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/v2

Download PDF

Research Article

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/v2

This work is licensed under a CC BY 4.0 License

Version 2

posted

You are reading this latest preprint version

Background: So far there are not licensed leishmania vaccines for humans so is necessary to develop a strategy that 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.

Methods: 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 and finally immune response predictions was run for the selection of 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. Molecular docking showed that those sequences bind to HLA-DRβ*04:01 pocket however some peptides bonded in a reverse way. Finally, 4 of them induced pro-inflammatory cytokines, while the other 2 showed anti-inflammatory profile.

Conclusion: This bioinformatic strategy allowed a sequential screening from 1 857 possible peptides to 4 promising candidates, raising the probability of these sequences being natural T CD4+ Leishmania spp. epitopes in humans. SMT133-148, NH69-83, CPA39-54 and CPA301-316 seems to be a good vaccine candidate to be tested in further in vitro assays.

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 Americas approximately 64,000 cases of leishmaniasis are reported annually, mainly in Brazil, Peru, and Colombia. 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 an important worldwide public health problem that causes economic impact because of treatment high 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 needed to eliminate the parasite inside the macrophage(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 parasite control. 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, however it has been reported that a predominance of Interferon γ (IFNγ) produced by Th1 lymphocytes is essential for the synthesis of Nitric Oxide (NO) by macrophages and, therefore, efficient phagocytosis of Leishmania amastigote(9,10). Th1 epitopes are those short linear sequences that antigen-presenting cells (APCs) bind to HLA class II pockets to be presented and recognized by T-CD4⁺ lymphocytes TCR, so, after their identification, those sequences can be synthesized and evaluated as vaccine candidates for leishmaniasis(11).

In recent decades, the rise of algorithm-based computational tools has allowed the creation of computational systems that "learn" based on the biological results of protein-protein interaction and can predict outcomes. In the field of vaccines, tools can 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).

This work aimed therefore to identify possible T-CD4⁺ human epitopes through bioinformatic analysis of different immunogenic Leishmania proteins and its binding ability to HLA class II pockets.

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: Molecular 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).

Immune response simulation: C-immsim server was used (https://kraken.iac.rm.cnr.it/C-IMMSIM/index.php?page=1) to predict cytokine production by each individual peptide sequence. DR MHC class II was set for DRB1_0401 alleles. All other parameters were set as default(31–33).

For the identification of possible T CD4⁺ epitopes from immunogenic Leishmania proteins, NH, SMT, CPA, CPB, and CPC were selected using L. panamensis as template, and then 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. 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 6 promissory sequences to this point (Table 1).

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_301-316 and CPB_42-57 with 93.3% and 86.6% of identity respectively. Additionally, when sequences were compared to Human homolog, only CPA_301-316showed important identity (60%) with H. sapiens.

Next, to validate strong interactions predicted by bioinformatic servers, molecular docking was performed comparing these 6 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 composed by 9mer core of each peptide (bold letters in Figure 1) as ligand.

The total binding energy (kJ.mol^-1) is the result of the consensus of different intra- and intermolecular interactions between the ligand-Receptor interpreted as lower binding energy, higher the binding affinity. The MHC-class II pocket is formed by Alpha (α) and Beta (β) chains of the DR4 protein. Global analysis (Table 2) showed that epitope SMT_133-148 had the strongest binding affinity (–233.90 Kj.mol-1) followed by CPA_39-54 (-229.14 Kj.mol-1) and CPA_301-316 (–228.65 Kj.mol-1). Finally, among the peptides selected, those with lower binding affinity were NH_69-83(–210.19 Kj.mol-1) and CPC_37-52 (–185.22 Kj.mol-1).

A deep analysis of molecular docking showed interactions between peptide and HLADR*04 α and β chain pocket amino acids (Table 2-Figure 2). The overall of the forces that interact inside the system epitope-receptor can be classified in four main bonds from strongest to weakest: i) Hydrogen Bonds (iii) hydrophobic interactions; (iii) attractive forces, and (vi) non-favorable interactions. β chain showed higher number of interactions (52) compared with α chain amino acids (44), however both chains had a similar number of HB and Hydrophobic bonds among all peptides studied, showing a similar contribution of both α and β chain to peptide bond and stability by strong forces. Among α chain, there were several positions which had repeated interactions with different peptides such as ASN62α that interacted with 9 residues forming mainly HB with hydrophobic and polar charged amino acids, as well as ASN69α that formed HB with 6 residues mainly polar and VAL65α formed hydrophobic bonds to mainly hydrophobic peptide residues. Likewise, among β chain, ASN82β, ILE67β, TRP61β and TYR30β are the position with higher numbers of interaction with 4 bonds each one.

