Medicinal Theoretical Chemistry Laboratory, Department of Pharmaceutical Sciences, Health Sciences Center, Universidade Federal de Pernambuco, Recife 50670-901, PE, Brazil.
Immunopathology and Molecular Biology Laboratory, Departament of Immunology, Aggeu Magalhaes Institute, Recife 50740-465, PE, Brazil.
Molecules. 2024 Nov 7;29(22):5259. doi: 10.3390/molecules29225259.
Significant populations in tropical and sub-tropical locations all over the world are severely impacted by a group of neglected tropical diseases called leishmaniases. This disease is caused by roughly 20 species of the protozoan parasite from the genus. Disease prevention strategies that include early detection, vector control, treatment of affected individuals, and vaccination are all essential. The diagnosis is critical for selecting methods of therapy, preventing transmission of the disease, and minimizing symptoms so that the affected individual can have a better quality of life. Nevertheless, the diagnostic methods do eventually have limitations, and there is no established gold standard. Some disadvantages include the existence of cross-reactions with other species, and limited sensitivity and specificity, which are mostly determined by the type of antigen used to perform the tests. A viable alternative for a more precise diagnosis is the application of recombinant antigens, which have been generated using bioinformatics approaches and have shown increased diagnostic accuracy. This approach proves valuable as it spans from epitope selection to predicting the interactions within the antibody-antigen complex through docking analysis. As a result, identifying potential new antigens using bioinformatics resources becomes an effective technique since it may result in an earlier and more accurate diagnosis. Consequently, the primary aim of this review is to conduct a comprehensive overview of the most significant in silico tools developed over time, with a focus on evaluating their efficacy and exploring their potential applications in optimizing the selection of highly specific molecules for a more effective diagnosis of leishmaniasis.
在世界上的热带和亚热带地区，大量人群受到一组被称为利什曼病的被忽视热带病的严重影响。这种疾病是由 属中的大约 20 种原生动物寄生虫引起的。包括早期检测、病媒控制、受影响个体的治疗和疫苗接种在内的疾病预防策略都是至关重要的。诊断对于选择治疗方法、预防疾病传播和减轻症状至关重要，以使受影响的个体能够拥有更好的生活质量。然而，诊断方法最终确实存在局限性，并且没有既定的金标准。一些缺点包括与其他物种的交叉反应，以及敏感性和特异性有限，这主要取决于用于进行测试的抗原类型。一种更精确诊断的可行替代方法是应用重组抗原，这些抗原是使用生物信息学方法生成的，并且已经显示出提高的诊断准确性。这种方法很有价值，因为它涵盖了从表位选择到通过对接分析预测抗体-抗原复合物内的相互作用。因此，使用生物信息学资源来识别潜在的新抗原成为一种有效的技术，因为它可能导致更早和更准确的诊断。因此，本综述的主要目的是对随着时间的推移开发的最重要的计算工具进行全面概述，重点评估它们的功效，并探索它们在优化选择高度特异性分子以更有效地诊断利什曼病方面的潜在应用。
