Interaction between proteins often depends on the sequence features and structure features of proteins. Both of these features are helpful for machine learning methods to predict (protein–protein interaction) PPI sites. In this study, we introduced a new structure feature: concave–convex feature on the protein surface, which was computed by the structural data of proteins in Protein Data Bank database. And then, a prediction model combining protein sequence features and structure features was constructed, named SSPPI_Ensemble (Sequence and Structure geometric feature-based PPI site prediction). Three sequence features, i.e., PSSMs (Position-Specific Scoring Matrices), HMM (Hidden Markov Models) and raw protein sequence, were used. The Dictionary of Secondary Structure in Proteins and the concave–convex feature were used as the structure feature. Compared with the other prediction methods, our method has achieved better performance or showed the obvious advantages on the same test datasets, confirming the proposed concave–convex feature is useful in predicting PPI sites.
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