On the peptide side, SMT_133-148 and CPA_301-316 bonded to DRβ1*04 pocket in a canonical way from N terminus to C terminus (Figure 2 B and D), while surprisingly NH_69-83,CPA_39-54,CPB_42-57 and CPC_37-52bonded in a reverse way from C to N terminus (Figure 2 A, C, E and F). When a peptide binds to HLA class II molecules may be an indication of such sequence acting as an T CD4 epitope, however each epitope may trigger different immune response profiles, so finally, immune response triggered by each peptide was predicted by C-immSim server, injecting a single dose of antigen (each 15mer peptide) plus adjuvant. Figure 3 shows a dynamic of cytokine production for over 30 days after immunization in humans carrying HLADRβ*04:01. Results showed that 15 days after immunization, NH_69-83 and SMT_133-148 showed a peak of predominant pro-inflammatory Th1-related response with higher levels of IFNγ and IL-2 (Figure 3 A-B), while CPA peptides induced only IFNγ (Figure 3 C-D) and CPB_42-57 and CPC_37-52 induced IL-2 and TGFβ in higher levels related to anti-inflammatory response(Figure 3 E-F).

Gathering all results, this work made a series of steps to assure the selection of highly probable human vaccine candidate that fulfill some basic conditions such being a promiscuous antigen, do not cross-react with human proteins, being highly conserved among Leishmania species, to be presented by MHC class II molecules and finally to trigger Th-1 immune response. Methodologies proposed in this work allowed a step-by-step selection from a huge number of possible sequences to a limited number of peptides with high probability to be a natural Leishmania epitope for humans. Considering these conditions, SMT_133-148 seems to be a good vaccine candidate to be tested in further in vitro assays, and, in addition, NH_69-83, CPA_39-54and CPA_301-316could also be sequences with the proper response.

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(34).

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(35).

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(36). 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(37–39). Another group of studies has been directed to analyze visceral leishmaniasis species such as L. donovani T and B multiepitope through bioinformatics (40) and researchers that included simultaneously LC and LV species (41).

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.

Molecular docking showed the possibility for sequences to bind in canonical and reverse way, behavior that has been reported for CLIP peptide where CLIP_102-120 binds in a canonical way while CLIP_106-120 binds in a reverse way to HLA-DR1 pocket, opening the possibilities for some antigens to bind in reverse way too(42). Also, recently using machine learning and MHC-II peptidomics data it has been reported that not only HLA-DR pocket allows reverse binding but algo HLA-DP and HLA-DQ (43) which can be used to improve prediction algorithms.

Immunoinformatic represents a great alternative in vaccinology research because it allows the screening 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 first development steps. 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 4, SMT_133-148 seems to be a good vaccine candidate to be tested in further in vitro assays, in addition, NH_69-83, CPA_39-54and CPA_301-316could also be sequences with the proper response. Finally, in further studies these sequences will be synthesized and prove their immunogenicity in vitro in human PBMCs to validate their utility as prophylactic or therapeutic alternative options to fight leishmaniasis.

Acknowledgements: This work was possible due to the support of Cooperative University of Colombia, project code INV3340.

Declaration of interest statement: The authors declare no conflicts of interest in the development of this project.

