{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T21:55:09Z","timestamp":1782251709950,"version":"3.54.5"},"reference-count":121,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T00:00:00Z","timestamp":1757030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>Background: Tuberculosis (TB) remains a global health priority, with current interventions like the Bacille Calmette\u2013Gu\u00e9rin (BCG) vaccine lacking efficacy against latent infection and drug-resistant strains. Novel vaccines targeting both latent and active TB are urgently needed. Objective: This study aims to design a multi-epitope vaccine (MEV) and evaluate its immunogenicity, structural stability, and interactions with toll-like receptor 2\/4 (TLR-2\/4) via computational biology approaches. Methods: We designed MEV using bioinformatics tools, prioritizing immunodominant epitopes from Mycobacterium tuberculosis antigens. Structural stability was optimized through disulfide engineering, and molecular docking\/dynamics simulations were used to analyze interactions and conformational dynamics with TLR-2\/4. Antigenicity, immunogenicity, population coverage, and immune responses were computationally assessed. Results: The MEV candidate, CP91110P, exhibited 86.18% predicted global human leukocyte antigen (HLA)-I\/II coverage, high antigenicity (VaxiJen: 0.8789), and immunogenicity (IEDB: 4.40091), with favorable stability (instability index: 33.48) and solubility (0.485). Tertiary structure analysis indicated that 98.34% residues were located in favored regions. Molecular docking suggested strong TLR-2 (\u22121535.9 kcal\/mol) and TLR-4 (\u22121672.5 kcal\/mol) binding. Molecular dynamics simulations indicated stable TLR-2 interactions (RMSD: 6\u20138 \u00c5; Rg: 38.50\u201339.50 \u00c5) and flexible TLR-4 binding (RMSD: 2\u20136 \u00c5; Rg: 33\u201336 \u00c5). Principal component analysis, free energy landscapes, and dynamic cross-correlation matrix analyses highlighted TLR-2\u2019s structural coherence versus TLR-4\u2019s adaptive flexibility. Immune simulations predicted potential robust natural killer cell activation, T helper 1 polarization (interferon-gamma\/interleukin-2 dominance), and elevated IgM\/IgG levels. Conclusions: CP91110P is predicted to stably bind to TLR-2 and flexibly interact with TLR-4, with prediction of its high antigenicity and broad coverage across immune populations. However, this conclusion requires confirmation through experimental validation. Therefore, it may provide a promising candidate for experimental validation in the development of tuberculosis vaccines.<\/jats:p>","DOI":"10.3390\/biology14091196","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T12:18:18Z","timestamp":1757074698000},"page":"1196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["CP91110P: A Computationally Designed Multi-Epitope Vaccine Candidate for Tuberculosis via TLR-2\/4 Synergistic Immunomodulation"],"prefix":"10.3390","volume":"14","author":[{"given":"Yajing","family":"An","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Institute of Tuberculosis, Senior Department of Tuberculosis, The Eighth Medical Center of PLA General Hospital, Beijing 100091, China"},{"name":"Graduate School, Hebei North University, Zhangjiakou 075000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syed Luqman","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Abdul Wali Khan University, Mardan 23200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhua","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Institute of Tuberculosis, Senior Department of Tuberculosis, The Eighth Medical Center of PLA General Hospital, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aigul","family":"Abduldayeva","sequence":"additional","affiliation":[{"name":"Research Institute of Preventive Medicine named Academician E. Dalenov, Astana Medical University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruizi","family":"Ni","sequence":"additional","affiliation":[{"name":"Graduate School, Hebei North University, Zhangjiakou 075000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufeng","family":"Li","sequence":"additional","affiliation":[{"name":"Graduate School, Hebei North University, Zhangjiakou 075000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Graduate School, Hebei North University, Zhangjiakou 075000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[{"name":"Graduate School, Hebei North University, Zhangjiakou 075000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Immunology, College of Laboratory Medicine & Institute of Microcirculation, Hebei North University, Zhangjiakou 075000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0333-890X","authenticated-orcid":false,"given":"Wenping","family":"Gong","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Institute of Tuberculosis, Senior Department of Tuberculosis, The Eighth Medical Center of PLA General Hospital, Beijing 100091, China"},{"name":"Graduate School, Hebei North University, Zhangjiakou 075000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e01545-24","DOI":"10.1128\/spectrum.01545-24","article-title":"Application value of nucleic acid MALDI-TOF MS in mycobacterial species identification and drug resistance detection in Mycobacterium tuberculosis","volume":"13","author":"Liu","year":"2025","journal-title":"Microbiol. Spectr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/S1473-3099(24)00861-2","article-title":"Evaluating H56:IC31 vaccine in tuberculosis recurrence prevention","volume":"25","author":"Gong","year":"2025","journal-title":"Lancet Infect. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e70054","DOI":"10.1002\/imt2.70054","article-title":"Gut microbiota and tuberculosis","volume":"4","author":"Liu","year":"2025","journal-title":"iMeta"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"999","DOI":"10.15212\/ZOONOSES-2024-0061","article-title":"Decoding the WHO Global Tuberculosis Report 2024: A Critical Analysis of Global and Chinese Key Data","volume":"5","author":"Chen","year":"2025","journal-title":"Zoonoses"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"An, Y., Ni, R., Zhuang, L., Yang, L., Ye, Z., Li, L., Parkkila, S., Aspatwar, A., and Gong, W. (2025). Tuberculosis vaccines and therapeutic drug: Challenges and future directions. Mol. Biomed., 6.","DOI":"10.1186\/s43556-024-00243-6"},{"key":"ref_6","first-page":"100026","article-title":"Harnessing bioinformatics for the development of a promising multi-epitope vaccine against tuberculosis: The ZL9810L vaccine","volume":"2","author":"Zhuang","year":"2024","journal-title":"Decod. Infect. Transm."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e419","DOI":"10.1002\/mco2.419","article-title":"Mycobacterium tuberculosis: Immune response, biomarkers, and therapeutic intervention","volume":"5","author":"Zhuang","year":"2024","journal-title":"MedComm"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Flores-Valdez, M.A. (2021). After 100 Years of BCG Immunization against Tuberculosis, What Is New and Still Outstanding for This Vaccine?. Vaccines, 10.","DOI":"10.3390\/vaccines10010057"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhuang, L., Ye, Z., Li, L., Yang, L., and Gong, W. (2023). Next-Generation TB Vaccines: Progress, Challenges, and Prospects. Vaccines, 11.","DOI":"10.3390\/vaccines11081304"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cheng, P., Wang, L., and Gong, W. (2022). In silico Analysis of Peptide-Based Biomarkers for the Diagnosis and Prevention of Latent Tuberculosis Infection. Front. Microbiol., 13.","DOI":"10.3389\/fmicb.2022.947852"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1164\/rccm.201902-0260LE","article-title":"New World Health Organization Treatment Recommendations for Multidrug-Resistant Tuberculosis: Are We Well Enough Prepared?","volume":"200","author":"Kranzer","year":"2019","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1002\/ila2.65","article-title":"Machine learning model based on SERPING1, C1QB, and C1QC: A novel diagnostic approach for latent tuberculosis infection","volume":"2","author":"Li","year":"2024","journal-title":"iLABMED"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"94","DOI":"10.37201\/req\/s03.20.2022","article-title":"Latent tuberculosis infection: Approach and therapeutic schemes","volume":"35","author":"Navas","year":"2022","journal-title":"Rev. Esp. De Quimioter."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1038\/cmi.2017.92","article-title":"Multi-epitope vaccines: A promising strategy against tumors and viral infections","volume":"15","author":"Zhang","year":"2018","journal-title":"Cell. Mol. Immunol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100148","DOI":"10.1016\/j.imj.2024.100148","article-title":"Leveraging computer-aided design and artificial intelligence to develop a next-generation multi-epitope tuberculosis vaccine candidate","volume":"3","author":"Zhuang","year":"2024","journal-title":"Infect. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, J., Jiang, F., Cheng, P., Ye, Z., Li, L., Yang, L., Zhuang, L., and Gong, W. (2024). Construction of novel multi-epitope-based diagnostic biomarker HP16118P and its application in the differential diagnosis of Mycobacterium tuberculosis latent infection. Mol. Biomed., 5.","DOI":"10.1186\/s43556-024-00177-z"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1097\/ID9.0000000000000104","article-title":"Development and Evaluation of a Promising Biomarker for Diagnosis of Latent and Active Tuberculosis Infection","volume":"4","author":"Peng","year":"2024","journal-title":"Infect. Dis. Immun."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Peng, C., Tang, F., Wang, J., Cheng, P., Wang, L., and Gong, W. (2023). Immunoinformatic-Based Multi-Epitope Vaccine Design for Co-Infection of Mycobacterium tuberculosis and SARS-CoV-2. J. Pers. Med., 13.","DOI":"10.3390\/jpm13010116"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/med4.40","article-title":"Design and development of a multi-epitope vaccine for the prevention of latent tuberculosis infection","volume":"1","author":"Jiang","year":"2023","journal-title":"Med. Adv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiang, F., Peng, C., Cheng, P., Wang, J., Lian, J., and Gong, W. (2023). PP19128R, a Multiepitope Vaccine Designed to Prevent Latent Tuberculosis Infection, Induced Immune Responses In Silico and In Vitro Assays. Vaccines, 11.","DOI":"10.3390\/vaccines11040856"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jiang, F., Han, Y., Liu, Y., Xue, Y., Cheng, P., Xiao, L., and Gong, W. (2023). A comprehensive approach to developing a multi-epitope vaccine against Mycobacterium tuberculosis: From in silico design to in vitro immunization evaluation. Front. Immunol., 14.","DOI":"10.3389\/fimmu.2023.1280299"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, F., Dang, W., Du, Y., Xu, X., He, P., Zhou, Y., and Zhu, B. (2024). Tuberculosis Vaccines and T Cell Immune Memory. Vaccines, 12.","DOI":"10.3390\/vaccines12050483"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e353","DOI":"10.1002\/mco2.353","article-title":"Mycobacterium tuberculosis: Pathogenesis and therapeutic targets","volume":"4","author":"Yang","year":"2023","journal-title":"MedComm"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e41714","DOI":"10.1016\/j.heliyon.2025.e41714","article-title":"Computational epitope-based vaccine design with bioinformatics approach; a review","volume":"11","author":"Basmenj","year":"2025","journal-title":"Heliyon"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1093\/bfgp\/elr037","article-title":"Bioinformatics tools and databases for analysis of next-generation sequence data","volume":"11","author":"Lee","year":"2012","journal-title":"Brief. Funct. Genomics"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1080\/20477724.2020.1838190","article-title":"Immunodominant regions prediction of nucleocapsid protein for SARS-CoV-2 early diagnosis: A bioinformatics and immunoinformatics study","volume":"114","author":"Dai","year":"2020","journal-title":"Pathog. Glob. Health"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5446","DOI":"10.1128\/IAI.70.10.5446-5453.2002","article-title":"Epitope mapping of the immunodominant antigen TB10.4 and the two homologous proteins TB10.3 and TB12.9, which constitute a subfamily of the esat-6 gene family","volume":"70","author":"Brock","year":"2002","journal-title":"Infect. Immun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.tube.2010.09.006","article-title":"The MycoBrowser portal: A comprehensive and manually annotated resource for mycobacterial genomes","volume":"91","author":"Kapopoulou","year":"2011","journal-title":"Tuberculosis"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1002\/eji.201141602","article-title":"Double- and monofunctional CD4+ and CD8+ T-cell responses to Mycobacterium tuberculosis DosR antigens and peptides in long-term latently infected individuals","volume":"41","author":"Commandeur","year":"2011","journal-title":"Eur. J. Immunol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.2174\/0929866527666200505215346","article-title":"Identification of Novel Cytotoxic T Lymphocyte Epitopes of Drug- Resistance Related Protein InhA from Mycobacterium tuberculosis","volume":"27","author":"Li","year":"2020","journal-title":"Protein Pept. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s41541-019-0148-y","article-title":"Identification of antigens presented by MHC for vaccines against tuberculosis","volume":"5","author":"Bettencourt","year":"2020","journal-title":"NPJ Vaccines"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"eadj6367","DOI":"10.1126\/sciadv.adj6367","article-title":"Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning","volume":"9","author":"Nilsson","year":"2023","journal-title":"Sci. Adv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nilsson, J.B., Kaabinejadian, S., Yari, H., Peters, B., Barra, C., Gragert, L., Hildebrand, W., and Nielsen, M. (2023). Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome. Commun. Biol., 6.","DOI":"10.1038\/s42003-023-04749-7"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kaabinejadian, S., Barra, C., Alvarez, B., Yari, H., Hildebrand, W.H., and Nielsen, M. (2022). Accurate MHC Motif Deconvolution of Immunopeptidomics Data Reveals a Significant Contribution of DRB3, 4 and 5 to the Total DR Immunopeptidome. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.835454"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"W449","DOI":"10.1093\/nar\/gkaa379","article-title":"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","volume":"48","author":"Reynisson","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Doytchinova, I.A., and Flower, D.R. (2007). VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform., 8.","DOI":"10.1186\/1471-2105-8-4"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.vaccine.2006.09.032","article-title":"Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties","volume":"25","author":"Doytchinova","year":"2007","journal-title":"Vaccine"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dhanda, S.K., Vir, P., and Raghava, G.P. (2013). Designing of interferon-gamma inducing MHC class-II binders. Biol. Direct, 8.","DOI":"10.1186\/1745-6150-8-30"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Calis, J.J., Maybeno, M., Greenbaum, J.A., Weiskopf, D., De Silva, A.D., Sette, A., Ke\u015fmir, C., and Peters, B. (2013). Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol., 9.","DOI":"10.1371\/journal.pcbi.1003266"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1007\/s00894-014-2278-5","article-title":"AllerTOP v.2--a server for in silico prediction of allergens","volume":"20","author":"Dimitrov","year":"2014","journal-title":"J. Mol. Model."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., and Raghava, G.P. (2013). In silico approach for predicting toxicity of peptides and proteins. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0073957"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/978-1-4939-2285-7_7","article-title":"Peptide toxicity prediction","volume":"1268","author":"Gupta","year":"2015","journal-title":"Methods Mol. Biol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1002\/prot.21078","article-title":"Prediction of continuous B-cell epitopes in an antigen using recurrent neural network","volume":"65","author":"Saha","year":"2006","journal-title":"Proteins"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/978-1-60327-118-9_29","article-title":"Prediction methods for B-cell epitopes","volume":"409","author":"Saha","year":"2007","journal-title":"Methods Mol. Biol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3524","DOI":"10.1080\/07391102.2018.1519460","article-title":"Vaccinomics approach for developing multi-epitope peptide pneumococcal vaccine","volume":"37","author":"Dorosti","year":"2019","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103992","DOI":"10.1016\/j.meegid.2019.103992","article-title":"Development a multi-epitope driven subunit vaccine for immune response reinforcement against Serogroup B of Neisseria meningitidis using comprehensive immunoinformatics approaches","volume":"75","author":"Rostamtabar","year":"2019","journal-title":"Infect. Genet. Evol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3312","DOI":"10.1080\/07391102.2020.1846625","article-title":"Designing a novel multiepitope peptide vaccine against melanoma using immunoinformatics approach","volume":"40","author":"Yazdani","year":"2022","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.tim.2023.10.002","article-title":"Gonococcal PorB: A multifaceted modulator of host immune responses","volume":"32","author":"Jones","year":"2024","journal-title":"Trends Microbiol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7689","DOI":"10.1016\/j.vaccine.2018.10.074","article-title":"Meningococcal PorB induces a robust and diverse antigen specific T cell response as a vaccine adjuvant","volume":"36","author":"Mosaheb","year":"2018","journal-title":"Vaccine"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2858","DOI":"10.1158\/0008-5472.CAN-10-3487","article-title":"Enhanced efficacy of therapeutic cancer vaccines produced by co-treatment with Mycobacterium tuberculosis heparin-binding hemagglutinin, a novel TLR4 agonist","volume":"71","author":"Jung","year":"2011","journal-title":"Cancer Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.ijbiomac.2019.08.259","article-title":"Development of a conserved chimeric vaccine based on helper T-cell and CTL epitopes for induction of strong immune response against Schistosoma mansoni using immunoinformatics approaches","volume":"141","author":"Rahmani","year":"2019","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: A new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mubarak, A.S., Ameen, Z.S., Hassan, A.S., and Ozsahin, D.U. (2024). Enhancing tuberculosis vaccine development: A deconvolution neural network approach for multi-epitope prediction. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-59291-1"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"105870","DOI":"10.1016\/j.actatropica.2021.105870","article-title":"Computational discovery and ex-vivo validation study of novel antigenic vaccine candidates against tuberculosis","volume":"217","author":"Arega","year":"2021","journal-title":"Acta Trop."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e22121","DOI":"10.1016\/j.heliyon.2023.e22121","article-title":"Prediction of an immunogenic peptide ensemble and multi-subunit vaccine for Visceral leishmaniasis using bioinformatics approaches","volume":"9","author":"Kupani","year":"2023","journal-title":"Heliyon"},{"key":"ref_56","first-page":"531","article-title":"Protein identification and analysis tools in the ExPASy server","volume":"112","author":"Wilkins","year":"1999","journal-title":"Methods Mol. Biol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1093\/bioinformatics\/btx345","article-title":"Protein-Sol: A web tool for predicting protein solubility from sequence","volume":"33","author":"Hebditch","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Bui, H.H., Sidney, J., Dinh, K., Southwood, S., Newman, M.J., and Sette, A. (2006). Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinform., 7.","DOI":"10.1186\/1471-2105-7-153"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"W287","DOI":"10.1093\/nar\/gkae328","article-title":"Deep learning for the PSIPRED Protein Analysis Workbench","volume":"52","author":"Buchan","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1007\/s10989-019-09916-1","article-title":"Prediction of potential epitopes for peptide vaccine formulation against teschovirus a using immunoinformatics","volume":"26","author":"Barman","year":"2020","journal-title":"Int. J. Pept. Res. Ther."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"38391","DOI":"10.1039\/C9RA05218F","article-title":"Protein secondary structure prediction with context convolutional neural network","volume":"9","author":"Long","year":"2019","journal-title":"RSC Adv."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1093\/bioinformatics\/16.4.404","article-title":"The PSIPRED protein structure prediction server","volume":"16","author":"McGuffin","year":"2000","journal-title":"Bioinformatics"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Sahay, A., Piprodhe, A., and Pise, M. (2020). In silico analysis and homology modeling of strictosidine synthase involved in alkaloid biosynthesis in catharanthus roseus. J. Genet. Eng. Biotechnol., 18.","DOI":"10.1186\/s43141-020-00049-3"},{"key":"ref_64","first-page":"681","article-title":"SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments","volume":"11","author":"Geourjon","year":"1995","journal-title":"Comput. Appl. Biosci. CABIOS"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"47","DOI":"10.34184\/kssb.2021.9.3.47","article-title":"Accurate protein structure prediction: What comes next?","volume":"9","author":"Seok","year":"2021","journal-title":"BIODESIGN"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"W294","DOI":"10.1093\/nar\/gks493","article-title":"GalaxyWEB server for protein structure prediction and refinement","volume":"40","author":"Ko","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"W407","DOI":"10.1093\/nar\/gkm290","article-title":"ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins","volume":"35","author":"Wiederstein","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1002\/prot.340170404","article-title":"Recognition of errors in three-dimensional structures of proteins","volume":"17","author":"Sippl","year":"1993","journal-title":"Proteins"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1002\/pro.5560020916","article-title":"Verification of protein structures: Patterns of nonbonded atomic interactions","volume":"2","author":"Colovos","year":"1993","journal-title":"Protein Sci. A Publ. Protein Soc."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"W318","DOI":"10.1093\/nar\/gkae270","article-title":"The structure assessment web server: For proteins, complexes and more","volume":"52","author":"Waterhouse","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"W375","DOI":"10.1093\/nar\/gkm216","article-title":"MolProbity: All-atom contacts and structure validation for proteins and nucleic acids","volume":"35","author":"Davis","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1107\/S0907444909042073","article-title":"MolProbity: All-atom structure validation for macromolecular crystallography","volume":"66","author":"Chen","year":"2010","journal-title":"Acta Crystallogr. Sect. D Biol. Crystallogr."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1002\/pro.3330","article-title":"MolProbity: More and better reference data for improved all-atom structure validation","volume":"27","author":"Williams","year":"2018","journal-title":"Protein Sci. A Publ. Protein Soc."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1002\/prot.21715","article-title":"QMEAN: A comprehensive scoring function for model quality assessment","volume":"71","author":"Benkert","year":"2008","journal-title":"Proteins"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1093\/bioinformatics\/btz828","article-title":"QMEANDisCo-distance constraints applied on model quality estimation","volume":"36","author":"Studer","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Ponomarenko, J., Bui, H.H., Li, W., Fusseder, N., Bourne, P.E., Sette, A., and Peters, B. (2008). ElliPro: A new structure-based tool for the prediction of antibody epitopes. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-514"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Craig, D.B., and Dombkowski, A.A. (2013). Disulfide by Design 2.0: A web-based tool for disulfide engineering in proteins. BMC Bioinform., 14.","DOI":"10.1186\/1471-2105-14-346"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"6562","DOI":"10.1073\/pnas.86.17.6562","article-title":"Stabilization of phage T4 lysozyme by engineered disulfide bonds","volume":"86","author":"Matsumura","year":"1989","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Gong, W., Pan, C., Cheng, P., Wang, J., Zhao, G., and Wu, X. (2022). Peptide-Based Vaccines for Tuberculosis. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.830497"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1107\/S2059798322002741","article-title":"Elucidation of protein function using computational docking and hotspot analysis by ClusPro and FTMap","volume":"78","author":"Jones","year":"2022","journal-title":"Acta Crystallogr. Sect. D Struct. Biol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1016\/j.str.2020.06.006","article-title":"Performance and Its Limits in Rigid Body Protein-Protein Docking","volume":"28","author":"Desta","year":"2020","journal-title":"Structure"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1002\/prot.25219","article-title":"New additions to the ClusPro server motivated by CAPRI","volume":"85","author":"Vajda","year":"2017","journal-title":"Proteins"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1038\/nprot.2016.169","article-title":"The ClusPro web server for protein-protein docking","volume":"12","author":"Kozakov","year":"2017","journal-title":"Nat. Protoc."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1002\/prot.24403","article-title":"How good is automated protein docking?","volume":"81","author":"Kozakov","year":"2013","journal-title":"Proteins"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"W271","DOI":"10.1093\/nar\/gku339","article-title":"iMODS: Internal coordinates normal mode analysis server","volume":"42","author":"Aliaga","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1002\/prot.20151","article-title":"Predictions of protein flexibility: First-order measures","volume":"56","author":"Kovacs","year":"2004","journal-title":"Proteins"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2843","DOI":"10.1093\/bioinformatics\/btr497","article-title":"iMod: Multipurpose normal mode analysis in internal coordinates","volume":"27","author":"Garzon","year":"2011","journal-title":"Bioinformatics"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Rapin, N., Lund, O., Bernaschi, M., and Castiglione, F. (2010). Computational immunology meets bioinformatics: The use of prediction tools for molecular binding in the simulation of the immune system. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0009862"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Stolfi, P., Castiglione, F., Mastrostefano, E., Di Biase, I., Di Biase, S., Palmieri, G., and Prisco, A. (2022). In-silico evaluation of adenoviral COVID-19 vaccination protocols: Assessment of immunological memory up to 6 months after the third dose. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.998262"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"e002694","DOI":"10.1136\/jitc-2021-002694","article-title":"Identification and validation of viral antigens sharing sequence and structural homology with tumor-associated antigens (TAAs)","volume":"9","author":"Ragone","year":"2021","journal-title":"J. Immunother. Cancer"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"W126","DOI":"10.1093\/nar\/gkm219","article-title":"OPTIMIZER: A web server for optimizing the codon usage of DNA sequences","volume":"35","author":"Puigbo","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"da Silva, M.V., Massaro Junior, V.J., Machado, J.R., Silva, D.A., Castellano, L.R., Alexandre, P.B., Rodrigues, D.B., and Rodrigues, V. (2015). Expression pattern of transcription factors and intracellular cytokines reveals that clinically cured tuberculosis is accompanied by an increase in Mycobacterium-specific Th1, Th2, and Th17 cells. BioMed Res. Int., 2015.","DOI":"10.1155\/2015\/591237"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"e2","DOI":"10.1016\/S1473-3099(21)00403-5","article-title":"100 years of Mycobacterium bovis bacille Calmette-Guerin","volume":"22","author":"Lange","year":"2022","journal-title":"Lancet. Infect. Dis."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"96","DOI":"10.3934\/microbiol.2021007","article-title":"Bacillus Calmette-Guerin (BCG): The adroit vaccine","volume":"7","author":"Adesanya","year":"2021","journal-title":"AIMS Microbiol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1001\/jama.1994.03510330076038","article-title":"Efficacy of BCG vaccine in the prevention of tuberculosis. Meta-analysis of the published literature","volume":"271","author":"Colditz","year":"1994","journal-title":"JAMA"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1016\/S0140-6736(95)92348-9","article-title":"Variation in protection by BCG: Implications of and for heterologous immunity","volume":"346","author":"Fine","year":"1995","journal-title":"Lancet"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"e148291","DOI":"10.1172\/JCI148291","article-title":"BCG turns 100: Its nontraditional uses against viruses, cancer, and immunologic diseases","volume":"131","author":"Singh","year":"2021","journal-title":"J. Clin. Investig."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1128\/MMBR.00021-14","article-title":"Tuberculosis vaccines and prevention of infection","volume":"78","author":"Hawn","year":"2014","journal-title":"Microbiol. Mol. Biol. Rev."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1056\/NEJMoa1714021","article-title":"Prevention of M. tuberculosis Infection with H4:IC31 Vaccine or BCG Revaccination","volume":"379","author":"Nemes","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1038\/s41467-024-45058-9","article-title":"Identification of differentially recognized T cell epitopes in the spectrum of tuberculosis infection","volume":"15","author":"Panda","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"100022","DOI":"10.1074\/mcp.R120.002309","article-title":"The Peptide Vaccine of the Future","volume":"20","author":"Nelde","year":"2021","journal-title":"Mol. Cell. Proteom. MCP"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"3210","DOI":"10.1021\/acs.chemrev.9b00472","article-title":"Peptide-Based Vaccines: Current Progress and Future Challenges","volume":"120","author":"Malonis","year":"2020","journal-title":"Chem. Rev."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"109728","DOI":"10.1016\/j.intimp.2023.109728","article-title":"Developing a multiepitope vaccine for the prevention of SARS-CoV-2 and monkeypox virus co-infection: A reverse vaccinology analysis","volume":"115","author":"Jiang","year":"2023","journal-title":"Int. Immunopharmacol."},{"key":"ref_105","first-page":"CD012915","article-title":"MVA85A vaccine to enhance BCG for preventing tuberculosis","volume":"4","author":"Kashangura","year":"2019","journal-title":"Cochrane Database Syst Rev."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Portal-Celhay, C., Tufariello, J.M., Srivastava, S., Zahra, A., Klevorn, T., Grace, P.S., Mehra, A., Park, H.S., Ernst, J.D., and Jacobs, W.R. (2016). Mycobacterium tuberculosis EsxH inhibits ESCRT-dependent CD4(+) T-cell activation. Nat. Microbiol., 2.","DOI":"10.1038\/nmicrobiol.2016.232"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, J., Liang, J., Zhang, Y., Teng, X., Yuan, X., and Fan, X. (2015). Protection against Mycobacterium tuberculosis infection offered by a new multistage subunit vaccine correlates with increased number of IFN-\u03b3+ IL-2+ CD4+ and IFN-\u03b3+ CD8+ T cells. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0122560"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Martin, M., deVisch, A., Boudehen, Y.M., Barthe, P., Gutierrez, C., Turapov, O., Aydogan, T., Heriaud, L., Gracy, J., and Neyrolles, O. (2023). A Mycobacterium tuberculosis Effector Targets Mitochondrion, Controls Energy Metabolism, and Limits Cytochrome c Exit. Microbiol. Spectr., 11.","DOI":"10.1128\/spectrum.01066-23"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Mushtaq, K., Sheikh, J.A., Amir, M., Khan, N., Singh, B., and Agrewala, J.N. (2015). Rv2031c of Mycobacterium tuberculosis: A master regulator of Rv2028-Rv2031 (HspX) operon. Front. Microbiol., 6.","DOI":"10.3389\/fmicb.2015.00351"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"e70074","DOI":"10.1002\/iid3.70074","article-title":"Bioinformatics Analysis and Immunogenicity Assessment of the Novel Multi-Stage DNA Vaccine W541 Against Mycobacterium tuberculosis","volume":"12","author":"Yang","year":"2024","journal-title":"Immun. Inflamm. Dis."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"323","DOI":"10.3390\/vaccines2020323","article-title":"Immune Adjuvant Effect of Molecularly-defined Toll-Like Receptor Ligands","volume":"2","author":"Toussi","year":"2014","journal-title":"Vaccines"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.2174\/1389201017666160914191106","article-title":"Harnessing Bioinformatics for Designing a Novel Multiepitope Peptide Vaccine Against Breast Cancer","volume":"17","author":"Mahmoodi","year":"2016","journal-title":"Curr. Pharm. Biotechnol."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.1128\/IAI.00683-12","article-title":"The amino acid sequence of Neisseria lactamica PorB surface-exposed loops influences Toll-like receptor 2-dependent cell activation","volume":"80","author":"Toussi","year":"2012","journal-title":"Infect. Immun."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.jtbi.2014.01.018","article-title":"A novel multi-epitope peptide vaccine against cancer: An in silico approach","volume":"349","author":"Nezafat","year":"2014","journal-title":"J. Theor. Biol."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.1002\/eji.1830191209","article-title":"Universally immunogenic T cell epitopes: Promiscuous binding to human MHC class II and promiscuous recognition by T cells","volume":"19","author":"Tan","year":"1989","journal-title":"Eur. J. Immunol."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Yun, J.S., Kim, A.R., Kim, S.M., Shin, E., Ha, S.J., Kim, D., and Jeong, H.S. (2024). In silico analysis for the development of multi-epitope vaccines against Mycobacterium tuberculosis. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1474346"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Halajian, E.A., LeBlanc, E.V., Gee, K., and Colpitts, C.C. (2022). Activation of TLR4 by viral glycoproteins: A double-edged sword?. Front. Microbiol., 13.","DOI":"10.3389\/fmicb.2022.1007081"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"8874339","DOI":"10.1155\/2021\/8874339","article-title":"COVID-19 and Toll-Like Receptor 4 (TLR4): SARS-CoV-2 May Bind and Activate TLR4 to Increase ACE2 Expression, Facilitating Entry and Causing Hyperinflammation","volume":"2021","author":"Aboudounya","year":"2021","journal-title":"Mediat. Inflamm."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"7890","DOI":"10.4049\/jimmunol.178.12.7890","article-title":"A quantitative analysis of the variables affecting the repertoire of T cell specificities recognized after vaccinia virus infection","volume":"178","author":"Assarsson","year":"2007","journal-title":"J. Immunol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"5831","DOI":"10.4049\/jimmunol.1302101","article-title":"HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity","volume":"191","author":"Paul","year":"2013","journal-title":"J. Immunol."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"12","DOI":"10.29245\/2578-3009\/2020\/4.1198","article-title":"SARS-CoV-2 Virus and Human Leukocyte Antigen (HLA) Class II: Investigation in silico of Binding Affinities for COVID-19 Protection and Vaccine Development","volume":"4","author":"Charonis","year":"2020","journal-title":"J. Immunol. Sci."}],"container-title":["Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-7737\/14\/9\/1196\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:40:04Z","timestamp":1760035204000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-7737\/14\/9\/1196"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,5]]},"references-count":121,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["biology14091196"],"URL":"https:\/\/doi.org\/10.3390\/biology14091196","relation":{},"ISSN":["2079-7737"],"issn-type":[{"value":"2079-7737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,5]]}}}