Back

Origins and breadth of pairwise epistasis in an α-helix of β-lactamase TEM-1

Birgy, A.; Roussel, C.; Kemble, H.; Mullaert, J.; Panigoni, K.; Chapron, A.; Chatel, J.; Magan, M.; Jacquier, H.; Cocco, S.; Monasson, R.; Tenaillon, O.

2021-11-29 evolutionary biology
10.1101/2021.11.29.470435 bioRxiv
Show abstract

Epistasis affects genome evolution together with our ability to predict individual mutation effects. The mechanistic basis of epistasis remains, however, largely unknown. To quantify and better understand interactions between fitness-affecting mutations, we focus on a 11 amino-acid -helix of the protein {beta}-lactamase TEM-1, and build a comprehensive library of more than 15,000 double mutants. Analysis of the growth rates of these mutants shows pervasive epistasis, which can be largely explained by a non-linear two-state model, where inactivating, destabilizing, neutral, or stabilizing mutations additively contribute to the phenotype. Hence, most epistatic interactions can be predicted by a non-linear model informed by single-point mutational measurements only. Deviations from the two-state model are consistently found for few pairs of residues, in particular when they are in contact. This result, as well as single-point mutation parameters, can be quantitatively found back through direct-coupling-analysis-based statistical models inferred from homologous sequence data. Our results thus shed light on the existence and the origins of the multiple determinants of the epistatic landscape, even at the level of small structural components of a protein, and suggest that the corresponding constraints shape the entire {beta}-lactamase family.

Published in Nature Communications (predicted rank #3) · training set

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.