title: Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines.
abstract: Intrinsically disordered proteins have long stretches of their polypeptide chain, which do not adopt a single native structure composed of stable secondary and tertiary structure in the absence of binding partners. The prediction of intrinsically disordered regions in proteins from sequence is increasingly becoming of interest, as the presence of many such regions in the complete genome sequences are discovered and important functional roles are associated with them. We have developed a machine learning approach based on two support vector machines (SVM) to discriminate disordered regions from sequence. The SVM are trained and benchmarked on two sets, representing long and short disordered regions. A preliminary version of Spritz was shown to perform consistently well at the recent biannual CASP-6 experiment [Critical Assessment of Techniques for Protein Structure Prediction (CASP), 2004]. The fully developed Spritz method is freely available as a web server at http://distill.ucd.ie/spritz/ and http://protein.cribi.unipd.it/spritz/.
authors.affiliation: School of Computer Science and Informatics, University College Dublin, Ireland.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Vullo
authors.forename: Oscar
authors.initials: O
authors.lastname: Bortolami
authors.forename: Gianluca
authors.initials: G
authors.lastname: Pollastri
authors.forename: Silvio C E
authors.initials: SC
authors.lastname: Tosatto
mesh_terms: D001185:Artificial Intelligence; D020407:Internet; D011487:Protein Conformation; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D014584:User-Computer Interface