PrePPI: A Structure Informed Proteome-wide Database of Protein-Protein Interactions

Area of Science:

• Computational Biology
• Bioinformatics
• Structural Biology

Background:

• Protein-protein interactions (PPIs) are crucial for cellular functions.
• Predicting PPIs on a proteome-wide scale is essential for understanding complex biological systems.
• Existing methods often lack comprehensive structural information.

Purpose of the Study:

• To present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver.
• To enhance the prediction of human protein-protein interactions (PPIs) using a proteome-wide, structure-informed approach.
• To provide a valuable resource for exploring the human interactome.

Main Methods:

• Utilized a Bayesian framework combining structural and non-structural evidence for PPI prediction.
• Integrated AlphaFold structures, parsed into individual domains, into the structural modeling component.
• Employed a unique scoring function for evaluating putative protein complexes on a proteome-wide scale.

Main Results:

• The updated PrePPI webserver successfully predicts PPIs on a proteome-wide scale for the human interactome.
• Achieved high performance, validated by receiver operating characteristic curves on E. coli and human PPI databases.
• Developed a database of approximately 1.3 million human PPIs accessible via a user-friendly webserver.

Conclusions:

• PrePPI is a state-of-the-art resource offering an unprecedented structure-informed view of the human interactome.
• The updated webserver provides multiple functionalities for detailed examination of predicted PPIs and related data.
• This tool facilitates deeper understanding of protein complex formation and biological pathways.