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美国卫生研究院文献>BMC Bioinformatics
>Prediction of beta-turns at over 80 accuracy based on an ensemble of predicted secondary structures and multiple alignments
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Prediction of beta-turns at over 80 accuracy based on an ensemble of predicted secondary structures and multiple alignments
Backgroundβ-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of β-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based β-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor.
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