蛋白质优化设计与从头合成引领的疫苗研发革命

doi:10.12211/2096-8280.2025-068

摘要/Abstract

摘要：

疫苗作为防控传染性疾病的有力手段，在发展应用过程中经历了四次重要革命。近年来计算工具的迅猛发展更是推动疫苗研发走向新的阶段，形成以结构为导向，蛋白质优化与计算设计为核心的合成生物学研究范式。本文系统介绍了蛋白质优化设计中定向进化、半理性设计和理性设计三种策略与从头合成技术在疫苗研发中的应用与价值。在免疫原设计层面，介绍了结构稳定性改造、表位聚焦、糖基化修饰调控等策略对提升抗原免疫原性与广谱性等特性方面的潜力。在递送系统层面，介绍了蛋白纳米颗粒凭借高密度抗原展示与几何构象优势，结合“马赛克”多价展示技术，对诱导交叉中和抗体生成方面的优势。人工智能计算工具的突破性进展实现了从“结构模拟”到“功能定制”的转变，并极大地推动了以抗原-抗体复合物结构为导向的反向疫苗学的发展。整合表位计算筛选与从头蛋白骨架设计，实现了疫苗从天然结构到定制结构的突破。尽管面临高变异病原广谱保护、动态构象模拟等挑战，疫苗设计与计算工具的深度融合加速了新型冠状病毒、呼吸道合胞病毒等疫苗的临床转化，并为未来新发和突发传染病防控提供了通用设计方法。

关键词: 蛋白质优化, 从头合成, 疫苗设计, 纳米颗粒疫苗, 反向疫苗学

Abstract:

Vaccines, as a cornerstone of infectious disease prevention and control, have undergone four transformative revolutions throughout their development and application. In recent years, the rapid advancement of computational technologies has further propelled vaccine development into a new era, giving rise to a synthetic biology paradigm centered on structure-guided protein optimization and computational design. This article systematically reviews the applications and significance of three key protein optimization strategies—directed evolution, semi-rational design, and rational design—as well as de novo protein synthesis, with a focus on their roles in vaccine development. At the immunogen design level, strategies such as structural stabilization, epitope focusing, and glycosylation modulation are discussed for their potential to enhance antigen immunogenicity and broaden protective efficacy. At the delivery system level, the unique advantages of protein nanoparticles in eliciting cross-neutralizing antibody responses are emphasized. These nanoparticles utilize high-density antigen presentation and precise geometric conformations, combined with "mosaic" multivalent display technology. Advances in artificial intelligence (AI)-based computational tools have facilitated a paradigm shift from "structural simulation" to "functional customization", thereby significantly promoting the development of structure-guided reverse vaccinology based on antigen-antibody complex structures. The integration of computational epitope screening and de novo protein backbone design has facilitated a transition from natural structures to customized designs in vaccine development. Although challenges remain, such as achieving broad-spectrum protection against highly variable pathogens and accurately simulating dynamic conformations, the deep synergy between vaccine design and computational tools has significantly accelerated the clinical translation of vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and respiratory syncytial virus (RSV), and established a universal design framework for the prevention and control of future emerging and unknown infectious diseases.

Key words: protein optimization, de novo design, vaccine design, nanoparticle vaccine, reverse vaccinology

中图分类号:

Q816

陈涛, 赖锦涛, 胡美林, 马显才. 蛋白质优化设计与从头合成引领的疫苗研发革命[J]. 合成生物学, DOI: 10.12211/2096-8280.2025-068.

CHEN Tao, LAI Jingtao, HU Meilin, MA Xiancai. Revolution in vaccine development led by protein optimization design and de novo synthesis[J]. Synthetic Biology Journal, DOI: 10.12211/2096-8280.2025-068.

