Fully saturated mutagenesis scan of the SaCas9 WE domain aminio acid position 888, 889, and 909. SaCas9 WED domain mutagenesis scan in the paper "Accurate top protein variant discovery via low-N pick-and-validate machine learning"

A strategy to obtain greatest number of best-performing variants with least amount of experimental effort over the vast combinatorial mutational landscape would have enormous utility in boosting resource producibility for protein engineering. Towards this goal, we present a simple and effective machine learning-based strategy that outperforms other state-of-the-art methods. Our strategy integrates zero-shot prediction and multi-round sampling to direct active learning via experimenting only few predicted top variants. We find that four rounds of low-N pick-and-validate sampling of 12 variants for machine learning yielded the best accuracy of up to 92.6% in selecting the true top 1% variants in combinatorial mutant libraries, while two rounds of 24 variants can also be used. We demonstrate our strategy in successfully discovering high-performance protein variants from diverse families including the CRISPR-based genome editors, supporting its generalizable application for solving protein engineering tasks. A record of this paper’s Transparent Peer Review process is included in the Supplemental Information.