Machine-learning prediction of affinity and epistasis in the bovine pancreatic trypsin inhibitor-chymotrypsin complex.

Protein-protein interactions (PPIs) are shaped by evolutionary pressures that fine-tune binding affinities and drive the epistatic relationships that support functional outcomes. Here, we used the complex of bovine pancreatic trypsin inhibitor (BPTI) and chymotrypsin as a model system to study how mutations at one or two positions affect binding affinity and epistasis. To predict the binding affinity landscape of the BPTI-chymotrypsin complex, we combined deep sequencing data, obtained from a saturation scanning mutagenesis BPTI library, with a machine-learning (ML) model. Using this ML model, which was trained on a subset of experimental binding data, we predicted the binding affinities and epistatic interactions across thousands of single and double BPTI mutants, including those not observed in the library. Our predictive approach completed missing data points and enabled us to reveal global trends in affinity changes and mutation couplings within specific binding interface positions. Our analysis revealed that different mutations in the same position may have different effects on affinity, with most double mutations leading to increased epistasis, particularly at hotspot positions, thereby indicating a cooperative binding effect. In most cases, affinity and epistasis were inversely correlated, with affinity enhancement of double-mutant variants being associated with negative epistasis. Our approach can be readily generalized to predict mutation effects in larger combinatorial libraries and in proteins for which structural information is lacking.