IEDB-AR: immune epitope database-analysis resource in 2019

The Immune Epitope Database (IEDB): 2018 update

Vita, R., Mahajan, S., Overton, J. A., Dhanda, S. K., Martini, S. R., Cantrell, J. R., Wheeler, D. K., Sette, A., & Peters, B. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1006 · Cited by 2,194

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes

Jespersen, M. C., Peters, B., Nielsen, M., & Marcatili, P. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx346 · Cited by 1,739

ElliPro: a new structure-based tool for the prediction of antibody epitopes

Ponomarenko, J. V., Bui, H., Li, W., Fusseder, N., Bourne, P. E., Sette, A., & Peters, B. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-514 · Cited by 1,738

NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

Jurtz, V. I., Paul, S., Andreatta, M., Marcatili, P., Peters, B., & Nielsen, M. (2017) · The Journal of Immunology · doi:10.4049/jimmunol.1700893 · Cited by 1,476

Canonical structures for the hypervariable regions of immunoglobulins

Chothia, C., & Lesk, A. M. (1987) · Journal of Molecular Biology · doi:10.1016/0022-2836(87)90412-8 · Cited by 1,406

Improved method for predicting linear B-cell epitopes.

Larsen, J., Lund, O., & Nielsen, M. (2006) · Immunome Research · doi:10.1186/1745-7580-2-2 · Cited by 1,243

Gapped sequence alignment using artificial neural networks: application to the MHC class I system

Andreatta, M., & Nielsen, M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv639 · Cited by 1,098

Properties of MHC Class I Presented Peptides That Enhance Immunogenicity

Calis, J. J. A., Maybeno, M., Greenbaum, J., Weiskopf, D., Silva, A. D. D., Sette, A., Keşmir, C., & Peters, B. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003266 · Cited by 920

Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices

Sturniolo, T., Bono, E. B., Ding, J., Raddrizzani, L., Tuereci, O., Şahin, U., Braxenthaler, M., Gallazzi, F., Protti, M. P., Sinigaglia, F., et al. (1999) · Nature Biotechnology · doi:10.1038/9858 · Cited by 743

Reliable B cell epitope predictions: impacts of method development and improved benchmarking

Kringelum, J. V., Lundegaard, C., Lund, O., & Nielsen, M. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002829 · Cited by 697

From Alpha to omicron: The response of T cells

Grifoni, A., & Sette, A. (2022) · Current Research in Immunology · doi:10.1016/j.crimmu.2022.08.005 · Cited by 26

Epitope-Specific T Cell Receptor Data and Tools in the Immune Epitope Database

Chronister, W. D., Sette, A., & Peters, B. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2712-9_13 · Cited by 26

Contemplating immunopeptidomes to better predict them

Gfeller, D., Liu, Y., & Racle, J. (2023) · Seminars in Immunology · doi:10.1016/j.smim.2022.101708 · Cited by 29

Moving from Empirical to Rational Vaccine Design in the ‘Omics’ Era

Sharma, M., Krammer, F., García‐Sastre, A., & Tripathi, S. (2019) · Vaccines · doi:10.3390/vaccines7030089 · Cited by 27

Targets of influenza human T-cell response are mostly conserved in H5N1

Sidney, J., Kim, A.-R., de Vries, R. D., Peters, B., Meade, P. S., Krammer, F., Grifoni, A., & Sette, A. (2024) · mBio · doi:10.1128/mbio.03479-24 · Cited by 23

Mining of Marburg Virus Proteome for Designing an Epitope-Based Vaccine

Soltan, M. A., Abdulsahib, W. K., Amer, M., Refaat, A. M., Bagalagel, A. A., Diri, R. M., Albogami, S. M., Fayad, E., Eid, R. A., Sharaf, S., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.907481 · Cited by 25

Vaccinomics to design a multi-epitope-based vaccine against monkeypox virus using surface-associated proteins

Khan, S., Irfan, M., Hameed, A. R., Ullah, A., Abideen, S. A., Ahmad, S., Haq, M. U., Bakri, Y. E., Al-Harbi, A. I., Ali, M., et al. (2022) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2022.2158942 · Cited by 25

