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bioRxiv · 10.1101/509984

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

Heyne, M.·Papo, N.·Shifman, J.
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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 ({Delta}{Delta}Gbind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed a new approach that allows us to quantify {Delta}{Delta}Gbind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, Next Generation Sequencing, and a few experimental {Delta}{Delta}Gbind data points on purified proteins to generate {Delta}{Delta}Gbind 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. We show that {Delta}{Delta}Gbind for this interaction could be quantified with high accuracy over the range of 12 kcal/mol displayed by various BPTI single mutants.

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Heyne, M., Papo, N., Shifman, J.. 2019-01-03. Generating quantitative binding landscapes through fractional binding selections, deep sequencing and data normalization. https://doi.org/10.1101/509984

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