1. World Health Organization. Leishmaniasis [Internet]. [cited 2022 Mar 29]. Available from: https://www.who.int/health-topics/leishmaniasis#tab=tab_1

2. Instituto Nacional de Salud C. Protocolo de vigilancia de Leishmaniasis. [cited 2023 Dec 12]; Available from: https://www.ins.gov.co/buscador-eventos/Lineamientos/Pro_Leishmaniasis.pdf

3. Instituto Nacional de Salud. Sistema de Vigilancia SIVIGILA [Internet]. [cited 2022 Jun 16]. Available from: https://www.ins.gov.co/Direcciones/Vigilancia/Paginas/SIVIGILA.aspx

4. Okwor I, Uzonna J. Social and Economic Burden of Human Leishmaniasis. Am J Trop Med Hyg [Internet]. 2016 Mar 1 [cited 2024 Jan 16];94(3):489–93. Available from: https://pubmed.ncbi.nlm.nih.gov/26787156/

5. Belkaid Y, Kamhawi S, Modi G, Valenzuela J, Noben-Trauth N, Rowton E, et al. Development of a Natural Model of Cutaneous Leishmaniasis: Powerful Effects of Vector Saliva and Saliva Preexposure on the Long-Term Outcome of Leishmania major Infection in the Mouse Ear Dermis. J Exp Med [Internet]. 1998 Nov 11 [cited 2022 Aug 27];188(10):1941. Available from: /pmc/articles/PMC2212417/

6. Bacon KM, Hotez PJ, Kruchten SD, Kamhawi S, Bottazzi ME, Valenzuela JG, et al. The potential economic value of a cutaneous leishmaniasis vaccine in seven endemic countries in the Americas. Vaccine [Internet]. 2012 Nov 20 [cited 2023 Dec 12];31(3):480–6. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23176979/?tool=EBI

7. Dinc R. Leishmania Vaccines: the Current Situation with Its Promising Aspect for the Future. Korean J Parasitol [Internet]. 2022 Dec 1 [cited 2024 Jan 16];60(6):379–91. Available from: https://pubmed.ncbi.nlm.nih.gov/36588414/

8. Sacks D, Noben-Trauth N. The immunology of susceptibility and resistance to Leishmania major in mice. Nat Rev Immunol [Internet]. 2002 Nov [cited 2024 Jan 16];2(11):845–58. Available from: https://pubmed.ncbi.nlm.nih.gov/12415308/

9. Tripathi P, Singh V, Naik S. Immune response to leishmania: paradox rather than paradigm. FEMS Immunol Med Microbiol [Internet]. 2007 Nov 1 [cited 2022 Aug 25];51(2):229–42. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1574-695X.2007.00311.x

10. Gollob KJ, Viana AG, Dutra WO. Immunoregulation in human American leishmaniasis: balancing pathology and protection. Parasite Immunol [Internet]. 2014 Aug 1 [cited 2022 Aug 25];36(8):367–76. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/pim.12100

11. Desai D V., Kulkarni-Kale U. T-cell epitope prediction methods: An overview. Methods in Molecular Biology [Internet]. 2014 [cited 2022 Aug 27];1184:333–64. Available from: https://link.springer.com/protocol/10.1007/978-1-4939-1115-8_19

12. Sidney J, Peters B, Sette A. Epitope prediction and identification- adaptive T cell responses in humans. Semin Immunol. 2020 Aug 1;50:101418.

13. Patronov A, Doytchinova I. T-cell epitope vaccine design by immunoinformatics. Open Biol [Internet]. 2013 [cited 2024 Jan 16];3(1). Available from: /pmc/articles/PMC3603454/

14. Trachtenberg EA, Keyeux G, Bernal JE, Rhodas MC, Erlich HA. Results of Expedition Humana. Tissue Antigens [Internet]. 1996 Sep 1 [cited 2024 Jan 16];48(3):174–81. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1399-0039.1996.tb02625.x

15. Correa PA, Whitworth WC, Kuffner T, McNicholl J, Anaya JM. HLA-DR and DQB1 gene polymorphism in the North-western Colombian population. Tissue Antigens [Internet]. 2002 May 1 [cited 2024 Jan 16];59(5):436–9. Available from: https://onlinelibrary.wiley.com/doi/full/10.1034/j.1399-0039.2002.590515.x

16. Ossa Reyes H, Manrique A, Quintanilla S, Peña A. Polimorfismos del sistema HLA (loci A*, B* y DRB1*) en población colombiana. Nova. 2007 Jun 15;5(7):25–30.

