"Site and Mutation"-Specific Predictions Enable Minimal Directed Evolution Libraries

Directed evolution experiments designed to improve the activity of a biocatalyst have increased in sophistication from the early days of completely random mutagenesis. Sequence-based and structure-based methods have been developed to identify "hotspot" positions that when randomized provide a higher frequency of beneficial mutations that improve activity. These focused mutagenesis methods reduce library sizes and therefore reduce screening burden, accelerating the rate of finding improved enzymes. Looking for further acceleration in finding improved enzymes, we investigated whether two existing methods, one sequence-based (Protein GPS) and one structure-based (using Bioluminate and MOE), were sufficiently predictive to provide not just the hotspot position, but also the amino acid substitution that improved activity at that position. By limiting the libraries to variants that contained only specific amino acid substitutions, library sizes were kept to less than 100 variants. For an initial round of ATA-117 R-selective transaminase evolution, we found that the methods used produced libraries where 9% and 18% of the amino acid substitutions chosen were amino acids that improved reaction performance in lysates. The ability to create combinations of mutations as part of the initial design was confounded by the relatively large number of predicted mutations that were inactivating (30% and 45% for the sequence-based and structure-based methods, respectively). Despite this, combining several mutations identified within a given method produced variant lysates 7- and 9-fold more active than the wild-type lysate, highlighting the capability of mutations chosen this way to generate large advances in activity in addition to the reductions in screening.