RoseTTAFold diffusion-guided short peptide design: a case study of binders against Keap1/Nrf2.

ABSTRACT: In this study, we proposed a novel comprehensive computational framework that combines deep generative modeling with in silico peptide optimization to expedite the discovery of bioactive compounds. Our methodology utilizes RFdiffusion, a variation of the RoseTTAFold model for protein design, in tandem with ProteinMPNN, a deep neural network for protein sequence optimization, to provide short candidate peptides for targeted binding interactions. As a proof-of-concept, we focused on Keap1 (Kelch-like ECH-associated protein 1), a key regulator in the Keap1/Nrf2 antioxidant pathway. To achieve this, we designed peptide sequences that would interact with specific binding subpockets within its Kelch domain. We integrated machine learning models to forecast essential peptide properties, including

SUBMITTER: Morena F 

PROVIDER: S-EPMC11928978 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature