MPIDNN-GPPI: multi-protein language model with an improved deep neural network for generalized protein‒protein interaction prediction.

ABSTRACT: Predicting protein‒protein interactions (PPIs) plays a crucial role in understanding biological processes. Although biological experimental methods can identify PPIs, they are costly, time-consuming, labor-intensive, and often lack stability. In contrast, computational approaches for PPI prediction, particularly deep learning methods, can efficiently learn representations from protein sequences. However, the generalizability, robustness, and stability of computational PPI prediction models still need improvement, especially for species with limited verified PPI data. Protein embeddings generated by protein language models can extract features from protein sequences and reflect hierarchical biological structures, making them suitable for predicting PPIs. Therefore, in this study, we propose

SUBMITTER: Li Y 

PROVIDER: S-EPMC12628903 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature