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De novo design of protein structure and function with RFdiffusion.

ABSTRACT: There has been considerable recent progress in designing new proteins using deep-learning methods^1-9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models^10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

SUBMITTER: Watson JL

PROVIDER: S-EPMC10468394 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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De novo design of protein structure and function with RFdiffusion.

Watson Joseph L JL Juergens David D Bennett Nathaniel R NR Trippe Brian L BL Yim Jason J Eisenach Helen E HE Ahern Woody W Borst Andrew J AJ Ragotte Robert J RJ Milles Lukas F LF Wicky Basile I M BIM Hanikel Nikita N Pellock Samuel J SJ Courbet Alexis A Sheffler William W Wang Jue J Venkatesh Preetham P Sappington Isaac I Torres Susana Vázquez SV Lauko Anna A De Bortoli Valentin V Mathieu Emile E Ovchinnikov Sergey S Barzilay Regina R Jaakkola Tommi S TS DiMaio Frank F Baek Minkyung M Baker David D

Nature 20230711 7976

There has been considerable recent progress in designing new proteins using deep-learning methods<sup>1-9</sup>. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models<sup>10,11</sup> have had considerable success in image and language generative modelling but limited success when applied to ...[more]

PMID: 37433327

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Project description:Proteins are molecular machines whose function depends on their ability to achieve complex folds with precisely defined structural and dynamic properties. The rational design of proteins from first-principles, or de novo, was once considered to be impossible, but today proteins with a variety of folds and functions have been realized. We review the evolution of the field from its earliest days, placing particular emphasis on how this endeavor has illuminated our understanding of the principles underlying the folding and function of natural proteins, and is informing the design of macromolecules with unprecedented structures and properties. An initial set of milestones in de novo protein design focused on the construction of sequences that folded in water and membranes to adopt folded conformations. The first proteins were designed from first-principles using very simple physical models. As computers became more powerful, the use of the rotamer approximation allowed one to discover amino acid sequences that stabilize the desired fold. As the crystallographic database of protein structures expanded in subsequent years, it became possible to construct proteins by assembling short backbone fragments that frequently recur in Nature. The second set of milestones in de novo design involves the discovery of complex functions. Proteins have been designed to bind a variety of metals, porphyrins, and other cofactors. The design of proteins that catalyze hydrolysis and oxygen-dependent reactions has progressed significantly. However, de novo design of catalysts for energetically demanding reactions, or even proteins that bind with high affinity and specificity to highly functionalized complex polar molecules remains an importnant challenge that is now being achieved. Finally, the protein design contributed significantly to our understanding of membrane protein folding and transport of ions across membranes. The area of membrane protein design, or more generally of biomimetic polymers that function in mixed or non-aqueous environments, is now becoming increasingly possible.

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De novo design of protein structure and function with RFdiffusion.

Publications

De novo design of protein structure and function with RFdiffusion.

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