Dataset Information

Prediction of hot spots in protein interfaces using extreme learning machines with the information of spatial neighbour residues.

ABSTRACT: The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming increasingly important for the research on protein-protein interaction and drug design. For each interface residue or target residue to be predicted, the authors extract hybrid features which incorporate a wide range of information of the target residue and its spatial neighbor residues, that is, the nearest contact residue in the other face (mirror-contact residue) and the nearest contact residue in the same face (intra-contact residue). Here, feature selection is performed using random forests to avoid over-fitting. Thereafter, the extreme learning machine is employed to effectively integrate these hybrid features for predicting hot spots in protein interfaces. By the 5-fold cross validation in the training set, their method can achieve accuracy (ACC) of 82.1% and Matthew's correlation coefficient (MCC) of 0.459, and outperforms some alternative machine learning methods in the comparison study. Furthermore, their method achieves ACC of 76.8% and MCC of 0.401 in the independent test set, and is more effective than the major existing hot spot predictors. Their prediction method offers a powerful tool for uncovering candidate residues in the studies of alanine scanning mutagenesis for functional protein interaction sites.

SUBMITTER: Wang L

PROVIDER: S-EPMC8687429 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Prediction of hot spots in protein interfaces using extreme learning machines with the information of spatial neighbour residues.

Wang Lin L Zhang Wenjuan W Gao Qiang Q Xiong Congcong C

IET systems biology 20140801 4

The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming increasingly important for the research on protein-protein interaction and drug design. For each interface residue or target residue to be predicted, the authors extract hybrid features which incorporate a wide range of information of the target residue and its spatial neighbor residues, that is, the nearest contact residue in the other face (mirror-contact res ...[more]

PMID: 25075532

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Project description:BACKGROUND: It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required. RESULTS: In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods. CONCLUSION: We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. The data and source code are available on web site http://home.ustc.edu.cn/~jfxia/hotspot.html.

Dataset Information

Prediction of hot spots in protein interfaces using extreme learning machines with the information of spatial neighbour residues.

Publications

Prediction of hot spots in protein interfaces using extreme learning machines with the information of spatial neighbour residues.

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