Evolvability as a function of purifying selection in TEM-1 β-lactamase.

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Low-N protein engineering with data-efficient deep learning (2020, methodology): “From previous work, we know this hydrolysis reaction is well modeled by Michaelis-Menten dynamics 83 .”
Variational auto-encoding of protein sequences (2017, methodology): “We report the outcomes from training the model using the procedure described above on 5 protein families for which fitness measurements are publicly available [8, 9, 10, 11, 12].”
Adaptation in protein fitness landscapes is facilitated by indirect paths (2016, unknown): “Workflow of mRNA display and data validation.”
The genetic landscape of a physical interaction (2018, background): “…occur in the folding of individual proteins or RNAs (‘threshold robustness’) (Bershtein et al., 2006; Olson et al., 2014; Tokuriki and Tawfik, 2009), in saturating enzyme flux (Kacser and Burns, 1973; Stiffler et al., 2015), and in regulatory dynamics (Gjuvsland et al., 2007; Omholt et al., 2000).”
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).”