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Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization

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  • Michael Heyne

    (Hebrew University of Jerusalem
    Ben-Gurion University of the Negev)

  • Niv Papo

    (Ben-Gurion University of the Negev)

  • Julia M. Shifman

    (Hebrew University of Jerusalem)

Abstract

Quantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔGbind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed an attractive approach that allows us to quantify ΔΔGbind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, deep sequencing, and a few experimental ΔΔGbind data points on purified proteins to generate ΔΔGbind values for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin (BT). We show that ΔΔGbind for this interaction could be quantified with high accuracy over the range of 12 kcal mol−1 displayed by various BPTI single mutants.

Suggested Citation

  • Michael Heyne & Niv Papo & Julia M. Shifman, 2020. "Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-13895-8
    DOI: 10.1038/s41467-019-13895-8
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