Optimized Quantitative Bacterial Two-Hybrid (qB2H) for Protein-Protein Interaction Assessment

Area of Science:

• Biochemistry and Molecular Biology
• Protein Engineering
• Computational Biology

Background:

• Characterizing mutation effects on protein-protein interactions (PPIs) is vital for understanding protein structure and function.
• Massively parallel analyses, like deep mutational scanning, generate data for machine learning but rely on reliable quantitative methods.
• Existing bacterial two-hybrid (B2H) systems have limitations affecting accurate dataset generation for PPI variant analysis.

Purpose of the Study:

• To engineer and benchmark optimized quantitative B2H (qB2H) systems for reliable and accurate PPI variant analysis.
• To demonstrate the utility of qB2H in interface mapping and protein binder optimization.
• To provide R&D scientists with a robust platform for quantitative PPI analysis and data-driven discovery.

Main Methods:

• Engineered and benchmarked optimized quantitative B2H (qB2H) alternatives.
• Developed strain-independent assays with improved metrics for high-quality dataset generation.
• Applied qB2H for interface mapping, perturbation analysis, and integration with AI-based protein design.

Main Results:

• Engineered qB2H systems overcome limitations of existing B2H methods, enabling accurate PPI variant dataset generation.
• Perturbation analysis of single-site variants accurately identified known antisilencing function 1 (ASF1) complex contact positions, consistent with crystallographic data.
• Integration with generative AI yielded an ASF1-binding peptide with a 70-fold affinity increase, showcasing qB2H's application in protein engineering.

Conclusions:

• The developed qB2H platform offers a robust and reusable solution for quantitative PPI analysis.
• qB2H facilitates both rational protein engineering and data-driven discovery by providing high-quality PPI data.
• The study makes code, data, and materials available, promoting community access and further research in PPI analysis.