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Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines

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 incre...

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Detalles Bibliográficos
Autores principales: Vullo, Alessandro, Bortolami, Oscar, Pollastri, Gianluca, Tosatto, Silvio C. E.
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1538873/
https://www.ncbi.nlm.nih.gov/pubmed/16844983
http://dx.doi.org/10.1093/nar/gkl166
Descripción
Sumario: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 and .