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

doi:10.21203/rs.3.rs-3937337/v1

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

https://doi.org/10.21203/rs.3.rs-3937337/v1

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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: NH_69-83, SMT_133-148, CPA_39-54, CPA_301-316, CPB_42-57, and CPC_37-52. After alignment and molecular docking analysis, the most promising sequences were SMT_113-148 and CPA_39-54.

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.

Peptides

T-Lymphocyte

HLA-DR Antigens

Cutaneous leishmaniasis

Vaccines.

Leishmaniasis is a neglected protozoan disease transmitted to humans by sandflies. Worldwide there are between 0.9-1.6 million cases every year in 98 tropical and subtropical countries[1]. Cutaneous leishmaniasis (CL) is the most prevalent clinical manifestation of the disease, caused by several species belonging to the Viannia and Leishmania subgenus of the parasite. In the Americas occurs approximately 64,000 cases of leishmaniasis annually, and Brazil, Peru, and Colombia report the highest number of cases. In Colombia, an average of 7,481 cases were reported each year in the last decade, and the most prevalent species are Leishmania panamensis (L. panamensis), L. braziliensis, and L. guyanesis[1–3].

Leishmaniasis is a significant public worldwide health problem that causes an important economic impact because of treatment costs, healthcare services, and Disability-adjusted life years (DALYs)[4], therefore the development of an effective vaccine would be cost-effective compared with pharmacological treatment[5, 6]. However, currently, there are no vaccines licensed for use in humans, as many attempts have failed to scale from animal models to human clinical trials because of the complex immune response necessary to eliminate the parasite[7].

The Leishmania parasite survives and multiplies as intracellular amastigotes inside the phagolysosomes of macrophages in mammals, thus T helper (Th) cellular immune response is crucial for controlling the parasite. In murine models T-CD4+ Th1 profile cells are associated with resistance to leishmaniasis while the Th2 response is associated with susceptibility[5, 8], however in humans, there are mixed profile responses, but it is essential to trigger a predominance of Interferon γ (IFNγ) produced by Th1 lymphocytes, for the reactivation of Nitric Oxide (NO) inside the macrophage and, therefore, efficient phagocytosis of Leishmania amastigote[9, 10]. Considering the nature of the disease, identifying the sequences or epitopes presented by antigen-presenting cells (APCs) through HLA class II molecules such as HLA-DR to activate T-CD4⁺ lymphocytes, then those epitopes can be synthesized and evaluated as vaccine candidates for leishmaniasis[11].

In recent decades, the advancement of algorithm-based computational tools has allowed the creation of machines that "learn" based on the biological results of protein-protein interaction and can predict outcomes based on constantly fed algorithms. Specifically in the field of vaccines, some tools predict the binding of protein fragments to the pockets of class I and II antigen-presenting molecules based on the nature of the sequences and HLA variants. These bioinformatics advances have allowed the analysis of many potential epitopes, optimizing time and resources in research, and accelerating important discoveries for the development of vaccines against pathogens that depend on an effective cellular immune response [12, 13].

Consequently, this project aimed to identify possible T-CD4⁺ epitopes through bioinformatic analysis of binding prediction assessing amino acid linear sequences derived from immunogenic proteins of Leishmania spp. against several HLA-DR alleles applying different prediction algorithms simultaneously, and finally confirming the sequence-HLA-DR binding through molecular docking.

Protein selection: Leishmania proteins selected are proteins that have antigenicity and/or immunogenicity reports as recombinant proteins in animal models or humans, besides those proteins are highly conserved among Leishmania species, especially those causing new world cutaneous leishmaniasis. Five L. panamensis proteins were selected for this study: i) Nucleoside Hydrolase-NH- (XP_010697986.1); ii) Sterol 24-c-Methyltransferase -SMT (NCBI accession number XP_010703182.1); iii) Cysteine Peptidase A -CPA (XP_010698124.1); iv) Cysteine Peptidase B-CPB (ABX74953.1) and v) Cysteine Peptidase C-CPC (XP_010700690.1).

