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

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 (DDGbind) 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 DDGbind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, Next Generation Sequencing, and a few experimental DDGbind data points on purified proteins to generate DDGbind 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 DDGbind for this interaction could be quantified with high accuracy over the range of 12 kcal/mol displayed by various BPTI single mutants.