17. Rocío Arias-Murillo Y, Ángel Castro-Jiménez M, Ríos-Espinosa F, López-Rivera JJ, Johanna Echeverry-Coral S, Esp B, et al. Analysis of HLA-A, HLA-B, HLA-DRB1 allelic, genotypic, and haplotypic frequencies in colombian population. Colomb Med [Internet]. 2010 [cited 2024 Jan 16];41(4):336–43. Available from: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S1657-95342010000400006&lng=en&nrm=iso&tlng=en

18. Ávila-Portillo LM, Carmona A, Franco L, Briceño I, Casas MC, Gómez A. Bajo polimorfismo en el sistema de antígenos de leucocitos humanos en población mestiza colombiana. Universitas Medica [Internet]. 2010 Aug 2 [cited 2024 Jan 16];51(4):359–70. Available from: https://revistas.javeriana.edu.co/index.php/vnimedica/article/view/16020

19. Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Peters B, et al. Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data. J Proteome Res. 2020 Jun 5;19(6):2304–15.

20. Racle J, Michaux J, Rockinger GA, Arnaud M, Bobisse S, Chong C, et al. Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Nat Biotechnol [Internet]. 2019 Nov 1 [cited 2023 Dec 6];37(11):1283–6. Available from: https://pubmed.ncbi.nlm.nih.gov/31611696/

21. Racle J, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, et al. Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. Immunity [Internet]. 2023 Jun 13 [cited 2023 Dec 6];56(6):1359-1375.e13. Available from: http://www.cell.com/article/S1074761323001292/fulltext

22. Chen B, Khodadoust MS, Olsson N, Wagar LE, Fast E, Liu CL, et al. Predicting HLA class II antigen presentation through integrated deep learning. Nat Biotechnol [Internet]. 2019 Nov 1 [cited 2024 Jan 15];37(11):1332–43. Available from: https://pubmed.ncbi.nlm.nih.gov/31611695/

23. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res [Internet]. 2004 Mar 1 [cited 2024 Jan 25];32(5):1792–7. Available from: https://dx.doi.org/10.1093/nar/gkh340

24. Edgar RC. MUSCLE: A multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics [Internet]. 2004 Aug 19 [cited 2024 Jan 25];5(1):1–19. Available from: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-5-113

25. Xu X, Zou X. MDockPeP: A Web Server for Blind Prediction of Protein-Peptide Complex Structures. Methods Mol Biol [Internet]. 2020 [cited 2024 Jan 21];2165:259–72. Available from: https://pubmed.ncbi.nlm.nih.gov/32621230/

26. Zhou P, Jin B, Li H, Huang SY. HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm. Nucleic Acids Res [Internet]. 2018 Jul 2 [cited 2024 Jan 21];46(W1):W443–50. Available from: https://pubmed.ncbi.nlm.nih.gov/29746661/

27. Xu X, Yan C, Zou X. MDockPeP: An ab-initio protein–peptide docking server. J Comput Chem [Internet]. 2018 Oct 30 [cited 2024 Jan 24];39(28):2409–13. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/jcc.25555

28. Pyrkov T V., Ozerov I V., Balitskaya ED, Efremov RG. Molecular docking: The role of noncovalent interactionsin the formation of protein-nucleotide and protein-peptide complexes. Russ J Bioorg Chem [Internet]. 2010 Jul 20 [cited 2024 Jan 24];36(4):446–55. Available from: https://link.springer.com/article/10.1134/S1068162010040023

29. Ling R, Dai Y, Huang B, Huang W, Yu J, Lu X, et al. In silico design of antiviral peptides targeting the spike protein of SARS-CoV-2. Peptides (NY) [Internet]. 2020 Aug 1 [cited 2024 Jan 24];130:170328. Available from: /pmc/articles/PMC7198429/

30. Pantsar T, Poso A. Binding Affinity via Docking: Fact and Fiction. Molecules 2018, Vol 23, Page 1899 [Internet]. 2018 Jul 30 [cited 2024 Jan 24];23(8):1899. Available from: https://www.mdpi.com/1420-3049/23/8/1899/htm

31. Rapin N, Lund O, Bernaschi M, Castiglione F. Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System. PLoS One [Internet]. 2010 [cited 2024 Apr 2];5(4):9862. Available from: /pmc/articles/PMC2855701/

