title: SPA: Short peptide analyzer of intrinsic disorder status of short peptides.
abstract: Disorder prediction for short peptides is important and difficult. All modern predictors have to be optimized on a preselected dataset prior to prediction. In the succeeding prediction process, the predictor works on a query sequence or its short segment. For implementing the prediction smoothly and obtaining sound prediction results, a specific length of the sequence or segment is usually required. The need of the preselected dataset in the optimization process and the length limitation in the prediction process restrict predictors' performance. To minimize the influence of these limitations, we developed a method for the prediction of intrinsic disorder in short peptides based on large dataset sampling and statistics. As evident from the data analysis, this method provides more reliable prediction of the intrinsic disorder status of short peptides.
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Xue
authors.forename: Wei-Lun
authors.initials: WL
authors.lastname: Hsu
authors.forename: Jun-Ho
authors.initials: JH
authors.lastname: Lee
authors.forename: Hua
authors.initials: H
authors.lastname: Lu
authors.forename: A Keith
authors.initials: AK
authors.lastname: Dunker
authors.forename: Vladimir N
authors.initials: VN
authors.lastname: Uversky
mesh_terms: D000595:Amino Acid Sequence; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D018360:Crystallography, X-Ray; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry* / Q000235:genetics; D011487:Protein Conformation; D017434:Protein Structure, Tertiary
chemical_list: D010455:Peptides