Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design

TL;DR

This paper introduces a novel AI method that uses axial vector features within a diffusion model to enable the design of D-peptide binders, successfully transferring knowledge from L-protein data and validated through laboratory experiments.

Contribution

It presents the first wet-lab validated generative AI approach for de novo D-peptide binder design using axial vector features for cross-chirality generalization.

Findings

Outperforms existing tools in in silico benchmarks.

Demonstrates efficacy in wet-lab validation.

First validated AI method for D-peptide design.

Abstract

D-peptide binders targeting L-proteins have promising therapeutic potential. Despite rapid advances in machine learning-based target-conditioned peptide design, generating D-peptide binders remains largely unexplored. In this work, we show that by injecting axial features to $E(3)$-equivariant (polar) vector features,it is feasible to achieve cross-chirality generalization from homo-chiral (L--L) training data to hetero-chiral (D--L) design tasks. By implementing this method within a latent diffusion model, we achieved D-peptide binder design that not only outperforms existing tools in in silico benchmarks, but also demonstrates efficacy in wet-lab validation. To our knowledge, our approach represents the first wet-lab validated generative AI for the de novo design of D-peptide binders, offering new perspectives on handling chirality in protein design.