LoGoPPI is presented, which infers PPIs from sequence by combining bi-encoder global protein representation with local residue-level late interaction, and provides a scalable framework for comparative and functional analysis of protein networks across diverse taxa.
Abstract
Graph-based protein function analysis is powerful, but protein-protein interaction (PPI) networks exist for only a small fraction of animal and plant genomes. We present LoGoPPI, which infers PPIs from sequence by combining bi-encoder global protein representation with local residue-level late interaction. LoGoPPI matches or exceeds state-of-the-art PLM-based cross-encoders while achieving orders-of-magnitude faster inference, up to ∼1,500-fold, and its local branch provides residue-level signals associated with interaction interfaces and structurally flexible regions. This efficiency enables practical proteome-wide PPI reconstruction at scales prohibitive for cross-encoder models, potentially extending interactome mapping to tens of thousands of animal and plant species. LoGoPPI thus provides a scalable framework for comparative and functional analysis of protein networks across diverse taxa.
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Protein–protein interactions (PPIs) play a crucial role in enabling proteins to carry out their functions within various biological processes (Hui et al., 2003). Since the introduction of the yeast two-hybrid (Y2H) method for PPI detection in 1989 (Fields and Song, 1989), the identification of PPIs has become a signifi...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
