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

## TL;DR

This paper introduces a new method to analyze how mutations in a protein affect its function, using beta-lactamase TEM-1 as a case study.

## Contribution

The novel inference scheme captures epistatic couplings, improving predictive power by 40% over previous methods.

## Key findings

- The method captures epistatic couplings between residues in mutational landscapes.
- Mutational effects depend on sequence context up to 20 residues away from the mutation site.
- The approach improves explicative power for beta-lactamase TEM-1 by about 40% compared to non-epistatic methods.

## Abstract

The quantitative characterization of mutational landscapes is a task of outstanding importance in evolutionary and medical biology: It is, e.g., of central importance for our understanding of the phenotypic effect of mutations related to disease and antibiotic drug resistance. Here we develop a novel inference scheme for mutational landscapes, which is based on the statistical analysis of large alignments of homologs of the protein of interest. Our method is able to capture epistatic couplings between residues, and therefore to assess the dependence of mutational effects on the sequence context where they appear. Compared to recent large-scale mutagenesis data of the beta-lactamase TEM-1, a protein providing resistance against beta-lactam antibiotics, our method leads to an increase of about 40% in explicative power as compared to approaches neglecting epistasis. We find that the informative sequence context extends to residues at native distances of about 20 from the mutated site, reaching thus far beyond residues in direct physical contact.