease substrate site predictors derived from machine learning on multilevel substrate phage display data","volume":"24","author":"Chen","year":"2008","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B21","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/978-1-60327-412-8_17","article-title":"Bioinformatics predictions of localization and targeting","volume":"619","author":"Rastogi","year":"2010","journal-title":"Methods Mol. Biol."},{"key":"key\n\t\t\t\t20170720162144_B22","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1093\/bioinformatics\/btn055","article-title":"ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction","volume":"24","author":"Overton","year":"2008","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B23","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/1472-6807-9-50","article-title":"CRYSTALP2: sequence-based protein crystallization propensity prediction","volume":"9","author":"Kurgan","year":"2009","journal-title":"BMC Struct. Biol."},{"key":"key\n\t\t\t\t20170720162144_B24","doi-asserted-by":"crossref","first-page":"423","DOI":"10.2174\/092986610790963726","article-title":"SVMCRYS: an SVM approach for the prediction of protein crystallization propensity from protein sequence","volume":"17","author":"Kandaswamy","year":"2010","journal-title":"Protein Pept. Lett."},{"key":"key\n\t\t\t\t20170720162144_B25","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0006-3495(94)80782-9","article-title":"The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: de novo design of an idealized leader peptidase cleavage site","volume":"66","author":"Schneider","year":"1994","journal-title":"Biophys J."},{"key":"key\n\t\t\t\t20170720162144_B26","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/j.molimm.2006.04.001","article-title":"Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties","volume":"44","author":"Cui","year":"2007","journal-title":"Mol. Immunol."},{"key":"key\n\t\t\t\t20170720162144_B27","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.1021\/jm8015365","article-title":"Identification of novel antibacterial peptides by chemoinformatics and machine learning","volume":"52","author":"Fjell","year":"2009","journal-title":"J. Med. Chem."},{"key":"key\n\t\t\t\t20170720162144_B28","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1093\/bioinformatics\/btn011","article-title":"Incorporating sequence information into the scoring function: a hidden Markov model for improved peptide identification","volume":"24","author":"Khatun","year":"2008","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B29","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1093\/bioinformatics\/btq245","article-title":"Machine learning based prediction for peptide drift times in ion mobility spectrometry","volume":"26","author":"Shah","year":"2010","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B30","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1093\/bioinformatics\/btm611","article-title":"Efficient peptide-MHC-I binding prediction for alleles with few known binders","volume":"24","author":"Jacob","year":"2008","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t20170720162144_B31","doi-asserted-by":"crossref","first-page":"W32","DOI":"10.1093\/nar\/gkl305","article-title":"PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence","volume":"34","author":"Li","year":"2006","journal-title":"Nucleic Acids Res."},{"key":"key\n\t\t\t\t20170720162144_B32","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.ab.2007.10.012","article-title":"PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition","volume":"373","author":"Shen","year":"2008","journal-title":"Anal. Biochem."},{"key":"key\n\t\t\t\t20170720162144_B33","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1021\/ci020382n","article-title":"Atomic-level-based AI topological descriptors for structure-property correlations","volume":"43","author":"Ren","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"key\n\t\t\t\t20170720162144_B34","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1002\/prot.21349","article-title":"Amino acid sequence autocorrelation vectors and Bayesian-regularized genetic neural networks for modeling protein conformational stability: gene V protein mutants","volume":"67","author":"Fernandez","year":"2007","journal-title":"Proteins"},{"key":"key\n\t\t\t\t20170720162144_B35","doi-asserted-by":"crossref","first-page":"2158","DOI":"10.1021\/ci050528t","article-title":"Elucidation of characteristic structural features of ligand binding sites of protein kinases: a neural network approach","volume":"46","author":"Niwa","year":"2006","journal-title":"J. Chem. Inf. Model"},{"key":"key\n\t\t\t\t20170720162144_B36","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s11030-009-9116-1","article-title":"Prediction of interaction between small molecule and enzyme using AdaBoost","volume":"13","author":"Niu","year":"2009","journal-title":"Mol. Divers"},{"key":"key\n\t\t\t\t20170720162144_B37","volume-title":"Handbook of Molecular Descriptors","author":"Todeschini","year":"2000"},{"key":"key\n\t\t\t\t20170720162144_B38","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1016\/j.bbapap.2006.07.005","article-title":"Influence of amino acid properties for discriminating outer membrane proteins at better accuracy","volume":"1764","author":"Gromiha","year":"2006","journal-title":"Biochim. Biophys. Acta"},{"key":"key\n\t\t\t\t20170720162144_B39","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1002\/jcc.20925","article-title":"Analysis and prediction of protein folding rates using quadratic response surface models","volume":"29","author":"Huang","year":"2008","journal-title":"J. Comput. Chem."},{"key":"key\n\t\t\t\t20170720162144_B40","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1021\/ci0340308","article-title":"Importance of native-state topology for determining the folding rate of two-state proteins","volume":"43","author":"Gromiha","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."}],"container-title":["Nucleic Acids Research"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/nar\/article-pdf\/39\/suppl_2\/W385\/18782589\/gkr284.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T14:42:17Z","timestamp":1522680137000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/nar\/article-lookup\/doi\/10.1093\/nar\/gkr284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,5,23]]},"references-count":40,"journal-issue":{"issue":"suppl_2","published-online":{"date-parts":[[2011,5,23]]},"published-print":{"date-parts":[[2011,7,1]]}},"URL":"https:\/\/doi.org\/10.1093\/nar\/gkr284","relation":{},"ISSN":["0305-1048","1362-4962"],"issn-type":[{"value":"0305-1048","type":"print"},{"value":"1362-4962","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,5,23]]}}}