YeastIT: Reducing mutational bias for in vivo directed evolution using a novel yeast mutator strain based on dual adenine-/cytosine-targeting and error-prone DNA repair

Abstract

Engineering proteins with new functions and properties often requires navigating large sequence spaces through rounds of iterative improvement. However, a disparity exists between the gradual pace of natural long-term evolution and a typical laboratory evolution workflow that relies on enriching functional variants from highly diverse in vitro generated libraries through very few screening rounds. Laboratory experiments often eschew presumed natural strategies such as neutral/non-adaptive and multi-phase evolution trajectories, and therefore mutagenesis technologies suitable for long nature-like timescales are needed. Here, we introduce YeastIT, a novel in vivo mutagenesis tool for protein engineering that leverages an S. cerevisiae strain engineered to exhibit mutagenic activity directed to the gene of interest, allowing its continuous diversification. Mutagenesis is achieved by generating DNA damage through nucleoside deamination, followed by introduction of mutations by harnessing the process of error-prone DNA translesion synthesis. By eliminating the transformation step, YeastIT allows multiple rounds of screening or selection without interruptions for library diversification, thereby enabling long-term and continuous evolution campaigns. Our characterization of the mutational spectrum and frequency of the YeastIT-generated libraries, and its comparison to other methods (error-prone PCR, PACE, MutaT7, eMutaT7, OrthoRep, TRIDENT, EvolVR) demonstrates comparable mutation rates combined with a significant reduction in mutagenic bias relative to most of the alternatives. To validate YeastIT, we carried out directed evolution of a DARPin binding protein to achieve a 15-fold improved affinity. YeastIT thus provides a tool for exploring different evolutionary trajectories which overcomes previous limitations of variant availability (due to bias and low mutation rates) and emulates the way proteins emerge in Nature.