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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Peptide binding predictions for HLA DR, DP and DQ molecules

Wang, P., Sidney, J., Kim, Y.-H., Sette, A., Lund, O., Nielsen, M., & Peters, B. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-568 · Cited by 681 · see citing works

Prediction of residues in discontinuous B‐cell epitopes using protein 3D structures

Andersen, P. H., Nielsen, M., & Lund, O. (2006) · Protein Science · doi:10.1110/ps.062405906 · Cited by 631 · see citing works

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

Nielsen, M., Lundegaard, C., & Lund, O. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-238 · Cited by 601 · see citing works

A consensus epitope prediction approach identifies the breadth of murine TCD8+-cell responses to vaccinia virus

Moutaftsi, M., Peters, B., Pasquetto, V., Tscharke, D. C., Sidney, J., Bui, H., Grey, H. M., & Sette, A. (2006) · Nature Biotechnology · doi:10.1038/nbt1215 · Cited by 559 · see citing works

Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method

Peters, B., & Sette, A. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-132 · Cited by 517 · see citing works

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

Nielsen, M., & Lund, O. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-296 · Cited by 513 · see citing works

Immune epitope database analysis resource

Kim, Y., Ponomarenko, J. V., Zhu, Z., Tamang, D., Wang, P., Greenbaum, J., Lundegaard, C., Sette, A., Lund, O., Bourne, P. E., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks438 · Cited by 511 · see citing works

The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design

Fleri, W., Paul, S., Dhanda, S. K., Mahajan, S., Xu, X., Peters, B., & Sette, A. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.00278 · Cited by 501 · see citing works

pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens

Hundal, J., Carreno, B. M., Petti, A. A., Linette, G. P., Griffith, O. L., Mardis, E. R., & Griffith, M. (2016) · Genome Medicine · doi:10.1186/s13073-016-0264-5 · Cited by 454 · see citing works

NetMHCcons: a consensus method for the major histocompatibility complex class I predictions

Karosiene, E., Lundegaard, C., Lund, O., & Nielsen, M. (2011) · Immunogenetics · doi:10.1007/s00251-011-0579-8 · Cited by 397 · see citing works

Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries

Sidney, J., Assarsson, E., Moore, C., Ngo, S., Pinilla, C., Sette, A., & Peters, B. (2008) · Immunome Research · doi:10.1186/1745-7580-4-2 · Cited by 326 · see citing works

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification

Andreatta, M., Karosiene, E., Rasmussen, M., Stryhn, A., Buus, S., & Nielsen, M. (2015) · Immunogenetics · doi:10.1007/s00251-015-0873-y · Cited by 289 · see citing works

TepiTool: A Pipeline for Computational Prediction of T Cell Epitope Candidates

Paul, S., Sidney, J., Sette, A., & Peters, B. (2016) · Current Protocols in Immunology · doi:10.1002/cpim.12 · Cited by 274 · see citing works

The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding

Zhang, H., Lund, O., & Nielsen, M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp137 · Cited by 258 · see citing works

Pan-Specific Prediction of Peptide–MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity

Rasmussen, M., Fenoy, E., Harndahl, M. N., Kristensen, A. B., Nielsen, I. K., Nielsen, M., & Buus, S. (2016) · The Journal of Immunology · doi:10.4049/jimmunol.1600582 · Cited by 241 · see citing works

Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior

Kim, Y.-H., Sidney, J., Pinilla, C., Sette, A., & Peters, B. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-394 · Cited by 219 · see citing works

Predicting HLA CD4 Immunogenicity in Human Populations

Dhanda, S. K., Karosiene, E., Edwards, L., Grifoni, A., Paul, S., Andreatta, M., Weiskopf, D., Sidney, J., Nielsen, M., Peters, B., et al. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01369 · Cited by 155 · see citing works

Development of a novel clustering tool for linear peptide sequences

Dhanda, S. K., Vaughan, K. C., Schulten, V. M. J., Grifoni, A., Weiskopf, D., Sidney, J., Peters, B., & Sette, A. (2018) · Immunology · doi:10.1111/imm.12984 · Cited by 92 · see citing works

LYRA, a webserver for lymphocyte receptor structural modeling

Klausen, M. S., Anderson, M. V., Jespersen, M. C., Nielsen, M., & Marcatili, P. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv535 · Cited by 87 · see citing works

