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The quantitative characterization of mutational landscapes is a task ofoutstanding importance in evolutionary and medical biology: It is, e.g., ofcentral importance for our understanding of the phenotypic effect of mutationsrelated to disease and antibiotic drug resistance. Here we develop a novelinference scheme for mutational landscapes, which is based on the statisticalanalysis of large alignments of homologs of the protein of interest. Our methodis able to capture epistatic couplings between residues, and therefore toassess the dependence of mutational effects on the sequence context where theyappear. Compared to recent large-scale mutagenesis data of the beta-lactamaseTEM-1, a protein providing resistance against beta-lactam antibiotics, ourmethod leads to an increase of about 40% in explicative power as compared toapproaches neglecting epistasis. We find that the informative sequence contextextends to residues at native distances of about 20{\AA} from the mutated site,reaching thus far beyond residues in direct physical contact.

Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1