Accurate protein stability predictions from homology models

Calculating changes in protein stability ({Delta}{Delta}G) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate {Delta}{Delta}Gs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of {Delta}{Delta}G values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested {Delta}{Delta}Gs and templates for homology modeling across a broad range of sequence identities, and selected three methods for {Delta}{Delta}G calculations to test. We find that {Delta}{Delta}G-values predicted from homology models compare equally well to experimental {Delta}{Delta}Gs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using {Delta}{Delta}Gs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.