De novo design of peptide binders to conformationally diverse targets with contrastive language modeling.

ABSTRACT: Designing binders to target undruggable proteins presents a formidable challenge in drug discovery. In this work, we provide an algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model and subsequently screen these novel sequences for target-selective interaction activity via a contrastive language-image pretraining (CLIP)-based contrastive learning architecture. By integrating these generative and discriminative steps, we create a Peptide Prioritization via CLIP (PepPrCLIP) pipeline and validate highly ranked, target-specific peptides experiment

SUBMITTER: Bhat S 

PROVIDER: S-EPMC11753435 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature