Li Qianying, Zhao Yanmin, Chordia Mahendra D, Xia Xiuming, Zhang Bo, Zheng Heping
Hunan University College of Biology, Changsha, Hunan 410082, China.
Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China; Shenzhen Tributary Biologics LLC, Shenzhen, Guangdong 518000, China.
Hum Immunol. 2025 May;86(3):111304. doi: 10.1016/j.humimm.2025.111304. Epub 2025 Apr 5.
The binding interface between antigens and antibodies is pivotal in humoral immune responses and provides crucial effective defense against pathogens and exogenous threats. Existing predictive computational methodologies, including structure-based and sequence-based approaches, offer valuable insights but face challenges such as unknown antigen structures and reliance on manually curated features. Most current methods primarily predict antigen epitope, often neglecting the specific molecular epitope-paratope interactions essential for immune efficacy. In this study, we introduce a novel approach EPP (Epitope-Paratope Predictor), using the ESM-2 protein language model as a feature encoder and a Bi-LSTM network to predict epitope-paratope interactions. Our method processes antigen and antibody sequences as inputs, leveraging a novel dataset strategy and encoding protein representations to enhance prediction accuracy. The results demonstrate a significant improvement in prediction accuracy compared to existing methods, highlighting the importance of protein feature encoder and temporal dependencies within sequences. The model's performance in different antigen clusters is analyzed, while those predictions are compared with that from AlphaFold3 and Dock method. Our method validation shows superior performance in recognizing distinctive epitopes of the same antigen when bound to different antibodies. This approach offers a new strategy for an in-depth understanding of antigen-antibody interactions, essential for an array of pioneer projects, such as structure-guided design and affinity maturation for precision antibodies targeting a given epitope.
抗原与抗体之间的结合界面在体液免疫反应中起着关键作用，并为抵御病原体和外部威胁提供至关重要的有效防御。现有的预测计算方法，包括基于结构和基于序列的方法，虽提供了有价值的见解，但面临诸如抗原结构未知以及依赖人工整理特征等挑战。当前大多数方法主要预测抗原表位，常常忽略了对免疫功效至关重要的特定分子表位 - 互补位相互作用。在本研究中，我们引入了一种新颖的方法EPP（表位 - 互补位预测器），使用ESM - 2蛋白质语言模型作为特征编码器，并通过双向长短期记忆网络来预测表位 - 互补位相互作用。我们的方法将抗原和抗体序列作为输入，利用一种新颖的数据集策略并对蛋白质表示进行编码，以提高预测准确性。结果表明，与现有方法相比，预测准确性有显著提高，突出了蛋白质特征编码器和序列内时间依赖性的重要性。分析了该模型在不同抗原簇中的性能，同时将这些预测结果与AlphaFold3和对接方法的预测结果进行比较。我们的方法验证表明，在识别同一抗原与不同抗体结合时的独特表位方面具有卓越性能。这种方法为深入理解抗原 - 抗体相互作用提供了一种新策略，这对于一系列前沿项目至关重要，例如针对给定表位的精密抗体的结构导向设计和亲和力成熟。
