Sequence Display enables large-scale sequence-activity datasets for rapid protein evolution.

Abstract

Engineering proteins with desired functions remains challenging and usually requires multiple rounds of screening and selection. Here, we present Sequence Display, a platform that generates large-scale protein sequence-activity datasets in a single round. Sequence Display enables multiplexed assessment of individual variant activity within a single experiment, offering a robust approach to mapping detailed sequence-function relationships. We demonstrate the platform's broad applicability by generating datasets for cytosine deaminase, uracil glycosylase inhibitor, aminoacyl-tRNA synthetase and a compact Cas9 nuclease. Integrating these datasets obtained from Sequence Display with pretrained protein language models, fine-grained, variant-specific activity landscapes can be constructed. We discovered several Cas9 variants with expanded protospacer-adjacent motif recognition and evolved aminoacyl-tRNA synthetase variants capable of recognizing different noncanonical amino acids. Together, this study establishes Sequence Display as a powerful tool for mapping protein activity landscapes and accelerating the discovery of optimized proteins for biological and medical applications.