Conditional language models enable the efficient design of proficient enzymes

Abstract

The design of functional enzymes holds promise for transformative solutions across various domains but presents significant challenges. Inspired by the success of language models in generating nature-like proteins, we explored the potential of an enzyme-specific language model in designing catalytically active artificial enzymes. Here, we introduce ZymCTRL ('enzyme control'), a conditional language model trained on the enzyme sequence space, capable of generating enzymes based on user-defined specifications. Experimental validation at diverse data regimes and for different enzyme families demonstrated ZymCTRL's ability to generate active enzymes across various sequence identity ranges. Specifically, we describe the design of carbonic anhydrases and lactate dehydrogenases in zero-shot, without requiring further training of the model, and showcasing activity at sequence identities below 40% compared to natural proteins. Biophysical analysis confirmed the globularity and well-folded nature of the generated sequences. Furthermore, fine-tuning the model enabled the generation of lactate dehydrogenases more likely to pass in silico filters and with activity comparable to their natural counterparts. Two of the artificial lactate dehydrogenases were scaled up and successfully lyophilised, maintaining activity and demonstrating preliminary conversion in one-pot enzymatic cascades under extreme conditions. Our findings open a new door towards the rapid and cost-effective design of artificial proficient enzymes. The model and training data are freely available to the community.