Surface-based Molecular Design with Multi-modal Flow Matching

TL;DR

SurfFlow is a novel surface-based generative model that co-designs peptide sequences, structures, and surfaces, significantly improving peptide binding predictions by incorporating molecular surface information.

Contribution

It introduces SurfFlow, a multi-modal flow matching algorithm for comprehensive peptide co-design considering sequence, structure, and surface features.

Findings

Outperforms full-atom baselines on PepMerge benchmark

Enhances peptide binding accuracy by surface-aware design

Demonstrates the importance of molecular surfaces in 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.