Recombinant factor VIIa analog in the management of hemophilia with inhibitors: results from a multicenter, randomized, controlled trial of vatreptacog alfa

Lentz, S. R., Ehrenforth, S., Karim, F. A., Matsushita, T., Weldingh, K. N., Windyga, J., & Mahlangu, J. N. (2014) · Journal of Thrombosis and Haemostasis · doi:10.1111/jth.12634 · Cited by 69 · see citing works

Determination of a Predictive Cleavage Motif for Eluted Major Histocompatibility Complex Class II Ligands

Paul, S., Karosiene, E., Dhanda, S. K., Jurtz, V. I., Edwards, L., Nielsen, M., Sette, A., & Peters, B. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01795 · Cited by 50 · see citing works

MHC-NP: Predicting peptides naturally processed by the MHC

Giguère, S., Drouin, A., Lacoste, A., Marchand, M., Corbeil, J., & Laviolette, F. (2013) · Journal of Immunological Methods · doi:10.1016/j.jim.2013.10.003 · Cited by 49 · see citing works

ImmunomeBrowser: a tool to aggregate and visualize complex and heterogeneous epitopes in reference proteins

Dhanda, S. K., Vita, R., Ha, B., Grifoni, A., Peters, B., & Sette, A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty463 · Cited by 42 · see citing works

Development of a strategy and computational application to select candidate protein analogues with reduced HLA binding and immunogenicity

Dhanda, S. K., Grifoni, A., Pham, J. W., Vaughan, K. C., Sidney, J., Peters, B., & Sette, A. (2017) · Immunology · doi:10.1111/imm.12816 · Cited by 25 · see citing works

Experimental validation of the RATE tool for inferring HLA restrictions of T cell epitopes

Paul, S., Arlehamn, C. S. L., Schulten, V. M. J., Westernberg, L., Sidney, J., Peters, B., & Sette, A. (2017) · BMC Immunology · doi:10.1186/s12865-017-0204-1 · Cited by 16 · see citing works

Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals

Grifoni, A., Weiskopf, D., Ramirez, S. I., Mateus, J., Dan, J. M., Moderbacher, C. R., Rawlings, S. A., Sutherland, A., Premkumar, L., Jadi, R. S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.015 · Cited by 3,595 · see citing works

Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR

Olson, R., Assaf, R., Brettin, T. S., Conrad, N. R., Cucinell, C. A., Davis, J. J., Dempsey, D. M., Dickerman, A., Dietrich, E. M., Kenyon, R. W., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1003 · Cited by 1,271 · see citing works

A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2

Grifoni, A., Sidney, J., Zhang, Y., Scheuermann, R. H., Peters, B., & Sette, A. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.03.002 · Cited by 920 · see citing works

SARS-CoV-2 vaccination induces immunological T cell memory able to cross-recognize variants from Alpha to Omicron

Tarke, A., Coelho, C. H., Zhang, Z., Dan, J. M., Yu, E. D., Methot, N., Bloom, N. I., Goodwin, B., Phillips, E., Mallal, S., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.015 · Cited by 858 · see citing works

Phenotype and kinetics of SARS-CoV-2-specific T cells in COVID-19 patients with acute respiratory distress syndrome

Weiskopf, D., Schmitz, K. S., Raadsen, M. P., Grifoni, A., Okba, N. M. A., Endeman, H., van den Akker, J. P. C., Molenkamp, R., Koopmans, M. P. G., van Gorp, E. C. M., et al. (2020) · Science Immunology · doi:10.1126/sciimmunol.abd2071 · Cited by 907 · see citing works

Impact of SARS-CoV-2 variants on the total CD4+ and CD8+ T cell reactivity in infected or vaccinated individuals

Tarke, A., Sidney, J., Methot, N., Yu, E. D., Zhang, Y., Dan, J. M., Goodwin, B., Rubiro, P., Sutherland, A., Wang, E., et al. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100355 · Cited by 706 · see citing works

Comprehensive analysis of T cell immunodominance and immunoprevalence of SARS-CoV-2 epitopes in COVID-19 cases

Tarke, A., Sidney, J., Kidd, C. K., Dan, J. M., Ramirez, S. I., Yu, E. D., Mateus, J., da Silva Antunes, R., Moore, E., Rubiro, P., et al. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100204 · Cited by 540 · see citing works

Imbalance of Regulatory and Cytotoxic SARS-CoV-2-Reactive CD4+ T Cells in COVID-19

Meckiff, B. J., Ramírez-Suástegui, C., Fajardo, V., Chee, S., Kusnadi, A., Simon, H. A., Eschweiler, S., Grifoni, A., Pelosi, E., Weiskopf, D., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.001 · Cited by 565 · see citing works

Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening

Basith, S., Manavalan, B., Shin, T. H., & Lee, G. (2020) · Medicinal Research Reviews · doi:10.1002/med.21658 · Cited by 311 · see citing works

T Cell Epitope Predictions

Peters, B., Nielsen, M., & Sette, A. (2020) · Annual Review of Immunology · doi:10.1146/annurev-immunol-082119-124838 · Cited by 268 · see citing works

The Immune Epitope Database (IEDB): 2024 update

Vita, R., Blazeska, N., Marrama, D., IEDB Curation Team Members, Shackelford, D., Zalman, L., Foos, G., Zarebski, L., Chan, K., Reardon, B., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1092 · Cited by 274 · see citing works

A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design

Chu, Y., Zhang, Y., Wang, Q., Zhang, L., Wang, X., Wang, Y., Salahub, D. R., Xu, Q., Wang, J., Jiang, X. X., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00459-7 · Cited by 190 · see citing works

Mapping and role of T cell response in SARS-CoV-2–infected mice

Zhuang, Z., Lai, X., Sun, J., Chen, Z., Zhang, Z., Dai, J., Liu, D., Li, Y., Li, F., Wang, Y., et al. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20202187 · Cited by 171 · see citing works

Computational and artificial intelligence-based methods for antibody development

Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178 · see citing works

TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors

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. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.640725 · Cited by 168 · see citing works

Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens

Ong, E., Wang, H., Wong, M. U., Seetharaman, M., Valdez, N., & He, Y. O. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa119 · Cited by 161 · see citing works

Identification of vaccine targets in pathogens and design of a vaccine using computational approaches

Rawal, K., Sinha, R., Abbasi, B. A., Chaudhary, A., Nath, S. K., Kumari, P., Preeti, P., Saraf, D., Singh, S., Mishra, K., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-96863-x · Cited by 159 · see citing works

Designing multi-epitope vaccine against Staphylococcus aureus by employing subtractive proteomics, reverse vaccinology and immuno-informatics approaches

Qamar, M. T. U., Ahmad, S., Fatima, I., Ahmad, F., Shahid, F., Naz, A., Abbasi, S., Khan, A., Mirza, M., Ashfaq, U., et al. (2021) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2021.104389 · Cited by 138 · see citing works

Designing Multi-Epitope Vaccines to Combat Emerging Coronavirus Disease 2019 (COVID-19) by Employing Immuno-Informatics Approach

Naz, A., Shahid, F., Butt, T. T., Awan, F. M., Ali, A., & Malik, A. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01663 · Cited by 130 · see citing works

Evolutionary Parasitology

Schmid‐Hempel, P. (2021) · doi:10.1093/oso/9780198832140.001.0001 · Cited by 130 · see citing works

Enzyme annotation in UniProtKB using Rhea

Morgat, A., Lombardot, T., Coudert, E., Axelsen, K. B., Neto, T. B., Géhant, S., Bansal, P., Bolleman, J. T., Gasteiger, E., de Castro, E., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz817 · Cited by 101 · see citing works

Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach

Mahmud, S., Rafi, M. O., Paul, G. K., Promi, M. M., Shimu, M. S. S., Biswas, S., Emran, T. B., Dhama, K., Alyami, S. A., Moni, M. A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-92176-1 · Cited by 101 · see citing works

The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases

Vizcaíno, J. A., Kubiniok, P., Kovalchik, K. A., Ma, Q., Duquette, J. D., Mongrain, I., Deutsch, E. W., Peters, B., Sette, A., Sirois, I., et al. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r119.001743 · Cited by 91 · see citing works

Immunogenicity and efficacy of the COVID-19 candidate vector vaccine MVA-SARS-2-S in preclinical vaccination

Tscherne, A., Schwarz, J. H., Rohde, C., Kupke, A., Kalodimou, G., Limpinsel, L., Okba, N. M. A., Bošnjak, B., Sandrock, I., Odak, I., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2026207118 · Cited by 88 · see citing works

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

Malone, B., Simovski, B., Moliné, C., Cheng, J., Gheorghe, M., Fontenelle, H., Vardaxis, I., Tennøe, S., Malmberg, J.-A., Stratford, R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-78758-5 · Cited by 85 · see citing works

