Full-Atom Peptide Design with Geometric Latent Diffusion

Peptide design plays a pivotal role in therapeutics, allowing brand new possibility to leverage target binding sites that are previously undruggable. Most existing methods are either inefficient or only concerned with the target-agnostic design of 1D sequences. In this paper, we propose a generative model for full-atom Peptide design with Geometric LAtent Diffusion (PepGLAD). We first collect a dataset consisting of both 1D sequences and 3D structures from Protein Data Bank (PDB) and literature, for the training of PepGLAD. We then identify two major challenges of leveraging current diffusion-based models for peptide design: the full-atom geometry and the variable binding geometry. To tackle the first challenge, PepGLAD derives a variational autoencoder that first encodes full-atom residues of variable size into fixed-dimensional latent representations, and then decodes back to the residue space after conducting the diffusion process in the latent space. For the second issue, PepGLAD explores a receptor-specific affine transformation to convert the 3D coordinates into a shared standard space, enabling better generalization ability across different binding shapes. Remarkably, our method improves diversity and in silico success rate by 18% and 8% in sequence-structure co-design, and achieves 26% absolute gain in recalling the reference binding conformation.