Autoimmunity to synovial extracellular matrix proteins in patients with postinfectious Lyme arthritis

Kanjana, K., Strle, K., Lochhead, R. B., Pianta, A., Mateyka, L. M., Wang, Q., Arvikar, S. L., Kling, D. E., Deangelo, C. A., Curham, L. M., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci161170 · Cited by 24

Pre-existing T Cell Memory against Zika Virus

Schouest, B., Grifoni, A., Pham, J. W., Mateus, J., Sydney, J., Brien, J. D., Silva, A. D. D., Balmaseda, Á., Harris, E., Sette, A., et al. (2021) · Journal of Virology · doi:10.1128/jvi.00132-21 · Cited by 26

Glycoproteogenomics: Setting the Course for Next-Generation Cancer Neoantigen Discovery for Cancer Vaccines

Ferreira, J. A., Relvas‐Santos, M., Peixoto, A., Silva, A. M. N., & Santos, L. L. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2021.03.005 · Cited by 23

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

Rezaei, S., Sefidbakht, Y., & Uskoković, V. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab241 · Cited by 24

Pan genome based reverse vaccinology approach to explore Enterococcus faecium (VRE) strains for identification of novel multi-epitopes vaccine candidate

Alotaibi, G., Khan, K., Mouslem, A. K. A., Khan, S. A., Abbas, M. N., Abbas, M., Shah, S. A., & Jalal, K. (2022) · Immunobiology · doi:10.1016/j.imbio.2022.152221 · Cited by 24

In Silico Prediction of Anti-Infective and Cell-Penetrating Peptides from Thalassophryne nattereri Natterin Toxins

de Cena, G. L., Scavassa, B. V., & Conceição, K. (2022) · Pharmaceuticals · doi:10.3390/ph15091141 · Cited by 24

In silico formulation of a next-generation multiepitope vaccine for use as a prophylactic candidate against Crimean-Congo hemorrhagic fever

Alam, R., Samad, A., Ahammad, F., Nur, S., Alsaiari, A., Imon, R. R., Talukder, M. E. K., Nain, Z., Rahman, M. M., Mohammad, F., et al. (2023) · BMC Medicine · doi:10.1186/s12916-023-02750-9 · Cited by 22

Neo-intline: integrated pipeline enables neoantigen design through the in-silico presentation of T-cell epitope

Li, B., Jing, P., Zheng, G., Pi, C., Zhang, L., Yin, Z., Xu, L., Qiu, J., Gu, H., Qiu, T., et al. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01644-9 · Cited by 23

Construction of an aerolysin-based multi-epitope vaccine against Aeromonas hydrophila: an in silico machine learning and artificial intelligence-supported approach

Alawam, A. S., & Alwethaynani, M. S. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1369890 · Cited by 24

Computational design of SARS-CoV-2 spike glycoproteins to increase immunogenicity by T cell epitope engineering

Ong, E., Huang, X., Pearce, R., Zhang, Y., & He, Y. O. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.12.039 · Cited by 24

Identification and construction of a multi-epitopes vaccine design against Klebsiella aerogenes: molecular modeling study

Alzarea, S. I. (2022) · Scientific Reports · doi:10.1038/s41598-022-18610-0 · Cited by 23

Integration of immunoinformatics and cheminformatics to design and evaluate a multitope vaccine against Klebsiella pneumoniae and Pseudomonas aeruginosa coinfection

Gouda, A. M., Soltan, M. A., Ghany, K. A. E., Sileem, A. E., El-Nahas, H. M., Ateya, M. A.-M., Elbatreek, M. H., Darwish, K. M., Bogari, H. A., Lashkar, M. O., et al. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1123411 · Cited by 23

Design of an Epitope-Based Vaccine Ensemble for Animal Trypanosomiasis by Computational Methods

Todó, L. M., Bigey, P., Reche, P. A., Pinazo, M., Gascón, J., & Alonso-Padilla, J. (2020) · Vaccines · doi:10.3390/vaccines8010130 · Cited by 22

