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 out

SUBMITTER: Watson JL 

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

REPOSITORIES: biostudies-literature