Zaman Naila, Gul Kainat, Khurram Kinza, Azam Syed Sikander
Computational Biology Lab, National Center for Bioinformatics, Quaid-i- Azam University, Islamabad, 45320, Pakistan.
Sci Rep. 2025 Jul 1;15(1):22316. doi: 10.1038/s41598-025-03536-0.
Haemophilus influenza, a major contributor to respiratory infections such as pneumonia, meningitis, sinusitis, chronic bronchitis, and acute otitis, poses a significant public health challenge, driven by rising antibiotic resistance particularly among the non-typeable H. influenza (NTHi) strains given their ability to evade immune surveillance. To address this, we employed a comprehensive immunoinformatics pipeline integrated with extensive pan-genome analysis of 59 strains of H. influenzae to design a novel multiepitope vaccine (MEV) candidate targeting most virulent and clinically significant proteins. Key surface exposed and virulence associated proteins, including Protein E, PilA, Protein D, P4, TolC, YadA, and HifC were prioritized based on their roles in bacterial adhesion, immune evasion, biofilm formation, and nutrient acquisition. Advanced in silico epitope prediction and verification strategies were utilized to map highly immunogenic regions across these proteins, followed by codon optimization to enhance expression efficiency in human systems. To further stabilize the vaccine construct, we performed disulfide engineering to enhance structural integrity and resilience. Comprehensive validation through in silico immune simulations, molecular dynamics (MD) simulations and binding free energy calculations confirmed the structural stability, immunogenic potential, and strong receptor affinity of the MEV candidate. Phylogenetic and virulence factor analysis further corroborated the broad coverage of the pathogenic relevance of the selected proteins. Together, our integrative approach presents a robust pipeline for rational vaccine design, offering a promising avenue toward combating multidrug resistant and immune evasive H. influenza strains.
流感嗜血杆菌是导致肺炎、脑膜炎、鼻窦炎、慢性支气管炎和急性中耳炎等呼吸道感染的主要病原体,它带来了重大的公共卫生挑战。特别是不可分型流感嗜血杆菌(NTHi)菌株具有逃避免疫监视的能力,导致抗生素耐药性不断上升,加剧了这一挑战。为应对这一问题,我们采用了综合免疫信息学流程,并结合对59株流感嗜血杆菌进行的广泛全基因组分析,设计了一种新型多表位疫苗(MEV)候选物,该候选物针对的是最具毒性和临床意义的蛋白质。根据关键表面暴露蛋白和毒力相关蛋白在细菌黏附、免疫逃避、生物膜形成和营养获取中的作用,对包括蛋白E、菌毛蛋白A、蛋白D、P4、外膜孔蛋白C、外膜蛋白A和HifC等进行了优先级排序。利用先进的计算机模拟表位预测和验证策略,绘制这些蛋白质上的高免疫原性区域,随后进行密码子优化,以提高在人体系统中的表达效率。为进一步稳定疫苗构建体,我们进行了二硫键工程,以增强结构完整性和弹性。通过计算机模拟免疫、分子动力学(MD)模拟和结合自由能计算进行的全面验证,证实了MEV候选物的结构稳定性、免疫原性潜力和强大的受体亲和力。系统发育和毒力因子分析进一步证实了所选蛋白质在致病性方面的广泛覆盖。总之,我们的综合方法为合理的疫苗设计提供了一个强大的流程,为对抗多重耐药和免疫逃避的流感嗜血杆菌菌株提供了一条有前景的途径。