De Novo Generation and Prioritization of Target-Binding Peptide Motifs from Sequence Alone

Abstract

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery, requiring innovative approaches to overcome the lack of putative binding sites. Recently, generative models have been trained to design binding proteins from the three-dimensional structure of a target protein alone, but thus exclude design to disordered or conformationally unstable targets. In this work, we provide a generalizable algorithmic framework to design short, target-binding peptide motifs, 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 state-of-the-art ESM-2 protein language model, and subsequently screen these de novo linear sequences for target-selective interaction activity via a 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 peptide motifs experimentally via fusion to E3 ubiquitin ligase domains, demonstrating functionally potent degradation of conventionally undruggable targets in vitro. Overall, our design strategy provides a modular toolkit for designing short binding motifs to any target protein without the reliance on stable and ordered tertiary structure, enabling generation of programmable modulators to undruggable and disordered proteins such as transcription factors and fusion oncoproteins.