A Comprehensive, High-Resolution Map of a Gene’s Fitness Landscape

By Elad Firnberg, Jason W. Labonte, Jeffrey J. Gray, M. Ostermeier. 2014

329 citations recorded by the source corpus.

Mutations are central to evolution, providing the genetic variation upon which selection acts. A mutation’s effect on the suitability of a gene to perform a particular function (gene fitness) can be positive, negative, or neutral. Knowledge of the distribution of fitness effects (DFE) of mutations is fundamental for understanding evolutionary dynamics, molecular-level genetic variation, complex genetic disease, the accumulation of deleterious mutations, and the molecular clock. We present comprehensive DFEs for point and codon mutants of the Escherichia coli TEM-1 β-lactamase gene and missense mutations in the TEM-1 protein. These DFEs provide insight into the inherent benefits of the genetic code’s architecture, support for the hypothesis that mRNA stability dictates codon usage at the beginning of genes, an extensive framework for understanding protein mutational tolerance, and evidence that mutational effects on protein thermodynamic stability shape the DFE. Contrary to prevailing expectations, we find that deleterious effects of mutation primarily arise from a decrease in specific protein activity and not cellular protein levels.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, methodology): “[22] Elad Firnberg, Jason W Labonte, Jeffrey J Gray, and Marc Ostermeier.”
Low-N protein engineering with data-efficient deep learning (2020, methodology): “We next challenged our approach to generalize to the enzyme TEM-1 β -lactamase and optimize protein function training only on single mutants, which lack epistatic information 48 .”
How Structural and Physicochemical Determinants Shape Sequence Constraints in a Functional Enzyme (2015, background): “of a lack of variability and/or confounding effects from epistatic interactions, or from other deep sequencing datasets of lower resolution [19,20].”
Physical model of the genotype-to-phenotype map of proteins (2016, methodology): “By sequence alignment methods [33, 41–43], it is possible to test whether these sensitive positions in the protein exhibit strong correlations in the gene, as predicted by the model.”
Quantifying and understanding the fitness effects of protein mutations: Laboratory versus nature (2016, methodology): “which represents the most recent bottleneck in human evolution, while microbial effective population sizes can be as high as 10.(10) For this reason, the sequences of human proteins have not been as stringently selected as those in microbes with larger effective population sizes.”
Evolvability as a function of purifying selection in TEM-1 β-lactamase. (2015, unknown): “Indeed, many comprehensive studies of mutational tolerance in proteins have assessed biochemical traits (e.g., protein-binding affinity; Fowler et al., 2010; McLaughlin et al., 2012) or other phenotypes (e.g., minimal inhibitory concentration of antibiotic, MIC; Firnberg et al., 2014; Jacquier et al”
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).”
Pervasive pairwise intragenic epistasis among sequential mutations in TEM-1 β-lactamase (2019, methodology): “correlation between sequencing counts (ni) and the standard deviation of the difference in fitness between synonymous alleles [20, 22].”