Dissecting enzyme function with microfluidic-based deep mutational scanning

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Mutation effects predicted from sequence co-variation (2015, background): “001; (3) ligand binding/protein interaction: all substitutions to residues within 4Å minimum atom distance of ligand or interaction partner, but excluding evolutionarily conserved cofactors; (4) buried/exposed: relative solvent accessibility calculated using DSSP < 0.”
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “Furthermore, our coevolutionary approach clearly outperforms state-of-the-art approaches like SIFT and PolyPhen-2, which are based on non-epistatic models.”
Biochemical analysis on microfluidic chips (2016, background): “Microfluidic-based deep mutational scanning provided a comprehensive and unbiased view to dissect enzyme function [168].”
Machine learning in protein engineering (2018, background): “…including green ﬂuorescent protein (Sarkisyan et al., 2016), amidase (Wrenbeck et al., 2017), β -lactamase (Klesmith et al., 2017), β -glucosidase (Romero et al., 2015), HIV envelope protein (Haddox et al., 2016), inﬂuenza nucleoprotein (Ashenberg et al., 2017), inﬂuenza hemagglutinin (Doud &…”
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].”