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Computational enzyme design has made great strides over the last five years. Traditional methods of enzyme design require synthesis and evaluation of many mutations. Computational enzyme design has emerged as a powerful tool to predict how specific mutations modify a protein’s activity, stability, and/or selectivity. Such computational approaches can evaluate many mutations and reduce the load of in vitro work by identifying mutations likely to accomplish design objectives. Computational approaches can explore mutational spaces inaccessible in traditional mutagenesis. Computational methods reduce cost and time compared with experimental approaches. We review the efficacy and key differences of computational enzyme design methods as published in recent studies. The included articles used computational methods to design enzymes, were published no earlier than 2015, met design objectives, and verified results in vitro.

The genetic and molecular basis of flagellar motility has been investigated for several decades, with innovative research strategies propelling advances at a steady pace. Furthermore, as the phenomenon is examined in diverse bacteria, new taxon-specific regulatory and structural features are being elucidated. Motility is also a straightforward bacterial phenotype that can allow undergraduate researchers to explore the palette of molecular genetic tools available to microbiologists. This study, driven primarily by undergraduate researchers, evaluated hundreds of flagellar motility mutants in the Gram-negative plant-associated bacterium Agrobacterium fabrum. The nearly saturating screen implicates a total of 37 genes in flagellar biosynthesis, including genes of previously unknown function.

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