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 Leishmania 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.

Asymptomatic Leishmania infantum, when associated with HIV, can become severe and potentially fatal. In this co-infection, the worst prognosis may be influenced by the host’s immunological aspects, which are crucial in determining susceptibility. Chemokines play an important role in this process by influencing the cellular composition at affected sites and impacting the disease’s outcome. Therefore, the aim of this study was to evaluate proinflammatory chemokines in HIV patients with the asymptomatic L. infantum infection. In this cross-sectional study, the levels of CCL2, CCL5, CXCL8, MIG, and IP-10 were measured in 160 serum samples from co-infected patients (n = 53), patients with HIV (n = 90), and negative controls (n = 17). Quantification was determined by flow cytometry. The obtained data were statistically analyzed using the Kruskal–Wallis test, followed by the Dunn’s post-test and the Spearman’s correlation coefficient. Significance was set at p < 0.05. The chemokines CCL2, CCL5, MIG, and IP-10 exhibited higher levels in the HIV group compared to co-infection. However, the elevated levels of all these chemokines and their increased connectivity in co-infected patients appear to be important in identifying proinflammatory immune responses associated with the asymptomatic condition. Furthermore, a weak negative correlation was observed between higher levels of CXCL8 and lower viral loads in co-infected patients.