Accurately predicting antibody-antigen binding residues, i.e., paratopes and
epitopes, is crucial in antibody design. However, existing methods solely focus
on uni-modal data (either sequence or structure), disregarding the
complementary information present in multi-modal data, and most methods predict
paratopes and epitopes separately, overlooking their specific spatial
interactions. In this paper, we propose a novel Multi-modal contrastive
learning and Interaction informativeness estimation-based method for Paratope
and Epitope prediction, named MIPE, by using both sequence and structure data
of antibodies and antigens. MIPE implements a multi-modal contrastive learning
strategy, which maximizes representations of binding and non-binding residues
within each modality and meanwhile aligns uni-modal representations towards
effective modal representations. To exploit the spatial interaction
information, MIPE also incorporates an interaction informativeness estimation
that computes the estimated interaction matrices between antibodies and
antigens, thereby approximating them to the actual ones. Extensive experiments
demonstrate the superiority of our method compared to baselines. Additionally,
the ablation studies and visualizations demonstrate the superiority of MIPE
owing to the better representations acquired through multi-modal contrastive
learning and the interaction patterns comprehended by the interaction
informativeness estimation.

通过使用抗体和抗原的序列和结构数据，本文提出了一种名为 MIPE 的多模态对比学习和相互信息估计的抗原表位和抗体表位预测方法，MIPE 通过最大化每个模态中与结合和非结合残基的表示并同时对齐单模态表示以获得有效的模态表示；为了利用空间交互信息，MIPE 还包括相互信息估计来计算抗体和抗原之间的估计交互矩阵，从而近似于实际矩阵。大量实验证明了我们的方法相比基线方法的优越性，并且消融实验和可视化结果证明了 MIPE 的优越性，这要归功于通过多模态对比学习获得的更好表示和相互信息估计所理解的交互模式。

多模态对比学习与交互信息性估计提升抗原表位和抗体表位预测

Improving Paratope and Epitope Prediction by Multi-Modal Contrastive  Learning and Interaction Informativeness Estimation

An accurate binding affinity prediction between T-cell receptors and epitopes
contributes decisively to develop successful immunotherapy strategies. Some
state-of-the-art computational methods implement deep learning techniques by
integrating evolutionary features to convert the amino acid residues of cell
receptors and epitope sequences into numerical values, while some other methods
employ pre-trained language models to summarize the embedding vectors at the
amino acid residue level to obtain sequence-wise representations.
Here, we propose a highly reliable novel method, MATE-Pred, that performs
multi-modal attention-based prediction of T-cell receptors and epitopes binding
affinity. The MATE-Pred is compared and benchmarked with other deep learning
models that leverage multi-modal representations of T-cell receptors and
epitopes. In the proposed method, the textual representation of proteins is
embedded with a pre-trained bi-directional encoder model and combined with two
additional modalities: a) a comprehensive set of selected physicochemical
properties; b) predicted contact maps that estimate the 3D distances between
amino acid residues in the sequences.
The MATE-Pred demonstrates the potential of multi-modal model in achieving
state-of-the-art performance (+8.4\% MCC, +5.5\% AUC compared to baselines) and
efficiently capturing contextual, physicochemical, and structural information
from amino acid residues. The performance of MATE-Pred projects its potential
application in various drug discovery regimes.

MATE-Pred 是一种高度可靠的新方法，通过多模式注意力机制预测 T 细胞受体和表位结合亲和力，展示了多模式模型在从氨基酸残基中高效捕捉上下文、物理化学和结构信息的潜力，以及在各种药物发现方案中的潜在应用。