Characterization of myeloperoxidase and its contribution to antimicrobial effect on extracellular traps in flounder (Paralichthys olivaceus)

Gan, Q., Chi, H., Dalmo, R. A., Meng, X., Tang, X., Xing, J., Sheng, X., & Zhan, W. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1124813 · Cited by 22

Pre-existing SARS-2-specific T cells are predicted to cross-recognize BA.2.86

Sette, A., Sidney, J., & Grifoni, A. (2023) · Cell Host & Microbe · doi:10.1016/j.chom.2023.11.010 · Cited by 21

Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery

Kangabam, R. D., Sahoo, S., Ghosh, A., Roy, R., Silla, Y., Misra, N., & Suar, M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104158 · Cited by 23

Expected immune recognition of COVID-19 virus by memory from earlier infections with common coronaviruses in a large part of the world population

Dijkstra, J. M., & Hashimoto, K. (2020) · F1000Research · doi:10.12688/f1000research.23458.1 · Cited by 20

Secrets behind Protein Sequences: Unveiling the Potential Reasons for Varying Allergenicity Caused by Caseins from Cows, Goats, Camels, and Mares Based on Bioinformatics Analyses

Zhao, S., Pan, F., Cai, S., Yi, J., Zhou, L., & Liu, Z. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032481 · Cited by 21

A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

Kaur, D., Arora, C., & Raghava, G. P. S. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00071 · Cited by 20

Development of an in silico multi-epitope vaccine against SARS-COV-2 by précised immune-informatics approaches

Zamane, S. A., Nobel, F. A., Jebin, R. A., Amin, M. A., Somadder, P. D., Antora, N. J., Hossain, M. I., Islam, M. J., Ahmed, K., & Moni, M. A. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100781 · Cited by 21

Structural immunoinformatics approach for rational design of a multi-epitope vaccine against triple negative breast cancer

Dhanushkumar, T., Kamaraj, B., Vasudevan, K., Gopikrishnan, M., Dasegowda, K. R., Rambabu, M., & George Priya Doss, C. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.125209 · Cited by 19

A trivalent mucosal vaccine encoding phylogenetically inferred ancestral RBD sequences confers pan-Sarbecovirus protection in mice

Case, J. B., Sanapala, S., Dillen, C. A., Rhodes, V., Zmasek, C. M., Chicz, T. M., Switzer, C., Scheaffer, S. M., Georgiev, G. P., Jacob-Dolan, C., et al. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.10.016 · Cited by 18

Can molecular mimicry explain the cytokine storm of SARS‐CoV‐2?: An in silico approach

Obando-Pereda, G. A. (2021) · Journal of Medical Virology · doi:10.1002/jmv.27040 · Cited by 19

Ecological immunology

Schmid‐Hempel, P. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780198832140.003.0005 · Cited by 19

TCRMatch: Predicting T-cell receptor specificity based on sequence similarity to previously characterized receptorsPreprint

Chronister, W. D., Crinklaw, A., Mahajan, S., Vita, R., Koşaloğlu‐Yalçın, Z., Zhen, Y., Greenbaum, J., Jessen, L. E., Nielsen, M., Christley, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.11.418426 · Cited by 19

Bioinformatic and Experimental Analysis of T Cell Immune Reactivity to SARS-CoV-2 and its Variants

Tarke, A., Grifoni, A., & Sette, A. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.876380 · Cited by 18

Computational Design of a Chimeric Vaccine against Plesiomonas shigelloides Using Pan-Genome and Reverse Vaccinology

Mushtaq, M., Khan, S., Hassan, M. S., Al-Harbi, A. I., Hameed, A. R., Khan, K., Ismail, S., Irfan, M., & Ahmad, S. (2022) · Vaccines · doi:10.3390/vaccines10111886 · Cited by 18

An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus

Kaur, B., Karnwal, A., Bansal, A., & Malik, T. (2024) · BioMed Research International · doi:10.1155/2024/4066641 · Cited by 17

