Wong Kenny, Subramanian Ishaan, Stevens Emma, Chakraborty Srirupa
Department of Chemical Engineering, Northeastern University, Boston, MA.
Department of Bioengineering, Northeastern University, Boston, MA.
bioRxiv. 2025 Mar 14:2025.03.11.642737. doi: 10.1101/2025.03.11.642737.
Viral antigen-antibody (Ag-Ab) interactions shape immune responses, drive pathogen neutralization, and inform vaccine strategies. Understanding their structural basis is crucial for predicting immune recognition, optimizing immunogen design to induce broadly neutralizing antibodies (bnAbs), and developing antiviral therapeutics. However, curated structural benchmarks for viral Ag-Ab interactions remain scarce. To address this, we present (Viral Antibody-antigen Structural COmplex dataset), a high-resolution, non-redundant collection of ~1225 viral Ag-Ab complexes sourced from the Protein Data Bank (PDB) and refined via energy minimization. Spanning Coronaviruses, Influenza, Ebola, HIV, and others, VASCO provides a comprehensive structural reference for viral immune recognition. By comparing VASCO against general protein-protein interactions (GPPI), we identify distinct sequence and structural features that define viral Ag-Ab binding. While conventional descriptors show broad similarities across datasets, deeper analyses reveal key sequence-space interactions, secondary structure preferences, and manifold-derived latent features that distinguish viral complexes. These insights highlight the limitations of GPPI-trained predictive models and the need for specialized computational frameworks. VASCO serves as a critical resource for advancing viral immunology, improving predictive modeling, and guiding immunogen design to elicit protective antibody responses. By bridging sequence and structural immunological datasets, VASCO should enable better docking, affinity prediction, and antiviral therapeutic development-key to pandemic preparedness and emerging pathogen response.
病毒抗原 - 抗体（Ag - Ab）相互作用塑造免疫反应、驱动病原体中和并为疫苗策略提供依据。了解其结构基础对于预测免疫识别、优化免疫原设计以诱导广泛中和抗体（bnAbs）以及开发抗病毒疗法至关重要。然而，关于病毒Ag - Ab相互作用的精选结构基准仍然稀缺。为了解决这一问题，我们展示了（病毒抗体 - 抗原结构复合物数据集），这是一个高分辨率、非冗余的集合，包含约1225个源自蛋白质数据库（PDB）并通过能量最小化进行优化的病毒Ag - Ab复合物。涵盖冠状病毒、流感病毒、埃博拉病毒、HIV等，VASCO为病毒免疫识别提供了全面的结构参考。通过将VASCO与一般蛋白质 - 蛋白质相互作用（GPPI）进行比较，我们确定了定义病毒Ag - Ab结合的独特序列和结构特征。虽然传统描述符在各数据集中显示出广泛的相似性，但更深入的分析揭示了区分病毒复合物的关键序列空间相互作用、二级结构偏好以及源自流形的潜在特征。这些见解突出了GPPI训练的预测模型的局限性以及对专门计算框架的需求。VASCO作为推进病毒免疫学、改进预测建模以及指导免疫原设计以引发保护性抗体反应的关键资源。通过连接序列和结构免疫学数据集，VASCO应能实现更好的对接、亲和力预测以及抗病毒治疗开发，这对于大流行防范和应对新兴病原体至关重要。
