Deep Sequencing of Systematic Combinatorial Libraries Reveals β-lactamase Sequence Constraints at High Resolution

By Zhifeng Deng, Wanzhi Huang, Erol Bakkalbasi, N. Brown, Carolyn J. Adamski, Kacie Rice, D. Muzny, Richard A. Gibbs, Timothy G. Palzkill. 2012

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In this study, combinatorial libraries were used in conjunction with ultra-high throughput sequencing to comprehensively determine the impact of each of the 19 possible amino acid substitutions at each residue position in the TEM-1β-lactamase enzyme. The libraries were introduced into E. coli and mutants were selected for ampicillin resistance. The selected colonies were pooled and subjected to ultra-high throughput sequencing to reveal the sequence preferences at each position. The depth of sequencing provided a clear, statistically significant picture of what amino acids are favored for ampicillin hydrolysis for all 263 positions of the enzyme in one experiment. Although the enzyme is generally tolerant of amino acid substitutions, several surface positions far from the active site are sensitive to substitutions suggesting a role for these residues in enzyme stability, solubility or catalysis. In addition, information on the frequency of substitutions was used to identify mutations that increase enzyme thermodynamic stability. Finally, a comparison of sequence requirements based on the mutagenesis results versus those inferred from sequence conservation in an alignment of 156 class A β-lactamases reveals significant differences in that several residues in TEM-1 do not tolerate substitutions and yet extensive variation is observed in the alignment, and vice versa. An analysis of the TEM-1 and other class A structures suggests residues that vary in the alignment may nevertheless make unique, but important, interactions within individual enzymes.

Subfields: Biotechnology, Molecular Biology, Molecular Medicine

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Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, methodology): “the change in Gibbs Free Energy relative to wild type ∆∆G ≡ ∆Gmut − ∆Gwt, as measured by four independent studies (Ref1, Ref2, Ref3 and Ref4 are [61],[62],[63] and [19] respectively).”
How Structural and Physicochemical Determinants Shape Sequence Constraints in a Functional Enzyme (2015, background): “[18], which expands on a study at lower resolution as feasible two decades ago [47].”
Capturing the mutational landscape of the beta-lactamase TEM-1 (2013, background): “This may, for instance, explain the discrepancies observed between the low (high) conservation of a residue in protein alignments and the strong (low) impact of mutations affecting that residue (11).”
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, background): “Survival assays involved placing the organism in hostile conditions where the target protein confers an advantage such as antibiotic resistance (Deng et al , 2012; Jacquier et al , 2013; Firnberg et al , 2014; Stiffler et al , 2015; Dandage et al , 2018).”
A Comprehensive, High-Resolution Map of a Gene’s Fitness Landscape (2014, methodology): “2013), and/or focused on the characterization of high fitness alleles with more than one mutation and assumed additivity for predicting the effect of the individual mutations (Deng et al. 2012).”