Background: Stenotrophomonas maltophilia is an aerobic, non-fermentative, gram negative, multidrug resistant and opportunistic nosocomial pathogen. It is associated with high morbidity and mortality in severely immunocompromised paediatric patients, including neonates. Immunoinformatic analysis paved a new way to design epitope-based vaccines which resulted in a potential immunogen with advantages such as lower cost, specific immunity, ease of production, devoid of side effects, and less time consumption than conventional vaccines. Till date, there is no development in the vaccines or antibody-based treatments for S. maltophilia-associated infections.

Introduction: Currently, epitope-based peptide vaccines against pathogenic bacteria have grasped more attention. In our present study, we have utilized various immunoinformatic tools to find a prominent epitope that interacts with the maximum number of HLA alleles and also with the maximum population coverage for developing a vaccine against Stenotrophomonas maltophilia.

Methods: This study has incorporated an immunoinformatic based screening approach to explore potential epitope-based vaccine candidates in Stenotrophomonas maltophilia proteome. In this study, 4365 proteins of the Stenotrophomonas maltophilia K279a proteome were screened to identify potential antigens that could be used as a good candidate for the vaccine. Various immunoinformatic tools were used to predict the binding of the promiscuous epitopes with Major Histocompatibility Complex (MHC) class I molecules. Other properties such as allergenicity, physiochemical properties, adhesion properties, antigenicity, population coverage, epitope conservancy and toxicity were analysed for the predicted epitope.

Results: This study helps in finding the prominent epitope in Stenotrophomonas infections. Hence, the main objective in this research was to screen complete Stenotrophomonas maltophilia proteome to recognize putative epitope candidates for vaccine design. Using computational vaccinology and immunoinformatic tools approach, several aspects are obligatory to be fulfilled by an epitope to be considered as a vaccine candidate. Our findings were promising and showed that the predicted epitopes were non-allergenic and fulfilled other parameters required for being a suitable candidate based on certain physio-chemical, antigenic and adhesion properties.

Conclusion: The epitopes LLFVLCWPL and KSGEGKCGA have shown the highest binding score of −103 and −78.1 kcal/mol with HLA-A*0201 and HLA-B*0702 MHC class I allele, respectively. They were also predicted to be immunogenic and non-allergenic. Further various immunological tests, both in vivo and in vitro methods, should be performed for finding the efficiency of the predicted epitope in the development of a targeted vaccine against Stenotrophomonas maltophilia infection.

背景：嗜麦芽窄食单胞菌是一种需氧、非发酵、革兰氏阴性、多重耐药性和机会性医院内病原体。它与严重免疫功能低下的儿科患者（包括新生儿）的高发病率和死亡率有关。免疫信息学分析为设计基于表位的疫苗开辟了一条新途径，它产生了一种潜在的免疫原，具有成本低、特异性免疫、易于生产、无副作用和比传统疫苗耗时少等优点。迄今为止，尚无针对嗜麦芽窄食单胞菌相关感染的疫苗或基于抗体的治疗方法的开发。

引言：目前，针对病原菌的基于表位的多肽疫苗受到越来越多的关注。在我们目前的研究中，我们利用各种免疫信息学工具来寻找一个突出的表位，该表位与最大数量的 HLA 等位基因相互作用，并具有最大的人群覆盖率，以开发针对嗜麦芽窄食单胞菌的疫苗。

方法：本研究采用了基于免疫信息学的筛选方法，以探索嗜麦芽窄食单胞菌蛋白质组中潜在的基于表位的候选疫苗。在这项研究中，对嗜麦芽窄食单胞菌 K279a 蛋白质组的 4365 种蛋白质进行了筛选，以确定可用作疫苗的良好候选者的潜在抗原。使用各种免疫信息学工具来预测混杂表位与主要组织相容性复合体 (MHC) I 类分子的结合。针对预测的表位分析了其他特性，例如过敏性、理化特性、粘附特性、抗原性、群体覆盖率、表位保守性和毒性。

结果：这项研究有助于发现在狭养单胞菌感染中的突出表位。因此，本研究的主要目标是筛选完整的嗜麦芽窄食单胞菌蛋白质组，以识别用于疫苗设计的推定候选表位。使用计算疫苗学和免疫信息学工具方法，几个方面必须由被视为疫苗候选者的表位实现。我们的研究结果是有希望的，并表明预测的表位是非过敏性的，并且满足作为基于某些物理化学、抗原性和粘附特性的合适候选者所需的其他参数。

结论：表位 LLFVLCWPL 和 KSGEGKCGA 与 HLA-A*0201 和 HLA-B*0702 MHC I 类等位基因的结合得分最高，分别为 -103 和 -78.1 kcal/mol。还预测它们具有免疫原性和非过敏性。应该进行进一步的各种免疫学测试，包括体内和体外方法，以发现预测表位在开发针对嗜麦芽窄食单胞菌感染的靶向疫苗中的效率。