Epitope identification: Linear sequence of each protein was analyzed to identify T epitope regions (strong binder) for the following HLA-DR alleles frequent in Colombian population: HLA-DRB1*01:01, HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02, HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:03, HLA-DRB1*04:04, HLA-DRB1*04:05, HLA-DRB1*04:07, HLA-DRB1*04:11, HLA-DRB1*07:01, HLA-DRB1*08:02, HLA-DRB1*09:01, HLA-DRB1*10:01, HLA-DRB1*11:01, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA-DRB1*13:01, HLA-DRB1*13:02, HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*14:02, HLA-DRB1*15:01 and HLA-DRB1*16:02 [14–18]. All sequences compared with each allele were evaluated by three bioinformatic servers: NetMHCIIpan 4.0 (https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.0/)[19], MixMHC2pred (http://mixmhc2pred.gfellerlab.org/)[20, 21] and MARIA - MHC Analyzer with Recurrent Integrated Architecture- (https://maria.stanford.edu/about.php)[22]. Strong binder cutoff was set in %Rank ≤1 for the first two tools and ≥95% for the third one. Finally, sequences were identified as strong binders for several alleles by all the tools simultaneously and were selected for further analyses. Multiple alignments were made by MUSCLE -Multiple Sequence Comparison by Log- Expectation-( https://www.ebi.ac.uk/Tools/msa/muscle/)[23, 24].

MHC-class II protein selection and receptor preparation for molecular docking studies: The selection of MHC-class 2 was made according to the distribution in the Latin American population and the availability of the crystallographic structures in databases. HLA-DRB1-0401 (PDB:4MDJ) and proteins were used for docking. Using AutoDock Tools 1.5.6 the receptors (HLA molecules) were prepared by removing water molecules and specific ligands, while polar hydrogens and Kollman charges were added to the proteins.

Molecular Peptide-protein docking by MDoCkPEP and HPEPDOCK 2.0 tools: Docking between peptide sequences and MHC class 2 (4MDJ and 1A6A) was performed using MDoCkPEP server (https://zougrouptoolkit.missouri.edu/mdockpep/index.html) (Accessed 2023)[25] and HPEPDOCK 2.0 (http://huanglab.phys.hust.edu.cn/hpepdock/) (Accessed Jul-Oct2023)[26]. The best models generated by the tools were selected based on the interaction energy (Kj/mol). Visualization of molecular docking models and analysis of binding variables. To visualize the models generated by the molecular docking tools, Chimera version 1.17.3 and DS-studio 2021 were used. In which favorable, unfavorable, HBond, hydrophobic interaction, and salt bridge bonds were analyzed[27–30].

For the identification of possible T CD4⁺ epitopes from immunogenic Leishmania proteins, NH, SMT, CPA, CPB, and CPC using L. panamensis as a template, linear sequences were divided into 15mer peptides along the protein, for example, peptide from 1–15 position, peptide from 2–16 position and so on. An overall of 1 856 peptides were evaluated and each one was compared against 25 HLA-DR alleles by both NetMHCIIpan and MixMHC2pred and sequences identified as Strong Binders for several alleles by both tools (30 sequences), were then run by MARIA tool. A total of 93.500 predictions were made where most sequences did not bind to any allele by any tool, and some showed good results only by one of the three tools (results not shown). Table 1 shows the best T CD4⁺ epitopes predicted simultaneously by the three prediction tools where 13 sequences were found to be strong binders to several alleles and to have > 90% probability of being presented.

The %Rank score is a normalization between the prediction binding score of the peptide to the HLA-DR allele in the study in comparison with the prediction of a set of random peptides, it means, the lower the value, the stronger the binding, meanwhile when using MARIA, the results are expressed as the probability of that peptide of being presented by such HLA-DR allele, which means that higher the value, higher possibility or being a true epitope. In this study, the results show the difference between the tools, in terms of different alleles identified as strong binders, however, to select the best sequences, there were detected those which all the tools in common identified as promiscuous natural Leishmania TCD4⁺ cell epitopes in humans. This strategy allowed us to go from 1 856 to 13 promissory sequences to this point. Considering the cutoff values set and the need for the identification of promiscuous T-cell epitopes presented by HLA-DR class II molecules identified in common by three different prediction algorithms, 6 sequences were selected for further analysis (bold letters in Table 1).

