Binding predictions and molecular docking as a computational approach to identify human T CD4 epitopes from Leishmania proteins

Abstract

Abstract Leishmaniasis is an important public health problem caused by a protozoan parasite and distributed in 98 countries worldwide. Leishmania can causes from skin ulcers to complex visceral involvement, and treatment options available for humans have high toxicity and prolonged application schemes, therefore low treatment adhesion. So far there are not licensed vaccines for humans so is necessary to develop a strategy that can improve treatment options or that can prevent the onset of the disease. To eliminate intracellular Leishmania amastigotes inside macrophage, a cellular immune response of CD4+ Th1 profile is essential, therefore the identification of sequences that binds strong to HLA class II pockets are good candidates to induce a protective immune response against Leishmania spp. The aim of this study was to identify T CD4+ epitopes from immunogenic Leishmania proteins. Methodology: First, three prediction tools were used as screening comparing the 15mer sequences along the complete protein sequence against 25 HLA-DR alleles employing NH, SMT, CPA, CPB, and CPC proteins. Second, molecular docking was run for the best candidates. Results: 6 peptides were identified as HLA-DR strong binders simultaneously from the three bioinformatic prediction tools: NH69-83, SMT133-148, CPA39-54, CPA301-316, CPB42-57, and CPC37-52. After alignment and molecular docking analysis, the most promising sequences were SMT113-148 and CPA39-54. Conclusion: This bioinformatic strategy allowed a sequential screening from 1 857 possible peptides to 2 promising candidates, raising the probability of these sequences being natural T CD4+ Leishmania spp. epitopes in humans, therefore good candidates to be evaluated in further studies.