An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches

Masum, M. H. U., Wajed, S., Hossain, M. I., Moumi, N. R., Talukder, A., & Rahman, M. M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305413 · Cited by 18

Benchmark datasets of immune receptor-epitope structural complexes

Mahajan, S., Zhen, Y., Jespersen, M. C., Jensen, K. K., Marcatili, P., Nielsen, M., Sette, A., & Peters, B. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3109-6 · Cited by 16

Comprehensive Analysis of CD4+ T Cell Responses to CMV pp65 Antigen Restricted by Single HLA-DR, -DQ, and -DP Allotype Within an Individual

Hyun, Y., Jo, H.-A., Lee, Y., Kim, S., Baek, I., Sohn, H., Cho, H.-I., & Kim, T. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2020.602014 · Cited by 18

In silico Design and Characterization of Multi-epitopes Vaccine for SARS-CoV2 from Its Spike Protein

Kathwate, G. H. (2022) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-021-10348-z · Cited by 18

MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides

Yan, C., Geng, A., Pan, Z., Zhang, Z., & Cui, F. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae505 · Cited by 19

Computational Methods in Immunoinformatics: Epitope Discovery and Diagnostic Applications

Bulla, A. C. S., da Silva, A. S., Sereno, B. P., Dias, M. F. R., & da Silva, M. L. (2025) · ACS Omega · doi:10.1021/acsomega.5c05538 · Cited by 17

Unaltered T cell responses to common antigens in individuals with Parkinson's disease

Williams, G. P., Muskat, K., Frazier, A., Xu, Y., Mateus, J., Grifoni, A., Antunes, R. D. S., Weiskopf, D., Amara, A. W., Standaert, D. G., et al. (2022) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2022.120510 · Cited by 17

The role of S477N mutation in the molecular behavior of SARS‐CoV‐2 spike protein: An in‐silico perspective

Mondeali, M., Etemadi, A., Barkhordari, K., Kesheh, M. M., Shavandi, S., Bahavar, A., Hosseini, F., Gomari, M. M., & Modarressi, M. H. (2023) · Journal of Cellular Biochemistry · doi:10.1002/jcb.30367 · Cited by 15

Design of a Novel and Potent Multi-Epitope Chimeric Vaccine against Human Papillomavirus (HPV): An Immunoinformatics Approach

Shahab, M., Guo, D., Zheng, G., & Zou, Y. (2023) · Biomedicines · doi:10.3390/biomedicines11051493 · Cited by 17

Computational formulation of a multiepitope vaccine unveils an exceptional prophylactic candidate against Merkel cell polyomavirus

Imon, R. R., Samad, A., Alam, R., Alsaiari, A. A., Talukder, M. E. K., Almehmadi, M. M., Ahammad, F., & Mohammad, F. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1160260 · Cited by 17

Multi-epitope-based vaccine models prioritization against Astrovirus MLB1 using immunoinformatics and reverse vaccinology approaches

Ali, A., Ali, S. L., Alamri, A., Khatrawi, E. M., Baiduissenova, A., Сулейменова, Ф. М., Mishra, V. K., Khan, A., Утеуович, Д. М., & Askarova, G. (2024) · Journal of Genetic Engineering and Biotechnology · doi:10.1016/j.jgeb.2024.100451 · Cited by 17

Assessing MAPPs assay as a tool to predict the immunogenicity potential of protein therapeutics

Ianni, A. D., Fraone, T., Balestra, P., Cowan, K. J., Sirtori, F. R., & Barbero, L. M. (2023) · Life Science Alliance · doi:10.26508/lsa.202302095 · Cited by 16

Advancements in nanobody generation: Integrating conventional, in silico, and machine learning approaches

Devasani, J. R., Guntuku, G., & Palla, M. S. (2024) · Biotechnology and Bioengineering · doi:10.1002/bit.28816 · Cited by 16

Computer-aided designing of oncolytic viruses for overcoming translational challenges of cancer immunotherapy

Lathwal, A., Kumar, R., & Raghava, G. P. S. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.04.008 · Cited by 16

Viroinformatics investigation of B-cell epitope conserved region in SARS-CoV-2 lineage B.1.1.7 isolates originated from Indonesia to develop vaccine candidate against COVID-19

Ansori, A. N. M., Nidom, R. V., Kusala, M. K. J., Indrasari, S., Normalina, I., Nidom, A. N., Afifah, B., Sari, K. B., Ramadhaniyah, N. L., Alamudi, M. Y., et al. (2021) · Journal of Pharmacy & Pharmacognosy Research · doi:10.56499/jppres21.1101_9.6.766 · Cited by 16

Design of a multi-epitope vaccine candidate against Vibrio cholerae

Koupaei, F. N., Kiumarsy, A., Feizi, M. M., Vajdi, Z., Pishkar, S. E., Alem, M., & Basmenj, E. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-90598-9 · Cited by 15

Computational methods and data resources for predicting tumor neoantigens

Zhao, X., Wei, L., & Zhang, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf302 · Cited by 16

Trans-ancestral fine-mapping of MHC reveals key amino acids associated with spontaneous clearance of hepatitis C in HLA-DQβ1

Valencia, A., Vergara, C., Thio, C. L., Vince, N., Douillard, V., Grifoni, A., Cox, A. L., Johnson, E. O., Kral, A. H., Goedert, J. J., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.01.001 · Cited by 15

A comprehensive overview of recent advances in generative models for antibodies

Meng, F., Zhou, N., Guangchun, H., Liu, R., Zhang, Y., Jing, M., & Hou, Q. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.06.016 · Cited by 17

Immuno-informatics study identifies conserved T cell epitopes in non-structural proteins of Bluetongue virus serotypes: formulation of a computationally optimized next-generation broad-spectrum multi-epitope vaccine

Kolla, H. B., Dutt, M., Kumar, A., Nanjundappa, R. H., Karakach, T. K., Singh, K. P., Kelvin, D. J., Mertens, P., & Umeshappa, C. S. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1424307 · Cited by 15

An Immunomodulatory Therapeutic Vaccine Targeting Oligomeric Amyloid-β1

Song, G., Yang, H., Shen, N., Pham, P., Brown, B., Lin, X., Hong, Y., Paul, S., Cai, J., Li, X., et al. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200413 · Cited by 16

Predicted B Cell Epitopes Highlight the Potential for COVID-19 to Drive Self-Reactive Immunity

Moody, R., Wilson, K., Boer, J. C., Holien, J. K., Flanagan, K. L., Jaworowski, A., & Plebanski, M. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.709533 · Cited by 14

SARS‐CoV‐2 spike evolutionary behaviors; simulation of N501Y mutation outcomes in terms of immunogenicity and structural characteristic

Rostami, N., Choupani, E., Hernandez, Y., Arab, S. S., Jazayeri, S. M., & Gomari, M. M. (2021) · Journal of Cellular Biochemistry · doi:10.1002/jcb.30181 · Cited by 14

Reverse Vaccinology and Immunoinformatic Assisted Designing of a Multi-Epitopes Based Vaccine Against Nosocomial Burkholderia cepacia

Alsowayeh, N., Albutti, A., & Al‐Shouli, S. T. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.929400 · Cited by 16

Immunoinformatics-Based development of a Multi-Epitope vaccine candidate targeting coinfection by Klebsiella pneumoniae and Acinetobacter baumannii

Afshan, G., Yaseen, N., Ali, S. H., & Khan, A. U. (2025) · BMC Infectious Diseases · doi:10.1186/s12879-025-11242-5 · Cited by 14

Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients

Carri, I., Schwab, E., Podaza, E., Alvarez, H. M. G., Mordoh, J., Nielsen, M., & Barrio, M. M. (2023) · Exploration of Immunology · doi:10.37349/ei.2023.00091 · Cited by 15

Immunoinformatics and reverse vaccinology methods to design peptide-based vaccines

