Journal
BMC SYSTEMS BIOLOGY
Volume 12, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s12918-018-0665-8
Keywords
Hot spot residues; Protein-protein interaction; Ensemble learning
Categories
Funding
- National Natural Science Foundation of China [61472282, 61872004, 61672035]
- Anhui Province Funds for Excellent Youth Scholars in Colleges [gxyqZD2016068]
- Anhui Scientific Research Foundation for Returned Scholars
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BackgroundHot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features.ResultsThis paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance.ConclusionThe experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction.Availabilityhttp://deeplearner.ahu.edu.cn/web/HotspotEL.htm.
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