Table 1

Predicted T CD4⁺ epitopes by bioinformatic tools.
#	Code	Peptide sequence	NetMHCIIpan		MixMHC2pred		MARIA
#	Code	Peptide sequence	HLA-DRβ allele as strong binder (%rank ≤ 1)	Average of %rank (min-max)	HLA-DRβ allele as strong binder (%rank ≤ 1)	Average of %rank (min-max)	HLA-DRβ allele with probability of being presented (≥ 95%)	Average of % probability (min-max)
1	NH_{69 − 83}	KPLVRKVRTAPQIHG	04:02 04:04, 04:03	7.83 (0.13–24.94)	04:07, 04:04, 04:03, 04:11, 04:01, 11:04, 04:02	9.80(0.009–47.3)	12:01, 13:01, 11:04, 14:02, 11:01, 01:01, 01:02, 14:01, 07:01, 04:02, 13:03, 10:01, 04:11, 13:02, 16:02, 04:04, 09:01, 15:01, 04:05, 04:03, 04:01, 03:01, 04:07, 08:02, 03:02	97.8 (97.3–98.6)
2	NH_{226 − 240}	TEVYETQRNTYAKVH	04:07, 04:03, 01:01, 04:01, 10:01, 16:02	4.04 (0.07-10)	04:01, 16:02.	14.51 (0.49–50.5)	-	92.2 (90.3–94.0)
3	NH_{244 − 258}	AVAYVIDPTVMTTNR	04:07, 03:01, 03:02, 04:01, 13:02, 13:03, 14:02	5.64 (0-20.82)	04:01, 13:03.	24.51 (0.004–79.6)	-	93.4 (92.0-94.5)
4	SMT_{133 − 148}	NNDYQITRARRHDAS	07:01, 08:02,	6.40 (0.4-16.57)	07:01	16.46 (0.38–42.7)	12:01, 13:01, 11:04, 14:02, 01:01, 11:01, 03:01, 01:02, 14:01, 04:02, 13:02, 07:01, 13:03, 04:11, 04:04, 10:01, 04:05, 04:03, 04:01, 16:02, 08:02, 15:01, 09:01, 04:07, 03:02	98.5 (98.2–99.1)
5	CPA_{39 − 54}	SAHFMHFKKQHGKSF	08:02, 11:01, 11:04	13.53 (0.33–28.63)	11:01, 13:01, 13:02.	22.70 (0.27–75.9)	11:04, 12:01, 13:01, 14:02, 11:01,	93.2 (90.6–95.8)
6	CPA_{72 − 87}	TAVYLNAQNPHAHYD	04:01, 04:03, 04:07, 16:02	5.39 (0.05–24.98)	04:01, 04:07, 16:02	10.65 (0.08–38.2)	-	91.1 (88.2–93.4)
7	CPA_{301 − 316}	KPPYWIVKNSWGTSW	04:01, 04:03, 04:04, 04:05, 04:07, 08:02, 16:02	5.46 (0.13–32.57)	04:01, 04:05, 16:02	28.92 (0.22–87.4)	12:01, 01:02, 01:01, 07:01, 13:01, 04:11, 14:02, 11:04, 04:04, 10:01, 11:01, 04:02, 09:01, 04:05, 04:03, 16:02, 04:01, 14:01, 15:01, 13:03, 13:02, 04:07, 08:02, 03:02, 03:01	98.8 (98.1–99.2)
8	CPB_{42 − 57}	KQTYKRVYATLAEEQ	01:01, 01:02, 04:01, 07:01, 08:02, 09:01, 10:01; 11:01, 16:02	6.73 (0.03–44.83)	01:01, 04:07, 16:02	19.47 (0.06–59.2)	01:01, 01:02, 12:01, 09:01, 07:01, 04:11, 10:01, 04:04, 04:01, 11:04, 04:07, 04:05, 13:01, 04:03, 14:02, 04:02, 11:01, 16:02, 14:01, 13:03, 08:02, 13:02, 15:01, 03:02.	96.5 (94.5–97.4)
9	CPB_{102 − 117}	ATHFAKAKKFASQHY	08:02, 11:01, 14:02	14.52 (0.09–34.99)	08:02, 11:01, 13:03	20.54 (0.27–71.7)	-	91.2 (88.5–93.8)
10	CPB_{113 − 128}	SQHYRKVGADLSTAP	04:01, 04:07, 10:01	10.57 (0.25–40.1)	04:07, 10:01.	26.11 (0.29–71.1)	-	94 (92.1–94.9)
11	CPB_{258 − 273}	NGPIAIAVDASAFMS	04:01, 04:02, 04:03, 04:04.	6.35 (0-19.8)	04:01, 04:02, 04:03, 04:04, 04:07, 04:11,	10.30 (0.005–47.8)	-	90.9 (89.1–92.9)
12	CPC_{37 − 52}	SNRFVAEINLKAKGQ	01:01, 01:02, 03:02, 04:01, 04:02, 04:03, 04:04, 04:05, 04:07, 04:11, 08:02, 10:01, 11:01, 13:02, 14:02, 16:02	1.43 (0-6.8)	01;01, 04;01, 04:04, 04:05, 04:07, 11:01, 16:02	6.06 (0.004–24.6)	01:01, 12:01, 01:02, 11:04, 13:01, 11:01, 14:02, 10:01, 09:01, 04:04, 04:01, 07:01, 04:02, 16:02, 04:11, 14:01, 04:07, 13:03, 04:05, 13:02, 04:03, 08:02, 15:01, 03:02, 03:01	97.5 (96.3–98.0)
13	CPC_{263 − 277}	YADFVSYKSGVYSHT	15:03, 16:02	9.86 (0.4–24.8)	13:02	21.40 (0.96–86.3)	01:01	93.8 (91.7–95.0)

