RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Motivation: Recent studies have found many proteins containing regions that do not form well-defined three-dimensional structures in their native states. The study and detection of such disordered regions is important both for understanding protein function and for facilitating structural analysis since disordered regions may affect solubility and/or crystallizability. Results: We have developed the regional order neural network (RONN) software as an application of our recently developed ‘bio-basis function neural network’ pattern recognition algorithm to the detection of natively disordered regions in proteins. The results of blind-testing a panel of nine disorder prediction tools (including RONN) against 80 protein sequences derived from the Protein Data Bank shows that, based on the probability excess measure, RONN performed the best. Availability: RONN is available at Requests for the RONN software and the database of disorder (XML format) can be directed to the corresponding author.

References in zbMATH (referenced in 7 articles )

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  1. He, Hao; Zhao, Jiaxiang; Sun, Guiling: The prediction of intrinsically disordered proteins based on feature selection (2019)
  2. Małysiak-Mrozek, Bożena: Uncertainty, imprecision, and many-valued logics in protein bioinformatics (2019)
  3. He, Hao; Zhao, Jiaxiang: A low computational complexity scheme for the prediction of intrinsically disordered protein regions (2018)
  4. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  5. Wills, Peter R.: Frameshifted prion proteins as pathological agents: quantitative considerations (2013)
  6. Aygün, E.; Oommen, B. J.; Cataltepe, Z.: Peptide classification using optimal and information theoretic syntactic modeling (2010) ioport
  7. Caragea, Cornelia; Sinapov, Jivko; Silvescu, Adrian; Dobbs, Drena; Honavar, Vasant: Glycosylation site prediction using ensembles of support vector machine classifiers (2007) ioport