图/表 3

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计算软件	开发时间	主要用途	核心特点
MODELLER^{[54, 55]}	1993年	基于同源建模的蛋白质三维结构预测，优化侧链构象。	依赖已知同源模板，引入空间约束进行优化，适用于结构补全与局部优化。
Rosetta^[56]	1998年	已知蛋白质稳定性增强、活性位点改造，全新结构蛋白的构建、功能位点设计。	基于物理力场与蒙特卡洛搜索，支持大规模构象采样，是蛋白质从头设计的里程碑式工具。
FoldX^[57]	2005年	快速评估点突变对蛋白质稳定性、结合亲和力的影响。	经验力场结合机器学习，适用于高通量虚拟突变筛选。
D-I-TASSER^{[58, 59]}	2008年	基于模板的蛋白质结构预测与功能注释，间接支持优化设计。	整合多重线程算法和分子动力学优化，提供从结构到功能的综合分析。
AlphaFold^[60]	2018年	高精度蛋白质结构预测。	首次将深度学习大规模应用于结构预测，显著提升精度。
AlphaFold 2^[60]	2020年	革命性的高精度蛋白质单链及复合物结构预测。	基于注意力机制的端到端模型，预测精度接近实验水平，开源后推动结构生物学变革。
RoseTTAFold^[61]	2021年	结构预测与部分设计功能，支持蛋白质-蛋白质复合物建模。	三轨神经网络（序列-结构-进化信息协同处理），计算效率高，可建模蛋白质-蛋白质相互作用。
ProteinMPNN^[62]	2022年	蛋白质序列设计，根据目标结构生成最优氨基酸序列。	基于图神经网络，设计速度更高，支持对称性设计和功能位点约束。
RFdiffusion^[36]	2023年	从头生成功能性蛋白质结构。	基于扩散模型生成三维结构，可直接融入功能约束，开创设计新范式。
AlphaFold3^[63]	2024年	预测蛋白质与核酸、配体、修饰等的复合结构，支持更广泛的分子相互作用建模。	统一框架处理蛋白质+生物分子复合系统，显著提升配体结合位点预测准确性，推动药物设计发展。

计算软件	开发时间	主要用途	核心特点
MODELLER^{[54, 55]}	1993年	基于同源建模的蛋白质三维结构预测，优化侧链构象。	依赖已知同源模板，引入空间约束进行优化，适用于结构补全与局部优化。
Rosetta^[56]	1998年	已知蛋白质稳定性增强、活性位点改造，全新结构蛋白的构建、功能位点设计。	基于物理力场与蒙特卡洛搜索，支持大规模构象采样，是蛋白质从头设计的里程碑式工具。
FoldX^[57]	2005年	快速评估点突变对蛋白质稳定性、结合亲和力的影响。	经验力场结合机器学习，适用于高通量虚拟突变筛选。
D-I-TASSER^{[58, 59]}	2008年	基于模板的蛋白质结构预测与功能注释，间接支持优化设计。	整合多重线程算法和分子动力学优化，提供从结构到功能的综合分析。
AlphaFold^[60]	2018年	高精度蛋白质结构预测。	首次将深度学习大规模应用于结构预测，显著提升精度。
AlphaFold 2^[60]	2020年	革命性的高精度蛋白质单链及复合物结构预测。	基于注意力机制的端到端模型，预测精度接近实验水平，开源后推动结构生物学变革。
RoseTTAFold^[61]	2021年	结构预测与部分设计功能，支持蛋白质-蛋白质复合物建模。	三轨神经网络（序列-结构-进化信息协同处理），计算效率高，可建模蛋白质-蛋白质相互作用。
ProteinMPNN^[62]	2022年	蛋白质序列设计，根据目标结构生成最优氨基酸序列。	基于图神经网络，设计速度更高，支持对称性设计和功能位点约束。
RFdiffusion^[36]	2023年	从头生成功能性蛋白质结构。	基于扩散模型生成三维结构，可直接融入功能约束，开创设计新范式。
AlphaFold3^[63]	2024年	预测蛋白质与核酸、配体、修饰等的复合结构，支持更广泛的分子相互作用建模。	统一框架处理蛋白质+生物分子复合系统，显著提升配体结合位点预测准确性，推动药物设计发展。