Today, our team is sharing a preprint on our new system for generative de-novo enzyme design, AlphaProtein Novo.

At @GoogleDeepMind, we have long focused on the building blocks of life (proteins). We developed AlphaFold to predict structure, trained language models to understand function, and built AlphaProteo to design de-novo binders. But proteins play many important roles in nature, such as enzymes that catalyze chemical reactions.

Designing entirely new enzymes from scratch to catalyze custom reactions has long been a holy grail for scientists. Our team has shown how AlphaProtein Novo can produce new-to-nature enzymes with state-of-the-art activity on 2 benchmark reactions. We’ve also designed custom enzymes to synthesize the pharmaceutical motif piperidine, and others to degrade the environmental plasticizer toxin DEHP.

While there is much more work to do in this direction, we’re excited to have demonstrated the ability to design functional proteins that go beyond what is known in nature. Huge congratulations to @jueseph, @ZvxyWu, Josh Abramson, the @GoogleDeepMind protein design team, and our collaborators in the @francesarnold & Liu Group! 🎉

🔗 Preprints and code: https://biorxiv.org/content/10.64898/2026.10.01.756017v1… https://biorxiv.org/content/10.64898/2026.10.01.754751v1… https://github.com/google-deepmind/alphaprotein-novo…

Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo

Source: https://www.biorxiv.org/content/10.64898/2026.10.01.756017v1 New Results

,View ORCID ProfileJoshuaAbramson,View ORCID ProfileThomasFrerix,View ORCID ProfileAlexander EChu,View ORCID ProfileRuijie KZhang,View ORCID ProfileLucaSchulz,View ORCID ProfileAmy EDanson,View ORCID ProfileTristan O. C.Kwan,View ORCID ProfileWenliang KLi,View ORCID ProfileJacobKelly,View ORCID ProfileZi-QiLi,View ORCID ProfileRosalia GSchneider,AshokThillaisundaram,HarshniraPatani,Vinicius FZambaldi,SukhdeepSingh,DavidLa,View ORCID ProfileMasyDomecillo,View ORCID ProfileAriane N.Mora,View ORCID ProfileJulia C.Reisenbauer,View ORCID ProfileYuZhang,EliseoPapa,AkvileZemgulyte,Yu-HanWu,View ORCID ProfileAugustinZidek,View ORCID ProfileJiaxinShi,GraceMargand,NailaAssem,KateStephen,View ORCID ProfileCharlieEmrich,View ORCID ProfilePengLiu,View ORCID ProfileColwellLucy,DemisHassabis,View ORCID ProfileRobFergus,View ORCID ProfileFrances H.Arnold,PushmeetKohli,View ORCID ProfileJueWang

doi:https://doi.org/10.64898/2026.10.01.756017

Abstract

Creating highly active enzymes for arbitrary reactions is a transformative goal for the molecular sciences (1). Traditional protein engineering (2,3) is constrained by a reliance on existing natural starting points, which are often challenging to discover. De novo design offers the potential to overcome this by creating enzymes from first principles (4), but has yet to achieve practically relevant catalytic properties. Here we present AlphaProtein Novo (AP Novo), a machine-learning pipeline that demonstrates, for the first time, that de novo enzyme design can outperform natural sequence mining in addressing challenging chemistry. We used AP Novo to design new-to-nature nitrene transferases to synthesise the pharmacophore piperidine with unprecedented product selectivity, and to create enzymes that degrade the environmental toxin DEHP under conditions that denature natural enzymes. We also obtained state-of-the-art catalytic efficiencies on two well-studied model reactions, and through iterative design and analysis found that key enablers of success were mechanism-inspired metrics based on AlphaFold 3 predictions and sequence-ensemble-based scoring of designed backbones. Our results show that de novo design has become a powerful complement to natural enzyme diversity for discovering catalysts for a range of applications.

Competing Interest Statement

Author-affiliated entities have filed a US provisional patent application relating to the de novo design of proteins and enzymes using generative diffusion models and structure prediction neural networks. All of the authors other than Z.-Q.L., M.D., A.N.M., J.C.R., Y.Z., P.L. and F.H.A. have commercial interests in the work described.

Copyright

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.All rights reserved. No reuse allowed without permission.