Jue Wang1,2,3,4, Joseph L. Watson1,2 and Sidney L. Lisanza1,2,3 1Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA 2Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA 3Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington 98195, USA 4DeepMind, London EC4A 3BF, United Kingdom Correspondence: juewangpost.harvard.edu

Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.