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.

Keywords: coevolution; epistasis; genotype–phenotype mapping; mutational landscape; statistical inference.

Fig. 1.

Pipeline of the mutational-landscape prediction: The homologous Pfam family containing the protein of interest (the Beta-lactamase2 family PF13354 in the case of TEM-1) is used to construct a global statistical model using the DCA. This model allows to score mutations by differences in the inferred genotype-to-phenotype mapping between the mutant and the wild-type amino acid sequence. This score, which is expected to incorporate (co-)evolutionary constraints acting across the entire family, is used as a predictor of the phenotypic effects of single (or few) amino acid substitutions in the protein of interest.

Fig. 2.

Context dependence of mutational effects: (A) Procedure of including all residues within a maximal native distance d_max into the prediction of the mutational effects of the residue of interest (labeled i in the figure). This leads to residue-specific subalignments, which consist of columns, which are not necessarily consecutive, but connected in 3D. The results are given in (B). The main figure shows the correlation R² between MIC data and our predictions, as a function of the cutoff distance d_max. The inset shows the average fraction of residues included into the reduced MSAs, again in dependence of d_max.

Fig. 3.

R² between experimental fitness and predicted fitness for the following features: Independent-residue model (IND), DCA, SIFT (SIFT), Polyphen-2 (Poly), PoPMuSiC (PoP), I-Mutant2.0(sequence+structure) (Imut+), MUpro (MUpro), I-Mutant2.0 (Imut), molecular simulations (SIM), relative solvent accessibility (RSA), and Blosum62 substitution matrix (BLO).

Fig. 4.

Statistical scores and thermodynamic stabilities. (A) Scatter plot of the log odd ratio $\Delta {\varphi }_{DCA}$ versus the experimental fitness μ_exp, stabilizing mutations mentioned in the text are highlighted in red. The highest scoring mutations are M182T, G92D, H153R, L201P and E147G, all reported as stabilizing. (B) $\Delta {\varphi }_{DCA}$ for a smaller set of single mutations is now plotted versus the change in Gibbs Free Energy relative to wild type $\Delta \Delta G\equiv \Delta G_{mut}-\Delta G_{wt}$, as measured by four independent studies (Ref1, Ref2, Ref3 and Ref4 are Kather et al. 2008, Raquet et al. 1995, Wang et al. 2002, and Deng et al. 2012, respectively).

Fig. 5.

Protein-specific amino acid substitution effects in TEM-1: Amino acid substitution effects, averaged over experimental measurements (A), DCA predictions (B), and extracted from BLOSUM62 (C). Blue squares correspond to nearly neutral mutations (log MIC > 5.3), whereas yellow squares correspond to highly deleterious mutations (log MIC < 2.6). White squares are used for unobserved substitutions. The histogram in (D) shows R² between averaged computational and experimental amino acid substitution effects.