TopoBind: Multi-Modal Prediction of Antibody-Antigen Binding Free Energy via Sequence Embeddings and Structural Topology

TL;DR

This paper introduces a multi-modal deep learning framework that combines sequence embeddings and structural topology features to accurately predict antibody-antigen binding free energy, advancing biomolecular modeling.

Contribution

The novel integration of sequence-based embeddings with topological structural features via a cross-attention mechanism improves binding free energy prediction accuracy.

Findings

Outperforms sequence-only models in accuracy.

Achieves state-of-the-art results on benchmark datasets.

Effectively captures multi-scale topological structures.

Abstract

Predicting the binding free energy between antibodies and antigens is a key challenge in structure-aware biomolecular modeling, with direct implications for antibody design. Most existing methods either rely solely on sequence embeddings or struggle to capture complex structural relationships, thus limiting predictive performance. In this work, we present a novel framework that integrates sequence-based representations from pre-trained protein language models (ESM-2) with a set of topological features. Specifically, we extract contact map metrics reflecting residue-level connectivity, interface geometry descriptors characterizing cross-chain interactions, distance map statistics quantifying spatial organization, and persistent homology invariants that systematically capture the emergence and persistence of multi-scale topological structures - such as connected components, cycles, and cavities - within individual proteins and across the antibody-antigen interface. By leveraging a cross-attention mechanism to fuse these diverse modalities, our model effectively encodes both global and local structural organization, thereby substantially enhancing the prediction of binding free energy. Extensive experiments demonstrate that our model consistently outperforms sequence-only and conventional structural models, achieving state-of-the-art accuracy in binding free energy prediction.