Surface-based Molecular Design with Multi-modal Flow Matching

TL;DR

This work proposes an omni-design peptides generation paradigm, called SurfFlow, a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides, and demonstrates the potential of integrating multiple protein modalities for more effective therapeutic peptide discovery.

Abstract

Therapeutic peptides show promise in targeting previously undruggable binding sites, with recent advancements in deep generative models enabling full-atom peptide co-design for specific protein receptors. However, the critical role of molecular surfaces in protein-protein interactions (PPIs) has been underexplored. To bridge this gap, we propose an omni-design peptides generation paradigm, called SurfFlow, a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides. SurfFlow employs a multi-modality conditional flow matching (CFM) architecture to learn distributions of surface geometries and biochemical properties, enhancing peptide binding accuracy. Evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics. These results highlight the advantages of considering molecular surfaces in de novo peptide discovery and demonstrate the potential of integrating multiple protein modalities for more effective therapeutic peptide discovery.

Figures (6)

• Figure 1: Comparison of full-atom peptide design with and without the surface constraint.
• Figure 2: Workflow of SurfFlow for our peptide omni-design, which considers the multi-modality consistency among sequence, structure, and molecular surface during the generation process.
• Figure 3: Binding energy distributions of designed and native peptides, where the lower is better.
• Figure 4: Peptide designed by DL algorithms and references.
• Figure 5: Peptide design with the cyclic condition.