Engineering a Multi-Epitope Vaccine for Leishmania infantum: A combination of traditional immuno-informatic tools and python programming Approaches
Keywords:
Leishmaniasis, Multi-epitope vaccine, Python, in silico design, mmunoinformatics, Vaccine development, Computational biologyAbstract
Leishmaniasis, a significant health challenge in subtropical regions, requires effective and safe therapeutic options. Our primary objective was to design and evaluate a multi-epitope peptide-based vaccine, LIDVC, targeting Leishmania infantum. LIDVC was developed by integrating key epitopes from GP63, KMP11, and LACK antigens with the adjuvant Ov-ASP-1. Advanced bioinformatics tools assessed the vaccine's antigenicity, allergenicity, toxicity, and population coverage. Structural integrity was confirmed through AlphaFold and DeepCDpred analyses, and molecular docking studies with TLR4 were performed. Our comprehensive computational evaluation demonstrated robust interactions between LIDVC and TLR4, favorable dynamic properties, and strong protective responses akin to those from natural infections. Codon optimization and in silico cloning showed potential for efficient expression in Escherichia coli. The LIDVC vaccine candidate shows promise against L. infantum, warranting experimental validation and preclinical studies. Our computational evaluation highlights LIDVC's potential as a safe and effective vaccine against leishmaniasis, addressing the need for improved therapeutic options in subtropical regions. Further studies will focus on experimental validation and developing a safe and effective vaccine against leishmaniasis.
References
1. Alvar J, Vélez ID, Bern C, Herrero M, Desjeux P, Cano J, et al. Leishmaniasis worldwide and global estimates of its incidence. PLoS One. 2012;7(5):e35671. doi: 10.1371/journal.pone.0035671.
2. Pradhan S, Schwartz RA, Patil A, Grabbe S, Goldust M. Treatment options for leishmaniasis. Clinical and Experimental Dermatology. 2022;47(3):516–21. doi: 10.1111/ced.14919.
3. Krishnamoorthy P, Raj AS, Kumar H. Machine learning-driven blood transcriptome-based discovery of SARS-CoV-2 specific severity biomarkers. J Med Virol. 2023;95(2):e28488. doi: 10.1002/jmv.28488.
4. Margaroni M, Agallou M, Tsanaktsidou E, Kammona O, Kiparissides C, Karagouni E. Immunoinformatics Approach to Design a Multi-Epitope Nanovaccine against Leishmania Parasite: Elicitation of Cellular Immune Responses. Vaccines (Basel). 2023;11(2). doi: 10.3390/vaccines11020304.
5. Yadagiri G, Singh A, Arora K, Mudavath SL. Immunotherapy and immunochemotherapy in combating visceral leishmaniasis. Front Med (Lausanne). 2023;10:1096458. doi: 10.3389/fmed.2023.1096458.
6. Nagill R, Kaur S. Vaccine candidates for leishmaniasis: a review. Int Immunopharmacol. 2011;11(10):1464–88. doi: 10.1016/j.intimp.2011.05.008.
7. Al-Qahtani AA, Alhamlan FS, Al-Qahtani AA. Pro-Inflammatory and Anti-Inflammatory Interleukins in Infectious Diseases: A Comprehensive Review. Tropical Medicine and Infectious Disease. 2024;9(1):13. doi: 10.3390/tropicalmed9010013.
8. Costa-da-Silva AC, Nascimento DO, Ferreira JRM, Guimarães-Pinto K, Freire-de-Lima L, Morrot A, et al. Immune Responses in Leishmaniasis: An Overview. Trop Med Infect Dis. 2022;7(4). doi: 10.3390/tropicalmed7040054.
9. Larijani A, Kia-Karimi A, Rahmati M, Derakhshan R. Computational Prediction of Leishmania Infantum Epitopes: A Bioinformatic-based Step to Leishmaniasis Vaccine Design. Journal of Visualized Medicine. 2024;1(6):–. Doi: 10.30699/jovm.006.e0109
10. Ponte-Sucre A, Gamarro F, Dujardin JC, Barrett MP, López-Vélez R, García-Hernández R, et al. Drug resistance and treatment failure in leishmaniasis: A 21st century challenge. PLoS Negl Trop Dis. 2017;11(12):e0006052. doi: 10.1371/journal.pntd.0006052.
11. Kurcinski M, Oleniecki T, Ciemny MP, Kuriata A, Kolinski A, Kmiecik S. CABS-flex standalone: a simulation environment for fast modeling of protein flexibility. Bioinformatics. 2019;35(4):694–5. doi: 10.1093/bioinformatics/bty685.
12. Knight CA, Harris DR, Alshammari SO, Gugssa A, Young T, Lee CM. Leishmaniasis: Recent epidemiological studies in the Middle East. Frontiers in Microbiology. 2023;13. doi: 10.3389/fmicb.2022.1052478.
13. Ghorbani M, Farhoudi R. Leishmaniasis in humans: drug or vaccine therapy? Drug Des Devel Ther. 2018;12:25–40. doi: 10.2147/DDDT.S146521.
14. Srivastava S, Shankar P, Mishra J, Singh S. Possibilities and challenges for developing a successful vaccine for leishmaniasis. Parasites & Vectors. 2016;9(1):277. doi: 10.1186/s13071-016-1553-y.
15. Ong E, Cooke MF, Huffman A, Xiang Z, Wong MU, Wang H, et al. Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning. Nucleic Acids Research. 2021;49(W1):W671–W8. doi: 10.1093/nar/gkab279.
16. Puigbò P, Bravo IG, Garcia-Vallve S. CAIcal: A combined set of tools to assess codon usage adaptation. Biology Direct. 2008;3(1):38. doi: 10.1186/1745-6150-3-38.
17. Ji S, Oruç T, Mead L, Rehman MF, Thomas CM, Butterworth S, et al. DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure. PLOS ONE. 2019;14(1):e0205214. doi: 10.1371/journal.pone.0205214.
18. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9. doi: 10.1038/s41586-021-03819-2.
19. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8:4. doi: 10.1186/1471-2105-8-4.
20. Wei L, Ye X, Sakurai T, Mu Z, Wei L. ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning. Bioinformatics. 2022;38(6):1514–24. doi: 10.1093/bioinformatics/btac006.
21. Savsani K, Jabbour G, Dakshanamurthy S. A New Epitope Selection Method: Application to Design a Multi-Valent Epitope Vaccine Targeting HRAS Oncogene in Squamous Cell Carcinoma. Vaccines (Basel). 2021;10(1). doi: 10.3390/vaccines10010063
22. Angamuthu K, Piramanayagam S. Evaluation of in silico protein secondary structure prediction methods by employing statistical techniques. Biomedical and Biotechnology Research Journal (BBRJ). 2017;1:29. Doi: 10.4103/bbrj.bbrj_28_17
23. Gasteiger E, C H, A G, Duvaud S, M.R W, Appel R, et al. In The Proteomics Protocols Handbook2005.
24. Torchala M, Bates P. Predicting the Structure of Protein–Protein Complexes Using the SwarmDock Web Server. Methods in molecular biology (Clifton, NJ). 2014;1137:181–97. doi: 10.1007/978-1-4939-0366-5_13.
25. Grote A, Hiller K, Scheer M, Münch R, Nörtemann B, Hempel DC, et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Research. 2005;33(suppl_2):W526–W31. doi: 10.1093/nar/gki376.
26. Buchan DWA, Minneci F, Nugent TCO, Bryson K, Jones DT. Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Research. 2013;41(W1):W349–W57. doi: 10.1093/nar/gkt381.
27. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Research. 2007;35(suppl_2):W407–W10. doi: 10.1093/nar/gkm290.
28. Ko J, Park H, Heo L, Seok C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Research. 2012;40(W1):W294–W7. doi: 10.1093/nar/gks493.
29. Sabatier M, Birsen R, Lauture L, et al. C/EBPα Confers Dependence to Fatty Acid Anabolic Pathways and Vulnerability to Lipid Oxidative Stress-Induced Ferroptosis in FLT3-Mutant Leukemia. Cancer Discov. 2023;13(7):1720-1747. doi:10.1158/2159-8290.CD-22-0411
Downloads
Published
Issue
Section
License
Copyright (c) 2026 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
