From Code-To-Cure: Harnessing Immunoinformatics for Next-Gen Vaccine Development
DOI:
https://doi.org/10.22270/ajprd.v13i3.1574Abstract
Vaccination is an important intervention in preventing infectious disease epidemics, saving millions of lives, and reducing infection rates.Vaccines have helped treat various illnesses for years by reducing or eliminating disease burdens. World Health Organization asserts that three million people are saved yearly because of immunization. The new direction in the development of vaccines currently is multi-epitope-based peptide vaccines. Epitope-based peptide vaccines are short protein fragments, known as epitopes, that induce an immune response against a particular pathogen. The conventional approach in vaccine design is labor-intensive, expensive, and time-consuming. The advances in immunoinformatics and vaccinomics have remodeled the face of vaccine science to next-generation vaccine design. Now, virtually new constructs of vaccines can be developed by knowledge of Reverse Vaccinology, various repositories of vaccines, and throughput methods. Such in silico vaccine research tools are strong, inexpensive, accurate, and safe for humans. The candidates for vaccines have rapidly moved into the stage of clinical trials already over the past more than a timeline.The present article will provide detailed information on immunoinformatics working protocol, available databases,and applications of in silico vaccine design with recent case studies that will assist researchers in further tailoring vaccines more rapidly and cost-effectively.
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References
Tomar N, De RK. Immunoinformatics: a brief review. Methods Mol Biol. 2014;1184:23-55.
Backert L, Kohlbacher O. Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Med. 2015;7:119.
Flower DR. Immunoinformatics and the in silico prediction of immunogenicity. An introduction. Methods Mol Biol. 2007;409:1-15.
Vivier E, Artis D, Colonna M, Diefenbach A, Di Santo JP, Eberl G, et al. Innate Lymphoid Cells: 10 Years On. Cell. 2018;174(5):1054-66.
DeLisi C, Berzofsky JA. T-cell antigenic sites tend to be amphipathic structures. Proceedings of the National Academy of Sciences. 1985;82(20):7048-52.
Margalit H, Spouge JL, Cornette JL, Cease KB, Delisi C, Berzofsky JA. Prediction of immunodominant helper T cell antigenic sites from the primary sequence. J Immunol. 1987;138(7):2213-29.
Lefranc MP, Giudicelli V, Ginestoux C, Jabado-Michaloud J, Folch G, Bellahcene F, et al. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res. 2009;37(Database issue):D1006-12.
Peters B, Nielsen M, Sette A. T Cell Epitope Predictions. Annu Rev Immunol. 2020;38:123-45.
Davis MM, Tato CM, Furman D. Systems immunology: just getting started. Nature Immunology. 2017;18(7):725-32.
Pappalardo F, Russo G, Tshinanu FM, Viceconti M. In silico clinical trials: concepts and early adoptions. Brief Bioinform. 2019;20(5):1699-708.
Kitaura K, Shini T, Matsutani T, Suzuki R. A new high-throughput sequencing method for determining diversity and similarity of T cell receptor (TCR) α and β repertoires and identifying potential new invariant TCR α chains. BMC Immunology. 2016;17(1):38.
Toor JS, Rao AA, McShan AC, Yarmarkovich M, Nerli S, Yamaguchi K, et al. A Recurrent Mutation in Anaplastic Lymphoma Kinase with Distinct Neoepitope Conformations. Front Immunol. 2018;9:99.
Rapin N, Hoof I, Lund O, Nielsen M. The MHC motif viewer: a visualization tool for MHC binding motifs. Curr Protoc Immunol. 2010;Chapter 18:18.7.1-.7.3.
Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. J Immunol. 2017;199(9):3360-8.
Fleri W, Paul S, Dhanda SK, Mahajan S, Xu X, Peters B, et al. The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design. Front Immunol. 2017;8:278.
Soria-Guerra RE, Nieto-Gomez R, Govea-Alonso DO, Rosales-Mendoza S. An overview of bioinformatics tools for epitope prediction: Implications on vaccine development. Journal of Biomedical Informatics. 2015;53:405-14.
Richters MM, Xia H, Campbell KM, Gillanders WE, Griffith OL, Griffith M. Best practices for bioinformatic characterization of neoantigens for clinical utility. Genome Medicine. 2019;11(1):56.
Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Research. 2020;48(W1):W449-W54.
Zhang W, Xiong Y, Zhao M, Zou H, Ye X, Liu J. Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature. BMC Bioinformatics. 2011;12(1):341.
Gottschalk RA, Martins AJ, Sjoelund V, Angermann BR, Lin B, Germain RN. Recent progress using systems biology approaches to better understand molecular mechanisms of immunity. Semin Immunol. 2013;25(3):201-8.
Li B, Li T, Pignon J-C, Wang B, Wang J, Shukla SA, et al. Landscape of tumor-infiltrating T cell repertoire of human cancers. Nature Genetics. 2016;48(7):725-32.
The UniProt C. UniProt: a hub for protein information. Nucleic Acids Research. 2015;43(D1):D204-D12.
Barker WC, Garavelli JS, Huang H, McGarvey PB, Orcutt BC, Srinivasarao GY, et al. The Protein Information Resource (PIR). Nucleic Acids Research. 2000;28(1):41-4.
Boeckmann B, Bairoch A, Apweiler R, Blatter M-C, Estreicher A, Gasteiger E, et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Research. 2003;31(1):365-70.
Lefranc M-P, Giudicelli V, Ginestoux C, Bodmer J, Müller W, Bontrop R, et al. IMGT, the international ImMunoGeneTics database. Nucleic Acids Research. 1999;27(1):209-12.
Reche PA, Zhang H, Glutting J-P, Reinherz EL. EPIMHC: a curated database of MHC-binding peptides for customized computational vaccinology. Bioinformatics. 2005;21(9):2140-1.
Blythe MJ, Doytchinova IA, Flower DR. JenPep: a database of quantitative functional peptide data for immunology. Bioinformatics. 2002;18(3):434-9.
Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanović S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics. 1999;50(3-4):213-9.
Saha S, Bhasin M, Raghava GPS. Bcipep: A database of B-cell epitopes. BMC Genomics. 2005;6(1):79.
Johnson G, Wu TT. Kabat database and its applications: 30 years after the first variability plot. Nucleic Acids Res. 2000;28(1):214-8.
Saha S, Raghava GP. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins. 2006;65(1):40-8.
Dhanda SK, Mahajan S, Paul S, Yan Z, Kim H, Jespersen MC, et al. IEDB-AR: immune epitope database—analysis resource in 2019. Nucleic Acids Research. 2019;47(W1):W502-W6.
Clifford JN, Høie MH, Deleuran S, Peters B, Nielsen M, Marcatili P. BepiPred-3.0: Improved B-cell epitope prediction using protein language models. Protein Sci. 2022;31(12):e4497.
Singh H, Raghava GP. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics. 2003;19(8):1009-14.
Singh H, Ansari HR, Raghava GP. Improved method for linear B-cell epitope prediction using antigen's primary sequence. PLoS One. 2013;8(5):e62216.
Bhasin M, Raghava GP. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine. 2004;22(23-24):3195-204.
Yao B, Zhang L, Liang S, Zhang C. SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity. PLOS ONE. 2012;7(9):e45152.
Larsen MV, Lundegaard C, Lamberth K, Buus S, Brunak S, Lund O, et al. An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions. Eur J Immunol. 2005;35(8):2295-303.
Liu F, Yuan C, Chen H, Yang F. Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings. Scientific Reports. 2024;14(1):2464.
Guan P, Hattotuwagama CK, Doytchinova IA, Flower DR. MHCPred 2.0: an updated quantitative T-cell epitope prediction server. Appl Bioinformatics. 2006;5(1):55-61.
