Quantitative model for binary measurements of protein-protein interactions

Area of Science:

• Biophysics
• Systems Biology
• Computational Biology

Background:

• Protein-protein interactions (PPIs) are fundamental to cellular processes.
• Understanding PPI networks is crucial for deciphering biological functions.
• Existing methods for measuring PPIs have limitations in quantification.

Purpose of the Study:

• To develop a stochastic model for quantifying binary PPI measurements.
• To investigate the factors contributing to PPI network topology.
• To explore the applicability of the binary response function (BRF) across different PPI detection methods.

Main Methods:

• Development of a stochastic model incorporating a binary response function (BRF).
• Analysis of the BRF's sharpness (Hill's coefficient) effect.
• Application of the model to yeast two-hybrid and mass spectrometry data.
• Investigation of protein association free energy and cellular protein abundance contributions.

Main Results:

• The proposed model quantifies binary PPI measurements effectively.
• The Hill's coefficient of the BRF significantly impacts detection probability.
• The BRF is applicable to both yeast two-hybrid and mass spectrometry.
• Both protein association constants and cellular abundance influence PPI network topology.

Conclusions:

• Protein association constants are not entirely independent.
• Cellular protein abundance is a key factor in PPI network topology.
• The model provides insights into the formation of power-law degree distributions in PPI networks.