Quantitative Description of a Protein Fitness Landscape Based on Molecular Features

Abstract

Understanding the driving forces behind protein evolution requires the ability to correlate the molecular impact of mutations with organismal fitness. To address this issue, we employ here metallo-β-lactamases as a model system, which are Zn(II) dependent enzymes that mediate antibiotic resistance. We present a study of all the possible evolutionary pathways leading to a metallo-β-lactamase variant optimized by directed evolution. By studying the activity, stability and Zn(II) binding capabilities of all mutants in the preferred evolutionary pathways, we show that this local fitness landscape is strongly conditioned by epistatic interactions arising from the pleiotropic effect of mutations in the different molecular features of the enzyme. Activity and stability assays in purified enzymes do not provide explanatory power. Instead, measurement of these molecular features in an environment resembling the native one provides an accurate description of the observed antibiotic resistance profile. We report that optimization of Zn(II) binding abilities of metallo-β-lactamases during evolution is more critical than stabilization of the protein to enhance fitness. A global analysis of these parameters allows us to connect genotype with fitness based on quantitative biochemical and biophysical parameters.

Keywords: antibiotic resistance; epistasis; metallo-ß-lactamase; protein evolution.

Fig. 1.

Structure of the BcII variant GLVN and mutational trajectories from wt-BcII to the evolved mutant GLVN. (A) Representation of the GLVN variant structure determined by X-ray crystallography (PDB 3FCZ) (Tomatis et al. 2008). Residues from the active site are shown as sticks, Zn(II) ions as black spheres and sites of mutations as magenta spheres. (B) Each combination of the four mutations G262S, L250S, V112A, and N70S was constructed to determine MICs (µg/ml) toward cephalexin conferred to Escherichia coli. The letter coding refers to residues from wt-BcII to indicate the presence of each mutation (G stands for G262S, L for L250S, V for V112A, and N for N70S). The single letter coding is used for the protein variant, whereas the two letter-coding including numbering refers to the mutation. MIC values are sequentially ordered according to increasing number of mutations, from wt to the GLVN mutant. Arrows indicate whether the mutation added is beneficial (green), neutral (light blue), or deleterious (light purple) in that genetic background. The circles’ colous indicate whether the variant shows an increment (green), no change (light blue), or a decrease (light purple) in the MIC value with respect to wt-BcII. Trajectories including a deleterious mutation are represented by dotted arrows. These are not expected to be successful under strong selection pressure, that is, the conditions that led to selection of mutant GLVN (Tomatis et al. 2005).

Fig. 2.

Catalytic efficiencies determined with purified enzymes. (A) Catalytic efficiency toward cephalexin (k_cat/K_M) for wt-BcII and its variants determined with purified enzymes. (B) MIC values plotted versus k_cat/K_M for each variant toward cephalexin.

Fig. 3.

Quantification of protein abundance in periplasmic extracts by Western Blot analyses. (A) Periplasmic extracts of E. coli expressing each of the variants as indicated were concentrated 5-fold. The internal calibration curve was performed with known quantities of purified wt-BcII. Enzyme quantity on each band was obtained by densitometry and the response correction factor previously obtained (see supplementary fig. S1, Supplementary Material online) was applied for each variant. MWM: PageRuler Plus Prestained Protein Ladder, Thermo Scientific. For Coomassie visualization extracts were further concentrated 4-fold. MWM, molecular weight markers are indicated in the figure. Total protein concentration was determined with Pierce BCA Protein Assay Kit. (B) Protein abundance in periplasmic extracts. The results are the average of three replicates ± standard error. A one way ANOVA test of this data showed that there is no significant difference between the means concentrations for each variant (P = 0.99).

Fig. 4.

Catalytic efficiencies determined with periplasmic extracts (A) Catalytic efficiency toward cephalexin (k_cat/K_M apparent values) for wt-BcII and its variants determined with periplasmic extracts. (B) MIC values plotted versus k_cat/K_M apparent values determined with periplasmic extracts for each variant toward cephalexin.

Fig. 5.

Correlations between MIC and catalytic efficiency for cephalexin weighed by the contribution of stability and Zn(II) binding affinity. (A) MIC values plotted versus k_cat/K_M × stability factor; stability factor = T_Mapp^mut/T_Mapp^wt (B) MIC values plotted versus k_cat/K_M × Zn factor; Zn factor = MIC_EDTA/MIC_Zn(II).