SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

Background: Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results: SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predic...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Lukasz A. Kurgan, Krzysztof J. Cios, Ke Chen 0003
• Keywords: BMCBI 2008, Prediction, Protein, Structural