Table 3_Design of cross-reactive antigens with machine learning and high-throughput experimental evaluation.xlsx

Selecting an optimal antigen is a crucial step in vaccine development, significantly influencing both the vaccine’s effectiveness and the breadth of protection it provides. High antigen sequence variability, as seen in pathogens like rhinovirus, HIV, influenza virus, complicates the design of a single cross-protective antigen. Consequently, vaccination with a single antigen molecule often confers protection against only a single variant. In this study, machine learning methods were applied to the design of factor H binding protein (fHbp), an antigen from the bacterial pathogen Neisseria meningitidis. The vast number of potential antigen mutants presents a significant challenge for improving fHbp antigenicity. Moreover, limited data on antigen-antibody binding in public databases constrains the training of machine learning models. To address these challenges, we used computational models to predict fHbp properties and machine learning was applied to select both the most promising and informative mutants using a Gaussian process (GP) model. These mutants were experimentally evaluated to both confirm promising leads and refine the machine learning model for future iterations. In our current model, mutants were designed that enabled the transfer of fHbp v1.1 specific conformational epitopes onto fHbp v3.28, while maintaining binding to overlapping cross-reactive epitopes. The top mutant identified underwent biophysical and x-ray crystallographic characterization to confirm that the overall structure of fHbp was maintained throughout this epitope engineering experiment. The integrated strategy presented here could form the basis of a next-generation, iterative antigen design platform, potentially accelerating the development of new broadly protective vaccines.筛选最优抗原是疫苗研发的关键环节，其对疫苗的效力与所提供的保护广度均具有显著影响。如鼻病毒、人类免疫缺陷病毒（Human Immunodeficiency Virus, HIV）、流感病毒等病原体所呈现的高抗原序列变异性，会给单一交叉保护性抗原的设计带来极大挑战。因此，仅使用单一抗原分子进行免疫接种，通常仅能针对单一毒株变体提供保护。本研究将机器学习方法应用于因子H结合蛋白（factor H binding protein, fHbp）的设计，该蛋白是细菌病原体脑膜炎奈瑟菌（Neisseria meningitidis）的一种抗原。数量庞大的潜在抗原突变体，给提升fHbp的抗原性带来了显著挑战。此外，公共数据库中抗原-抗体结合相关数据的匮乏，限制了机器学习模型的训练。为应对这些挑战，我们借助计算模型预测fHbp的相关特性，并采用高斯过程（Gaussian process, GP）模型，通过机器学习方法筛选出最具应用前景且信息价值最高的突变体。随后对这些突变体开展实验评估，一方面验证具有应用潜力的候选对象，另一方面为后续迭代优化机器学习模型提供支撑。在本研究构建的模型中，我们设计出一类突变体，可将fHbp v1.1特有的构象表位移植至fHbp v3.28中，同时保留其与重叠交叉反应性表位的结合能力。研究筛选出的最优突变体，接受了生物物理与X射线晶体学表征，结果证实，在本次表位工程实验全程中，fHbp的整体结构未发生改变。本研究提出的整合策略，可作为下一代迭代式抗原设计平台的核心框架，有望加速新型广谱保护性疫苗的研发进程。