Protein-protein interactions is essential for cellular processes in all organisms. The accurate in-silico identification of these interactions is a significant area of research in biology-related fields, which is crucial for protein function prediction and drug design. Protein sequence data serves as the primary source for computational protein prediction. However, existing models for predicting protein-protein interactions based on sequence information typically consider only a limited set of physicochemical properties of amino acids. Consequently, they fail to comprehensively characterize protein sequence information, resulting in models that perform well within the species for which they were trained but poorly in cross-species environments. Unlike previous models, this paper combines the SVHEHS descriptor with various feature coding techniques to characterize protein sequences more comprehensively. The model employs explicit integration of bidirectional gated recurrent units to fuse multi-information. The final model achieves prediction accuracies of 96.47% and 97.79% on the H. pylori and S. cerevisiae datasets, respectively, outperforming most current models reported in the literature. In particular, the experimental results indicate that the model exhibits strong generalizability across various species datasets, suggesting it can serve as a valuable reference for investigating protein interaction networks in different species.

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Journal Article

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Lan Q, Zheng Z, Tang Z, Qiu X, Yin Z

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