Engineering highly active nuclease enzymes with machine learning and high-throughput screening

• DOI: 10.1016/j.cels.2025.101236
• Publication Date: 2025-03-12T14:34:37Z
• Authors: Neil Thomas; David Belanger; Chenling Xu; Hanson Lee; Kathleen Hirano; Kosuke Iwai; Vanja Polic; Kendra D. Nyberg; Kevin G. Hoff; Lucas Frenz; Charlie A. Emrich; Jun W. Kim; Mariya Chavarha; Abi Ramanan; Jeremy J. Agresti; Lucy J. Colwell
• Keywords: Nuclease; High-Throughput Screening; Synthetic Biology; Protein Engineering; Directed Molecular Evolution

Highlights•TeleProt is a method for combining evolutionary and assay data to design novel proteins•TeleProt achieved an improved hit rate diversity compared with directed evolution•TeleProt discovered nuclease enzyme 11-fold-improved specific activity•Zero-shot showed higher relative error-prone PCRSummaryOptimizing enzymes function in chemical environments central goal of synthetic biology, but optimization often hindered by rugged fitness landscape costly experiments. In this work, we present TeleProt, machine learning (ML) framework that blends experimental diverse protein libraries, employ it improve the catalytic activity degrades biofilms accumulate on chronic wounds. After multiple rounds high-throughput experiments, TeleProt found significantly better top-performing than evolution (DE), had at finding diverse, high-activity variants, was even able high-performance initial library using no prior data. We have released dataset 55,000 one most extensive genotype-phenotype landscapes date, drive further progress ML-guided design. A record paper's transparent peer review process included supplemental information.Graphical abstract