NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

ABSTRACT:

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β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present

SUBMITTER: Petersen B 

PROVIDER: S-EPMC2994801 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature