Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling

TL;DR

This paper introduces CpSDE, a novel generative framework combining harmonic SDE and atom-bond modeling to design diverse cyclic peptides with improved stability and affinity, overcoming data scarcity and geometric constraints.

Contribution

The paper presents CpSDE, a new method integrating AtomSDE and ResRouter for cyclic peptide design, addressing key challenges in structural data scarcity and non-canonical amino acids.

Findings

Designed cyclic peptides show high stability and affinity.

The method effectively handles diverse cyclic peptide structures.

Overcomes limitations of existing generative models for cyclic peptides.

Abstract

Cyclic peptides offer inherent advantages in pharmaceuticals. For example, cyclic peptides are more resistant to enzymatic hydrolysis compared to linear peptides and usually exhibit excellent stability and affinity. Although deep generative models have achieved great success in linear peptide design, several challenges prevent the development of computational methods for designing diverse types of cyclic peptides. These challenges include the scarcity of 3D structural data on target proteins and associated cyclic peptide ligands, the geometric constraints that cyclization imposes, and the involvement of non-canonical amino acids in cyclization. To address the above challenges, we introduce CpSDE, which consists of two key components: AtomSDE, a generative structure prediction model based on harmonic SDE, and ResRouter, a residue type predictor. Utilizing a routed sampling algorithm that alternates between these two models to iteratively update sequences and structures, CpSDE facilitates the generation of cyclic peptides. By employing explicit all-atom and bond modeling, CpSDE overcomes existing data limitations and is proficient in designing a wide variety of cyclic peptides. Our experimental results demonstrate that the cyclic peptides designed by our method exhibit reliable stability and affinity.