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Understanding the relationship between protein sequence, function, andstability is a fundamental problem in biology. While high-throughput methodshave produced large numbers of sequence-function pairs, functional assays donot distinguish whether mutations directly affect function or are destabilizingthe protein. Here, we introduce a statistical method to infer the underlyingbiophysics from a high-throughput binding assay by combining information frommany mutated variants. We fit a thermodynamic model describing the bound,unbound, and unfolded states to high quality data of protein G domain B1binding to IgG-Fc. We infer an energy landscape with distinct folding andbinding energies for each substitution providing a detailed view of howmutations affect binding and stability across the protein. We accurately inferfolding energy of each variant in physical units, validated by independentdata, whereas previous high-throughput methods could only measure indirectchanges in stability. While we assume an additive sequence-energy relationship,the binding fraction is epistatic due its non-linear relation to energy.Despite having no epistasis in energy, our model explains much of the observedepistasis in binding fraction, with the remaining epistasis identifyingconformationally dynamic regions.

Biophysical inference of epistasis and the effects of mutations on protein stability and function