Prediction of Residue-specific Contributions to Binding and Thermal Stability Using Yeast Surface Display

Accurate prediction of residue burial as well as quantitative prediction of residue-specific contributions to protein stability and activity is challenging, especially in the absence of experimental structural information. This is important for prediction and understanding of disease causing mutations, and for protein stabilization and design. Using yeast surface display of a saturation mutagenesis library of the bacterial toxin CcdB, we probe the relationship between ligand binding and expression level of displayed protein, with in vivo solubility in E. coli and in vitro thermal stability. We find that both the stability and solubility correlate well with the total amount of active protein on the yeast cell surface but not with total amount of expressed protein. We coupled FACS and deep sequencing to reconstruct the binding and expression mean fluorescent intensity of each mutant. The reconstructed mean fluorescence intensity (MFI(seq)) was used to differentiate between buried site, exposed non active-site and exposed active-site positions with high accuracy. The MFI(seq) was also used as a criterion to identify destabilized as well as stabilized mutants in the library, and to predict the melting temperatures of destabilized mutants. These predictions were experimentally validated and were more accurate than those of various computational predictors. The approach was extended to successfully identify buried and active-site residues in the receptor binding domain of the spike protein of SARS-CoV-2, suggesting it has general applicability.