Accurate <i>de novo</i> design of high-affinity protein binding macrocycles using deep learning.

ABSTRACT: The development of macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource-intensive and provide little control over binding mode. Despite considerable progress in physics-based methods for peptide design and deep-learning methods for protein design, there are currently no robust approaches for de novo design of protein-binding macrocycles. Here, we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic peptide binders against protein targets of interest. We test 20 or fewer designed macrocycles against each of four diverse proteins and obtain medium to high-affinity binders against all selected targets. Designs against MCL1 and MDM2 demonstrate K_D between 1-10 μM, and the best anti-GABA

SUBMITTER: Rettie SA 

PROVIDER: S-EPMC11601608 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature