With deep-learning-powered advances in protein design methods, there is an ongoing paradigm shift in protein engineering from random selection to intentional computational design methods. Here we describe the current state of de novo protein design. While there is still room for improvement in success rates and activities, the long-standing challenges of designing new protein structures, assemblies and protein binders are close to being solved. The key current questions in these areas are not how to design, but what to design, and open-source design methodology such as RFdiffusion and ProteinMPNN together with protein structure prediction tools enable biochemists and molecular biologists to broadly explore possible applications. There has also been considerable progress in the de novo design of small-molecule target binders, enzymes and multistate protein systems. Current challenges for methods development include design of catalysts for reactions with high energy barriers and, more generally, design of switches and nanomachines that integrate binding, conformational change and catalysis. Over the next five to ten years, we anticipate the design of sophisticated protein nanomachines and materials with functionality ranging far beyond that generated during natural evolution for a wide range of applications in medicine, technology and sustainability.
Yang et al. (Wed,) studied this question.