title: Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1.
abstract: The quantitative characterization of mutational landscapes is a task of outstanding importance in evolutionary and medical biology: It is, for example, 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 with 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 with approaches neglecting epistasis. We find that the informative sequence context extends to residues at native distances of about 20 Å from the mutated site, reaching thus far beyond residues in direct physical contact.
authors.affiliation: UPMC, Institut de Calcul et de la Simulation, Sorbonne Universités, Paris, France Computational and Quantitative Biology, UPMC, UMR 7238, Sorbonne Universités, Paris, France Computational and Quantitative Biology, CNRS, UMR 7238, Paris, France.
authors.forename: Matteo
authors.initials: M
authors.lastname: Figliuzzi
authors.affiliation: Infection, Antimicrobials, Modelling, Evolution, INSERM, Université Denis Diderot Paris 7, UMR 1137, Sorbonne Paris Cité, Paris, France Service de Bactériologie-Virologie, Groupe Hospitalier Lariboisiére-Fernand Widal, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France.
authors.forename: Hervé
authors.initials: H
authors.lastname: Jacquier
authors.affiliation: Steinbuch Centre for Computing, Karlsruhe Institute for Technology, Eggenstein-Leopoldshafen, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Schug
authors.affiliation: Infection, Antimicrobials, Modelling, Evolution, INSERM, Université Denis Diderot Paris 7, UMR 1137, Sorbonne Paris Cité, Paris, France.
authors.forename: Oliver
authors.initials: O
authors.lastname: Tenaillon
authors.affiliation: Computational and Quantitative Biology, UPMC, UMR 7238, Sorbonne Universités, Paris, France Computational and Quantitative Biology, CNRS, UMR 7238, Paris, France martin.weigt@upmc.fr.
authors.forename: Martin
authors.initials: M
authors.lastname: Weigt
mesh_terms: D002874:Chromosome Mapping; D004252:DNA Mutational Analysis; D004269:DNA, Bacterial / Q000032:analysis / Q000235:genetics; D004843:Epistasis, Genetic; D029968:Escherichia coli Proteins / Q000235:genetics*; D019143:Evolution, Molecular; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics*; D001618:beta-Lactamases / Q000235:genetics*
chemical_list: D004269:DNA, Bacterial; D029968:Escherichia coli Proteins; D001618:beta-Lactamases; C034413:beta-lactamase TEM-1
keywords: coevolution; epistasis; genotype–phenotype mapping; mutational landscape; statistical inference