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 predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier’s input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion: The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED’s predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Hayat, Maqsood; Tahir, Muhammad; Khan, Sher Afzal: Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces (2014)
- Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra: Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition (2014)
- Liu, Tian; Jia, Cangzhi: A high-accuracy protein structural class prediction algorithm using predicted secondary structural information (2010)
- Yang, Jian-Yi; Peng, Zhen-Ling; Chen, Xin: Prediction of protein structural classes for low-homology sequences based on predicted secondary structure (2010) ioport
- Huang, Ri-Bo; Du, Qi-Shi; Wei, Yu-Tuo; Pang, Zong-Wen; Wei, Hang; Chou, Kuo-Chen: Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design (2009)
- Mizianty, Marcin J.; Kurgan, Lukasz A.: Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences (2009) ioport