{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T01:45:18Z","timestamp":1782870318300,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T00:00:00Z","timestamp":1561593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771262"],"award-info":[{"award-number":["61771262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Molecular recognition features (MoRFs) are one important type of intrinsically disordered proteins functional regions that can undergo a disorder-to-order transition through binding to their interaction partners. Prediction of MoRFs is crucial, as the functions of MoRFs are associated with many diseases and can therefore become the potential drug targets. In this paper, a method of predicting MoRFs is developed based on the sequence properties and evolutionary information. To this end, we design two distinct multi-layer perceptron (MLP) neural networks and present a procedure to train them. We develop a preprocessing process which exploits different sizes of sliding windows to capture various properties related to MoRFs. We then use the Bayes rule together with the outputs of two trained MLP neural networks to predict MoRFs. In comparison to several state-of-the-art methods, the simulation results show that our method is competitive.<\/jats:p>","DOI":"10.3390\/e21070635","type":"journal-article","created":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T11:26:18Z","timestamp":1561634778000},"page":"635","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Prediction of MoRFs in Protein Sequences with MLPs Based on Sequence Properties and Evolution Information"],"prefix":"10.3390","volume":"21","author":[{"given":"Hao","family":"He","sequence":"first","affiliation":[{"name":"College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2287-0811","authenticated-orcid":false,"given":"Jiaxiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiling","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1111\/febs.13202","article-title":"Functional roles of transiently and intrinsically disordered regions within proteins","volume":"282","author":"Uversky","year":"2015","journal-title":"FEBS J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1016\/j.jmb.2006.07.087","article-title":"Analysis of molecular recognition features (MoRFs)","volume":"362","author":"Mohan","year":"2006","journal-title":"J. Mol. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1042\/BJ20130545","article-title":"Promiscuity as a functional trait: Intrinsically disordered regions as central players of interactomes","volume":"454","author":"Cumberworth","year":"2013","journal-title":"Biochem. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1021\/pr0701411","article-title":"Characterization of molecular recognition features, MoRFs, and their binding partners","volume":"6","author":"Vacic","year":"2007","journal-title":"J. Proteome Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12454","DOI":"10.1021\/bi050736e","article-title":"Coupled folding and binding with alpha-helix-forming molecular recognition elements","volume":"44","author":"Oldfield","year":"2005","journal-title":"Biochemistry"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.sbi.2011.03.011","article-title":"Intrinsically disordered proteins: Regulation and disease","volume":"21","author":"Babu","year":"2011","journal-title":"Curr. Opin. Struc. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13468","DOI":"10.1021\/bi7012273","article-title":"Mining alpha-helix-forming molecular recognition features with cross species sequence alignments","volume":"46","author":"Cheng","year":"2007","journal-title":"Biochemistry"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2745","DOI":"10.1093\/bioinformatics\/btp518","article-title":"ANCHOR: Web server for predicting protein binding regions in disordered proteins","volume":"25","author":"Dosztanyi","year":"2009","journal-title":"Bioinformatics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"I75","DOI":"10.1093\/bioinformatics\/bts209","article-title":"MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins","volume":"28","author":"Disfani","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fang, C., Noguchi, T., Tominaga, D., and Yamana, H. (2013). MFSPSSMpred: Identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation. BMC Bioinform.","DOI":"10.1186\/1471-2105-14-300"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1093\/bioinformatics\/btu744","article-title":"DISOPRED3: Precise disordered region predictions with annotated protein-binding activity","volume":"31","author":"Jones","year":"2015","journal-title":"Bioinformatics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1093\/bioinformatics\/btv060","article-title":"Computational identification of MoRFs in protein sequences","volume":"31","author":"Malhis","year":"2015","journal-title":"Bioinformatics"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Meszaros, B., Simon, I., and Dosztanyi, Z. (2009). Prediction of Protein Binding Regions in Disordered Proteins. PLoS Comput. Biol., 5.","DOI":"10.1371\/journal.pcbi.1000376"},{"key":"ref_14","first-page":"A1326","article-title":"Gapped BLAST and PSI-BLAST: A new generation of protein database search programs","volume":"12","author":"Altschul","year":"1998","journal-title":"FASEB J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"D202","DOI":"10.1093\/nar\/gkm998","article-title":"AAindex: Amino acid index database, progress report 2008","volume":"36","author":"Kawashima","year":"2008","journal-title":"Nucleic. Acids Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1002\/prot.22193","article-title":"Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network","volume":"74","author":"Faraggi","year":"2009","journal-title":"Proteins"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1093\/bioinformatics\/btl032","article-title":"PROFbval: Predict flexible and rigid residues in proteins","volume":"22","author":"Schlessinger","year":"2006","journal-title":"Bioinformatics"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3433","DOI":"10.1093\/bioinformatics\/bti541","article-title":"IUPred: Web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content","volume":"21","author":"Dosztanyi","year":"2005","journal-title":"Bioinformatics"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1093\/bioinformatics\/bth195","article-title":"The DISOPRED server for the prediction of protein disorder","volume":"20","author":"Ward","year":"2004","journal-title":"Bioinformatics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1093\/bioinformatics\/btn326","article-title":"Intrinsic disorder prediction from the analysis of multiple protein fold recognition models","volume":"24","author":"McGuffin","year":"2008","journal-title":"Bioinformatics"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"i489","DOI":"10.1093\/bioinformatics\/btq373","article-title":"Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources","volume":"26","author":"Mizianty","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Malhis, N., Wong, E.T.C., Nassar, R., and Gsponer, J. (2015). Computational identification of MoRFs in protein sequences using hierarchical application of bayers rule. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0141603"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1093\/nar\/gkw409","article-title":"MoRFchibi system: Software tools for the identification of MoRFs in protein sequences","volume":"44","author":"Malhis","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.1093\/bioinformatics\/bty032","article-title":"OPAL: Prediction of MoRF regions in intrinsically disordered protein sequences","volume":"34","author":"Sharma","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"D301","DOI":"10.1093\/nar\/gkl971","article-title":"The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data","volume":"35","author":"Berman","year":"2007","journal-title":"Nucleic. Acids Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, H., and Zhao, J.X. (2018). A Low Computational Complexity Scheme for the Prediction of Intrinsically Disordered Protein Regions. Math. Probl. Eng.","DOI":"10.1155\/2018\/8087391"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3701","DOI":"10.1093\/nar\/gkg519","article-title":"GlobPlot: Exploring protein sequences for globularity and disorder","volume":"31","author":"Linding","year":"2003","journal-title":"Nucleic. Acids Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, H., Zhao, J.X., and Sun, G.L. (2019). The Prediction of Intrinsically Disordered Proteins Based on Feature Selection. Algorithms, 12.","DOI":"10.3390\/a12020046"},{"key":"ref_29","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J.L. (2014). Adam: A Method for Stochastic Optimization. arXiv."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/7\/635\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:01:43Z","timestamp":1760187703000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/7\/635"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,27]]},"references-count":30,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["e21070635"],"URL":"https:\/\/doi.org\/10.3390\/e21070635","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,27]]}}}