Sharma, V., Singh, S., Ratnakar, T. S., & Prajapati, V. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90264-9.00029-5 · Cited by 15

Genome-Based Multi-Antigenic Epitopes Vaccine Construct Designing against Staphylococcus hominis Using Reverse Vaccinology and Biophysical Approaches

Nawaz, M., Ullah, A., Al-Harbi, A. I., Haq, M. U., Hameed, A. R., Ahmad, S., Aziz, A., Raziq, K., Khan, S., Irfan, M., et al. (2022) · Vaccines · doi:10.3390/vaccines10101729 · Cited by 14

COVID-19 Vaccines: Computational tools and Development

Osamor, V. C., Ikeakanam, E., Bishung, J., Abiodun, T. N., & Ekpo, R. H. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101164 · Cited by 13

Development of a multi-epitope vaccine against Acinetobacter baumannii: A comprehensive approach to combating antimicrobial resistance

Beig, M., Sholeh, M., Moradkasani, S., Shahbazi, B., & Badmasti, F. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319191 · Cited by 13

Vaccine Development for Human Leishmaniasis

Clímaco, M. D. C., Kraemer, L. R., & Fujiwara, R. T. (2023) · Springer eBooks · doi:10.1007/978-3-031-24355-4_14 · Cited by 13

Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence

Federico, L., Malone, B., Tennøe, S., Chaban, V., Osen, J. R., Gainullin, M., Smorodina, E., Kared, H., Akbar, R., Greiff, V., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1265044 · Cited by 13

Targeting Yezo Virus Structural Proteins for Multi-Epitope Vaccine Design Using Immunoinformatics Approach

Rahman, S., Chiou, C.-C., Almutairi, M. M., Ajmal, A., Batool, S., Javed, B., Tanaka, T., Chen, C., Alouffi, A., & Ali, A. (2024) · Viruses · doi:10.3390/v16091408 · Cited by 12

Promising vaccine models against astrovirus MLB2 using integrated vaccinomics and immunoinformatics approaches

Ali, S. L., Ali, A., Ullah, W., Khan, A., Khatrawi, E. M., Malik, A., Abduldayeva, A., Baiduissenova, A., Althagafi, H. J., & Fallatah, D. I. (2024) · Molecular Systems Design & Engineering · doi:10.1039/d3me00192j · Cited by 12

Bioinformatic Analysis of B- and T-cell Epitopes from SARS-CoV-2 Structural Proteins and their Potential Cross-reactivity with Emerging Variants and other Human Coronaviruses

Pacheco-Olvera, D. L., Remy-Hernández, S. S., García-Valeriano, M. G., Rivera-Hernández, T., & López-Macías, C. I. R. (2022) · Archives of Medical Research · doi:10.1016/j.arcmed.2022.10.007 · Cited by 12

Preclinical risk assessment strategy to mitigate the T-cell dependent immunogenicity of protein biotherapeutics: State of the art, challenges and future perspectives

Ianni, A. D., Barbero, L. M., Fraone, T., Cowan, K. J., & Sirtori, F. R. (2023) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2023.115500 · Cited by 12

Development of a tetravalent subunit vaccine against dengue virus through a vaccinomics approach

Basheer, A., Jamal, S. B., Alzahrani, B., & Faheem, M. Q. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1273838 · Cited by 12

Discordance Between the Predicted Versus the Actually Recognized CD8+ T Cell Epitopes of HCMV pp65 Antigen and Aleatory Epitope Dominance

Lehmann, A. A., Zhang, T., Reche, P. A., & Lehmann, P. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2020.618428 · Cited by 12

An In-Silico Investigation to Design a Multi-Epitopes Vaccine against Multi-Drug Resistant Hafnia alvei

Alshabrmi, F. M., Alrumaihi, F., Alrasheedi, S. F., Al‐Megrin, W. A. I., Almatroudi, A., & Allemailem, K. S. (2022) · Vaccines · doi:10.3390/vaccines10071127 · Cited by 12

Immunoinformatic analysis of the whole proteome for vaccine design: An application to Clostridium perfringens

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