The next step was to define the % of the identity of the sequences with different new world LC species, so we compared each peptide from the L. panamensis sequence with L. braziliensis and L. guanenesis, and with human homologous. Figure 1 shows that most peptides are 100 identical between Leishmania species assessed, except for CPA_{42 − 57} and CPB_{42 − 57} with 93.3% and 86.6% of identity respectively. Additionally, when sequences were compared to Human homolog, only CPA_{301 − 316} showed important identity (60%) with H. sapiens.

Figure 1

Multiple alignments between selected peptides and Leishmania species or human homologs.

Foot note Fig. 1. L.p: L. panamensis; L.b: L. braziliensis; L.g: L. guyanensis; H.s: Homo sapiens NSS: No significant similarity found, § Cathepsin AAC78838,1; † Cathepsin AAC78839,1; ‡ CathepsinB NP_001371643; An * (asterisk) indicates positions which have a single, fully conserved residue. A : (colon) indicates conservation between groups of strongly similar properties as below. A. (period) indicates conservation between groups of weakly similar properties as below. Red: Small (small + hydrophobic (incl. aromatic -Y)); Blue Acidic; Magenta: Basic – H. Green: Hydroxyl + sulfhydryl + amine + G.

To confirm the previous prediction results, molecular docking was performed comparing these 6 selected sequences against the pocket of HLA-DR4. Molecular docking was performed using the MDockPep server to simulate and analyze the interaction between the HLA-DR4 receptor and peptide sequences (ligand). Numerous docking structures (an average of 10 structures per peptide sequence) were generated, based mainly on binding energy (kJ/mol). The best models were chosen according to the lowest binding energy of each of the protein-peptide interactions, for each of the sequences.