Høie MH, Gade FS, Johansen Julie M, Würtzen C, Winther O, Nielsen M, et al. DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations. Frontiers in Immunology. 2024;15.
Reche PA, Glutting J-P, Zhang H, Reinherz EL. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics. 2004;56(6):405-19.
Ansari HR, Raghava GP. Identification of conformational B-cell Epitopes in an antigen from its primary sequence. Immunome Res. 2010;6:6.
Schuler MM, Nastke M-D, Stevanović S. SYFPEITHI. In: Flower DR, editor. Immunoinformatics: Predicting Immunogenicity In Silico. Totowa, NJ: Humana Press; 2007. p. 75-93.
Sweredoski MJ, Baldi P. PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure. Bioinformatics. 2008;24(12):1459-60.
Dönnes P, Kohlbacher O. SVMHC: a server for prediction of MHC-binding peptides. Nucleic Acids Res. 2006;34(Web Server issue):W194-7.
Ponomarenko J, Bui H-H, Li W, Fusseder N, Bourne PE, Sette A, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics. 2008;9(1):514.
Reche PA, Reinherz EL. PEPVAC: a web server for multi-epitope vaccine development based on the prediction of supertypic MHC ligands. Nucleic Acids Res. 2005;33(Web Server issue):W138-42.
Rubinstein ND, Mayrose I, Martz E, Pupko T. Epitopia: a web-server for predicting B-cell epitopes. BMC Bioinformatics. 2009;10(1):287.
Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8(1):4.
Liang S, Zheng D, Yao B, Zhang C. EPCES and EPSVR: Prediction of B-Cell Antigenic Epitopes on Protein Surfaces with Conformational Information. Methods Mol Biol. 2020;2131:289-97.
Atanasova M, Patronov A, Dimitrov I, Flower DR, Doytchinova I. EpiDOCK: a molecular docking-based tool for MHC class II binding prediction. Protein Eng Des Sel. 2013;26(10):631-4.
Qiu T, Zhang L, Chen Z, Wang Y, Mao T, Wang C, et al. SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs. Nucleic Acids Res. 2023;51(W1):W528-w34.
Dimitrov I, Garnev P, Flower DR, Doytchinova I. EpiTOP—a proteochemometric tool for MHC class II binding prediction. Bioinformatics. 2010;26(16):2066-8.
Sela-Culang I, Ashkenazi S, Peters B, Ofran Y. PEASE: predicting B-cell epitopes utilizing antibody sequence. Bioinformatics. 2015;31(8):1313-5.
Doytchinova IA, Guan P, Flower DR. EpiJen: a server for multistep T cell epitope prediction. BMC Bioinformatics. 2006;7(1):131.
Negi SS, Braun W. Automated detection of conformational epitopes using phage display Peptide sequences. Bioinform Biol Insights. 2009;3:71-81.
Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology. 2018;154(3):394-406.
Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v.2--a server for in silico prediction of allergens. J Mol Model. 2014;20(6):2278.
Gasteiger E, Hoogland C, Gattiker A, Duvaud Se, Wilkins MR, Appel RD, et al. Protein Identification and Analysis Tools on the ExPASy Server. In: Walker JM, editor. The Proteomics Protocols Handbook. Totowa, NJ: Humana Press; 2005. p. 571-607.
Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Open Source Drug Discovery C, et al. In Silico Approach for Predicting Toxicity of Peptides and Proteins. PLOS ONE. 2013;8(9):e73957.
Hebditch M, Carballo-Amador MA, Charonis S, Curtis R, Warwicker J. Protein–Sol: a web tool for predicting protein solubility from sequence. Bioinformatics. 2017;33(19):3098-100.
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.
Buchan DWA, Jones DT. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Research. 2019;47(W1):W402-W7.
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.
Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P, Tufféry P. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Research. 2016;44(W1):W449-W54.
Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols. 2010;5(4):725-38.
Källberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, et al. Template-based protein structure modeling using the RaptorX web server. Nat Protoc. 2012;7(8):1511-22.
Bhattacharya D, Nowotny J, Cao R, Cheng J. 3Drefine: an interactive web server for efficient protein structure refinement. Nucleic Acids Research. 2016;44(W1):W406-W9.
Heo L, Park H, Seok C. GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Research. 2013;41(W1):W384-W8.
Nielsen H. Predicting Secretory Proteins with SignalP. Methods Mol Biol. 2017;1611:59-73.
Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305(3):567-80.
Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, et al. The ClusPro web server for protein-protein docking. Nat Protoc. 2017;12(2):255-78.
Honorato RV, Trellet ME, Jiménez-García B, Schaarschmidt JJ, Giulini M, Reys V, et al. The HADDOCK2.4 web server for integrative modeling of biomolecular complexes. Nat Protoc. 2024;19(11):3219-41.
Krissinel E, Henrick K. Inference of macromolecular assemblies from crystalline state. J Mol Biol. 2007;372(3):774-97.
Weng G, Wang E, Wang Z, Liu H, Zhu F, Li D, et al. HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Res. 2019;47(W1):W322-w30.
Tovchigrechko A, Vakser IA. GRAMM-X public web server for protein–protein docking. Nucleic Acids Research. 2006;34(suppl_2):W310-W4.
Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ. GROMACS: fast, flexible, and free. J Comput Chem. 2005;26(16):1701-18.
Pearlman DA, Case DA, Caldwell JW, Ross WS, Cheatham TE, DeBolt S, et al. AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Computer Physics Communications. 1995;91(1):1-41.
Brooks BR, Brooks Iii CL, Mackerell Jr AD, Nilsson L, Petrella RJ, Roux B, et al. CHARMM: The biomolecular simulation program. Journal of Computational Chemistry. 2009;30(10):1545-614.
Eastman P, Galvelis R, Peláez RP, Abreu CRA, Farr SE, Gallicchio E, et al. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. The Journal of Physical Chemistry B. 2024;128(1):109-16.
Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, et al. Scalable molecular dynamics with NAMD. Journal of Computational Chemistry. 2005;26(16):1781-802.
Tomatis Souverbielle C, Erdem G, Sánchez PJ. Update on nonpolio enterovirus and parechovirus infections in neonates and young infants. Curr Opin Pediatr. 2023;35(3):380-9.
Olijve L, Jennings L, Walls T. Human Parechovirus: an Increasingly Recognized Cause of Sepsis-Like Illness in Young Infants. Clin Microbiol Rev. 2018;31(1).
Parvizpour S, Pourseif MM, Razmara J, Rafi MA, Omidi Y. Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches. Drug Discov Today. 2020;25(6):1034-42.
Sarker A, Rahman MM, Khatun C, Barai C, Roy N, Aziz MA, et al. In Silico design of a multi-epitope vaccine for Human Parechovirus: Integrating immunoinformatics and computational techniques. PLOS ONE. 2024;19(12):e0302120.
Masum MHU, Wajed S, Hossain MI, Moumi NR, Talukder A, Rahman MM. An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches. PLOS ONE. 2024;19(7):e0305413.
Khan J, Sadiq A, Alrashed MM, Basharat N, Hassan Mohani SNU, Shah TA, et al. Designing multi-epitope vaccines against Echinococcus granulosus: an in-silico study using immuno-informatics. BMC Molecular and Cell Biology. 2024;25(1):29.
Sira E, Banico EC, Odchimar NMO, Fajardo LE, Fremista FF, Jr., Refuerzo HAB, et al. Immunoinformatics approach for designing a multiepitope subunit vaccine against porcine epidemic diarrhea virus genotype IIA spike protein. Open Vet J. 2024;14(5):1224-42.
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