Capturing the mutational landscape of the beta-lactamase TEM-1

By H. Jacquier, A. Birgy, Hervé Le Nagard, Y. Mechulam, E. Schmitt, Jérémy Glodt, Béatrice Berçot, E. Petit, J. Poulain, G. Barnaud and 2 more. 2013

243 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Molecular Medicine

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

In Molecular Medicine

Citations in the corpus

Adaptation in protein fitness landscapes is facilitated by indirect paths (2016, background): “Recently, the bottleneck in experimental throughput has been improved substantially by coupling saturation mutagenesis with deep sequencing (Fowler et al., 2010; Hietpas et al., 2011; Jacquier et al., 2013; Wu et al., 2014; Thyagarajan and Bloom, 2014; Qi et al., 2014; Stiffler et al., 2015) , which”
Predicting evolution (2017, background): “For regulatory or metabolic interactions, additional stabilizing selection against strong binding can modify the landscape to a single fitness peak at intermediate values of Δ G. Importantly, such landscapes have few fit parameters that can be learned from training data 62,80 .”
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).”
Dissecting enzyme function with microfluidic-based deep mutational scanning (2015, background): “Discussion Deep mutational scanning is a powerful tool for exploring the molecular basis of protein function (7, 15, 25, 26).”
Pervasive pairwise intragenic epistasis among sequential mutations in TEM-1 β-lactamase (2019, background): “The presence of M182T reduces the deleterious fitness effect of many mutations throughout the entire protein [24] – mutations that are presumably destabilizing.”
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].”
Epistasis in protein evolution (2016, background): “Similarly, in the antibiotic resistance gene TEM-1 b-lactamase, a “global suppressor” mutation M182T stabilizes the protein, relieving the otherwise deleterious effect of many other mutations that reduce protein stability, including many that enhance the protein’s activity on new antibiotics.(76,78,”
Physical model of the genotype-to-phenotype map of proteins (2016, background): “[44] Hervé Jacquier, Andŕ e Birgy, Herv́e Le Nagard, Yves Mechulam, Emmanuelle Schmitt, J éŕemy Glodt, Beatrice Bercot, Emmanuelle Petit, Julie Poulain, Guilne Barnaud, Pierre-Alexis Gros, and Olivier Tenaillon, “Capturing the mutational landscape of the beta-lactamase tem-1,” Proc.”
A Comprehensive, High-Resolution Map of a Gene’s Fitness Landscape (2014, methodology): “For example, the resolution of the MIC assay in a study of the amoxicillin resistance effects of 18% of the possible amino acid substitutions in TEM-1 was insufficient to capture the effects of synonymous mutations or to identify any beneficial mutations (Jacquier et al. 2013), both of which we read”
Evolvability as a function of purifying selection in TEM-1 β-lactamase. (2015, unknown): “, 2012) or other phenotypes (e.g., minimal inhibitory concentration of antibiotic, MIC; Firnberg et al., 2014; Jacquier et al., 2013); although much has been learned from these studies, the relationships between these properties and fitness are less clear.”