A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers
A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers
Study of interface residue pairs is important for understanding the interactions between monomers inside a trimer protein–protein complex. We developed a two-layer support vector machine (SVM) ensemble-classifier that considers physicochemical and geometric properties of amino acids and the influence of surrounding amino acids. Different descriptors and different combinations may give different prediction results. We propose feature combination engineering based on correlation coefficients and F-values. The accuracy of our method is 65.38% in independent test set, indicating biological significance. Our predictions are consistent with the experimental results. It shows the effectiveness and reliability of our method to predict interface residue pairs of protein trimers.
- Renmin University of China
- Tsinghua University China (People's Republic of)
- Renmin University of China China (People's Republic of)
Support Vector Machine, a two-layer SVM ensemble-classifier, feature combination engineering, Organic chemistry, Computational Biology, Proteins, Article, QD241-441, trimer protein–protein complexes, Protein Multimerization, Protein Structure, Quaternary
Support Vector Machine, a two-layer SVM ensemble-classifier, feature combination engineering, Organic chemistry, Computational Biology, Proteins, Article, QD241-441, trimer protein–protein complexes, Protein Multimerization, Protein Structure, Quaternary
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