Abstract
The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional 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 spots in protein interfaces.
Highlights
Protein-protein interactions (PPIs) play a critical role in most aspects of cellular functions like DNA replication and signal transduction [1, 2]
We proposed a novel hot spot prediction method, HEP, which based on our previous APIS and KFC2 approaches
The physicochemical features consist of a total of 17 attributes, where pseudo hydrophobicity (PSHP) is a novel feature for hot spot identification
Summary
Protein-protein interactions (PPIs) play a critical role in most aspects of cellular functions like DNA replication and signal transduction [1, 2]. Studies of principles governing PPIs have revealed that most of the binding free energy in an interaction is contributed by a small fraction of interface residues known as hot spots [3]. Porta-Pardo et al [4] have explored the role of missense mutations on PPI interfaces as cancer driver mutations in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas. Their analysis identified PPI interfaces enriched in somatic mutations in a total of 103 genes, proving that alteration of interaction interfaces is a common pathogenic mechanism of cancer mutations
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