The total binding energy (kJ.mol^− 1) is the result of the consensus of different intra- and intermolecular interactions between the peptide (ligand) and the DR protein (Receptor) and is used predictively to identify the peptide sequences with the highest affinity for the MHC-class II complex in the search and design of vaccines against various pathogens. In this sense, favorable interactions contribute to the binding strength of the pocket (Alpha and beta chain) of the DR4 protein and the peptide. These forces are modulated by the presence of hydrogen bonds (HB), which occur more frequently in biological systems contributing to the specificity of the binding and hydrophobic interactions, which play an important role in the stability of molecular complexes and the total binding energy. All these parameters were considered to order the peptide sequences according to their binding energy (kJ.mol^− 1).

The selected models showed interaction energy from − 185.22 Kj.mol^− 1 to -233.90 Kj.mol^− 1. Employing the DS Studio platform and Autodock tools, the intra and intermolecular interactions of the best models were visualized, showing the predominance of favorable interactions (15 to 33 interactions), hydrogen bridges (20 to 11 HB), and hydrophobic interactions (3 to 12 bonds). The lower presence of unfavorable interactions (0 to 9 interactions) and salt bridges (0 to 4 bridges) was visualized (Table 2).

Table 2

Molecular docking analysis between MHC class 2 (DR4) and selected peptide sequences of *Leishmania* spp.
Protein		Peptide	DR4 (Kj/mol)	favorable interaction DR4	unfavorable Interaction	HB total	Total Hydrophobic interaction	Salt bridge
NH	NH69-83	-191.88	29	3	13	10	2
SMT	SMT133-148	-233.90	33	6	16	12	2
CPA	CPA39-54	-229.14	21	2	11	8	0
CPA	CPA301-316	-228.65	28	1	18	8	2
CPB		CPB42-57	-210.19	27	9	16	8	0
CPC		CPC37-52	-185.22	20	5	11	9	1

Total binding Energy (kJ.mol^− 1). HB. Hydrogen bond. Data was obtained from the MDOCK-DStudio visualizer and AutoDock tools.

The analysis of intra and intermolecular interactions displayed that the lower binding energy of sequences was in the peptide-derived SMT protein (-233,90 kJ.mol^− 1), related to the number of favorable interactions and total hydrophobic interactions (Fig. 2). The peptide sequence interacts with several AAs of the A and B chains of MHC class II, with the presence of conventional hydrogen bonds (green circle), carbon bonds (orange circle), and hydrophobic bonds (pink circle) (Fig. 2A).

Figure 2. 2D diagram of Molecular docking protein-peptides.

Footnote Fig. 2. A. HLA-DR4 – SMT_{133 − 148} peptide. B. HLA-DR4-CPA_{39 − 54} .peptide. C.HLA-DR4- CPA_{301 − 316} peptide. D.HLA-DR4 – CPB_{42 − 57} peptide. E .HLA-DR4 – NH_{69 − 83} peptide. F. HLA-DR4-CPC_{37 − 52}. Intra and intermolecular interactions. Green. Conventional hydrogen bonds. Pink. Hydrophobic interactions. Orange. Carbon bonds. Red. Unfavorable bonds. Shadows. Solvent accessible surface.

The CPA protein sequences have binding energies like those of the SMT protein peptide. The 2D models show mostly conventional hydrogen bonds, followed by attraction and hydrophobic charges. More soluble accessible surfaces are also present (Fig. 2B-C). These results are consequent to the binding energies of sequences CPA39-54 (-229,14 kJ.mol-1) and CPA301-316 (-228,65 kJ.mol-1), and the few numbers of unfavorable interactions (Table 2).

Among the selected CPB protein sequences, it was observed that peptides 42–57 had binding energy (-210.19 kJ.mol-1). The analysis of inter- and intramolecular interactions showed the predominance of hydrogen bonds and hydrophobic bonds between peptides and proteins, as well as the low amount of carbon bonds and solvent-accessible surfaces (Fig. 2D). The 69–83 sequence of the NH protein also showed low binding energies (-191,88 kJ.mol-1), exhibiting the presence of favorable interactions, related to conventional hydrogen bonds, carbon bonds, and hydrophobic bonds within the peptide-protein complex (Fig. 2E).

