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SPA: Short peptide analyzer of intrinsic disorder status of short peptides
Author(s) -
Xue Bin,
Hsu WeiLun,
Lee JunHo,
Lu Hua,
Dunker A. Keith,
Uversky Vladimir N.
Publication year - 2010
Publication title -
genes to cells
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.912
H-Index - 115
eISSN - 1365-2443
pISSN - 1356-9597
DOI - 10.1111/j.1365-2443.2010.01407.x
Subject(s) - process (computing) , sequence (biology) , biology , spectrum analyzer , sampling (signal processing) , mean squared prediction error , data mining , computer science , artificial intelligence , computational biology , statistics , machine learning , pattern recognition (psychology) , mathematics , biochemistry , telecommunications , filter (signal processing) , computer vision , operating system
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.