32. Stolfi P, Castiglione F, Mastrostefano E, Di Biase I, Di Biase S, Palmieri G, et al. In-silico evaluation of adenoviral COVID-19 vaccination protocols: Assessment of immunological memory up to 6 months after the third dose. Front Immunol [Internet]. 2022 Oct 24 [cited 2024 Apr 2];13. Available from: /pmc/articles/PMC9639861/

33. Ragone C, Manolio C, Cavalluzzo B, Mauriello A, Tornesello ML, Buonaguro FM, et al. Identification and validation of viral antigens sharing sequence and structural homology with tumor-associated antigens (TAAs). J Immunother Cancer [Internet]. 2021 May 28 [cited 2024 Apr 2];9(5). Available from: https://pubmed.ncbi.nlm.nih.gov/34049932/

34. Dinc R. Leishmania Vaccines: the Current Situation with Its Promising Aspect for the Future. Korean J Parasitol [Internet]. 2022 Dec 1 [cited 2024 Jan 26];60(6):379. Available from: /pmc/articles/PMC9806502/

35. Soleymani S, Tavassoli A, Housaindokht MR. An overview of progress from empirical to rational design in modern vaccine development, with an emphasis on computational tools and immunoinformatics approaches. Comput Biol Med [Internet]. 2022 Jan 1 [cited 2024 Jan 26];140. Available from: https://pubmed.ncbi.nlm.nih.gov/34839187/

36. Seyed N, Zahedifard F, Safaiyan S, Gholami E, Doustdari F, Azadmanesh K, et al. In Silico Analysis of Six Known Leishmania major Antigens and In Vitro Evaluation of Specific Epitopes Eliciting HLA-A2 Restricted CD8 T Cell Response. PLoS Negl Trop Dis [Internet]. 2011 Sep [cited 2024 Jan 26];5(9):e1295. Available from: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0001295

37. Motamedpour L, Dalimi A, Pirestani M, Ghaffarifar F. In silico analysis and expression of a new chimeric antigen as a vaccine candidate against cutaneous leishmaniasis. Iran J Basic Med Sci [Internet]. 2020 Nov 1 [cited 2024 Jan 26];23(11):1409–18. Available from: https://ijbms.mums.ac.ir/article_16432.html

38. Ahmadpour NB, Dalimi A, Pirestani M, Sadraei J. A novel chimeric antigen as a vaccine candidate against leishmania major: In silico analysis. Iran J Parasitol. 2021 Apr 1;16(2):186–98.

39. Rabienia M, Roudbari Z, Ghanbariasad A, Abdollahi A, Mohammadi E, Mortazavidehkordi N, et al. Exploring membrane proteins of Leishmania major to design a new multi-epitope vaccine using immunoinformatics approach. European Journal of Pharmaceutical Sciences. 2020 Sep 1;152:105423.

40. Saha S, Vashishtha S, Kundu B, Ghosh M. In-silico design of an immunoinformatics based multi-epitope vaccine against Leishmania donovani. BMC Bioinformatics. 2022 Dec 1;23(1).

41. Bhattacharjee M, Banerjee M, Mukherjee A. In silico designing of a novel polyvalent multi-subunit peptide vaccine leveraging cross-immunity against human visceral and cutaneous leishmaniasis: an immunoinformatics-based approach. J Mol Model [Internet]. 2023 Apr 1 [cited 2024 Jan 26];29(4). Available from: https://pubmed.ncbi.nlm.nih.gov/36928431/

42. Günther S, Schlundt A, Sticht J, Roske Y, Heinemann U, Wiesmüller KH, et al. Bidirectional binding of invariant chain peptides to an MHC class II molecule. Proc Natl Acad Sci U S A [Internet]. 2010 Dec 21 [cited 2024 Sep 3];107(51):22219–24. Available from: https://www.pnas.org/doi/abs/10.1073/pnas.1014708107

43. Racle J, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, et al. Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. Immunity [Internet]. 2023 Jun 13 [cited 2024 Sep 3];56(6):1359-1375.e13. Available from: http://www.cell.com/article/S1074761323001292/fulltext

The authors declare no competing interests.

Download PDF

Version 2

posted

You are reading this latest preprint version

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

Status:

Version 2

Abstract

Figures

Introduction

Materials and Methods

Results

Discussion

Declarations

References

Additional Declarations

Status:

Version 2