The peptide sequence derived from the CPC protein showed binding energies of -182.22 kJ.mol-1 to MHC class II. This energy was the highest of those resulting from the other sequences analyzed, correlating with fewer numbers of favorable interactions (Table 2). The 2D analysis shows the presence of conventional hydrogen bridges, and hydrogen bridges, but little presence of carbon bridges; additionally, unfavorable interactions (red circle) and the absence of solvent-accessible surfaces are evident (Fig. 2F).

Protozoa vaccine have so far been elusive to humanity due to immune evasion by obligate intracellular parasites. Leishmania spp in addition, survives and multiplicate inside macrophages, the immune cell that is design to eliminate it. Several attempts have been made for decades, using first, second, and third vaccine generation, however, none of them have been licensed for use in humans[31].

With the rise of informatics, and machine learning, it was possible to “feed” machines with biological experiment results, and then be able to predict different outcomes through algorithms. Bioinformatic servers have been used to predict T epitopes by assessing the interaction between linear short sequences and the structure of the pocket of MHC molecules, considering that those strong binders may be presented by antigen-presenting cells -APCs[32].

In recent years, researchers working on the leishmaniasis vaccine have designed several chimeric antigens that include T and/or B epitopes based on bioinformatic tools for cutaneous leishmaniasis. Seyed et. al., 2011 designed CD8-T cell restricted epitopes from CPB, CPC, Sti1, TSA, Leif, and LPG-3 proteins from L. major, and pools from some sequences predicted then showed IFN-γ induction in human Peripheral Blood Mononuclear Cell -PBMCs- in vitro[33]. Other studies have constructed polytopes made of T epitopes or a combination of T and B-predicted epitopes from different L. major intracellular and membrane proteins (TSA, LPG3, GP63, STI1, LACK, SMT, LEIF, KMP11, and HASPB), that showed high predicted antigenicity besides non-allergenic properties according to in silico results[34–36]. Another group of studies has been directed to analyze visceral leishmaniasis species such as L. donovani T and B multiepitope through bioinformatics [37] and researchers that included simultaneously LC and LV species [38].

T epitope prediction tools allow to performance of high numbers of predictions; however, each server is based on different algorithms, so it is not clear which one performs closer to reality. To overcome this issue, in this study, three different servers (based on different algorithms) were used, and the sequences selected were those the three tools predict simultaneously. Additionally, to improve epitope screening molecular docking analyses were also considered to identify the best epitopes because it shows in more depth the intra- and intermolecular interactions that mediate the binding and specificity between the peptide sequences and the MHC class II immune receptor.

Immunoinformatic represents a great approach in vaccinology research because it allows to screen of a large number of proteins and sequences through rational design that leads to the control of different infectious and/or immune diseases, saving time, effort, and even research budget in the initial development stages. However, without the following in vitro and in vivo proofs of their utility, it would not be possible to know the real scope of bioinformatic strategies.

In conclusion, the development of this study allowed the analysis of 1 856 peptides and after a sequential and rational step-by-step screening, candidate numbers were finally 2, SMT_{113 − 148} and CPA_{39 − 54,} sequences that will be synthesized and prove their immunogenicity in vitro in human PBMCs to validate its utility as prophylactic or therapeutic alternative options to fight leishmaniasis.

Acknowledgment: This project was possible thanks to the support of Universidad Cooperativa de Colombia, General call, project code 3340.

Competing Interests: The authors declare that there were not competing interests in the development of the present study.

Author Contribution

MMF, DL, WDM, KR and YU run and analysed first prediction servers. KR run MARIA server. FET developed molecular docking. MMF, FET analysed general results. MMF and FET wrote the main manuscript text. MMF prepare figure 1. FET prepare figure 2. All authors discussed results. All autohrs reviewed the manuscript.

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

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