Linear-time prediction of proteome-scale microbial protein interactions

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Protein-protein interactions (PPIs) are crucial for biological processes.
• Predicting PPIs at the proteome scale is computationally intensive due to all-vs-all comparisons.
• Current methods struggle with efficiency and accuracy for large-scale proteome analysis.

Purpose of the Study:

• To develop a computationally efficient method for predicting physical protein interfaces.
• To enable rapid proteome-wide screening of protein-protein interactions.
• To make large-scale PPI network analysis accessible for microbial discovery.

Main Methods:

• Developed FlashPPI, a contrastive learning framework utilizing residue-level interactions.
• Employed a genomic language model to capture cross-protein coevolutionary signals from metagenomic data.
• Aligned interacting protein partners in a shared latent space for prediction.

Main Results:

• Achieved linear-time prediction of physical protein interfaces, overcoming quadratic complexity.
• Demonstrated a four-fold performance increase over existing sequence-based PPI prediction methods.
• Reduced proteome-wide screening time from days to minutes, with performance comparable to structure-folding models at lower computational cost.

Conclusions:

• FlashPPI significantly enhances the speed and accuracy of proteome-wide PPI prediction.
• The framework offers a cost-effective alternative to structure-based methods for large-scale screening.
• An integrated web platform makes FlashPPI accessible for microbial network analysis and discovery.