Epitope prediction and identification- adaptive T cell responses in humans

Sidney, J., Peters, B., & Sette, A. (2020) · Seminars in Immunology · doi:10.1016/j.smim.2020.101418 · Cited by 80 · see citing works

Two-step fitness selection for intra-host variations in SARS-CoV-2

Li, J., Du, P., Yang, L., Zhang, J., Song, C., Chen, D., Song, Y., Ding, N., Hua, M., Han, K., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110205 · Cited by 79 · see citing works

Next-generation IEDB tools: a platform for epitope prediction and analysis

Yan, Z., Kim, K., Kim, H., Ha, B., Gambiez, A., Bennett, J., Mendes, M. F. D. A., Trevizani, R., Mahita, J., Richardson, E., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae407 · Cited by 76 · see citing works

Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy

İmani, S., Li, X., Chen, K., Maghsoudloo, M., Kaboli, P. J., Hashemi, M., Khoushab, S., & Li, X. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2024.1501010 · Cited by 78 · see citing works

Multi-Epitope Vaccine for Monkeypox Using Pan-Genome and Reverse Vaccinology Approaches

Swetha, R. G., Basu, S., Ramaiah, S., & Anbarasu, A. (2022) · Viruses · doi:10.3390/v14112504 · Cited by 72 · see citing works

Defining antigen targets to dissect vaccinia virus and monkeypox virus-specific T cell responses in humans

Grifoni, A., Zhang, Y., Tarke, A., Sidney, J., Rubiro, P., Reina-Campos, M., Filaci, G., Dan, J. M., Scheuermann, R. H., & Sette, A. (2022) · Cell Host & Microbe · doi:10.1016/j.chom.2022.11.003 · Cited by 72 · see citing works

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules

Mei, S., Li, F., Xiang, D., Ayala, R., Faridi, P., Webb, G. I., Illing, P. T., Rossjohn, J., Akutsu, T., Croft, N. P., et al. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa415 · Cited by 70 · see citing works

T Cell Responses Induced by Attenuated Flavivirus Vaccination Are Specific and Show Limited Cross-Reactivity with Other Flavivirus Species

Grifoni, A., Voic, H., Dhanda, S. K., Kidd, C. K., Brien, J. D., Buus, S., Stryhn, A., Durbin, A. P., Whitehead, S. S., Diehl, S. A., et al. (2020) · Journal of Virology · doi:10.1128/jvi.00089-20 · Cited by 68 · see citing works

Nanotechnology‐facilitated vaccine development during the coronavirus disease 2019 (COVID‐19) pandemic

Wang, Z., Cui, K., Costabel, U., & Zhang, X. (2022) · Exploration · doi:10.1002/exp.20210082 · Cited by 66 · see citing works

The Future of Blood Testing Is the Immunome

Arnaout, R., Prak, E. T. L., Schwab, N., Rubelt, F., & Community, T. A. I. R. R. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.626793 · Cited by 63 · see citing works

Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion

Zinsli, L. V., Stierlin, N., Loessner, M. J., & Schmelcher, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.12.024 · Cited by 63 · see citing works

Designing a Multi-Epitope Vaccine against Chlamydia trachomatis by Employing Integrated Core Proteomics, Immuno-Informatics and In Silico Approaches

Aslam, S., Ahmad, S., Noor, F., Ashfaq, U., Shahid, F., Rehman, A., Qamar, M. T. U., Alatawi, E., Alshabrmi, F. M., & Allemailem, K. S. (2021) · Biology · doi:10.3390/biology10100997 · Cited by 55 · see citing works

The Immune Epitope Database and Analysis Resource Program 2003–2018: reflections and outlook

Martini, S. R., Nielsen, M., Peters, B., & Sette, A. (2019) · Immunogenetics · doi:10.1007/s00251-019-01137-6 · Cited by 61 · see citing works

Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach

Alam, A., Khan, A., Imam, N., Siddiqui, M. F., Waseem, M., Malik, M. Z., & Ishrat, R. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa340 · Cited by 68 · see citing works

Distinct antibody repertoires against endemic human coronaviruses in children and adults

Khan, T., Rahman, M., Ali, F. A., Huang, S. S. Y., Ata, M., Zhang, Q., Bastard, P., Liu, Z., Jouanguy, E., Béziat, V., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.144499 · Cited by 58 · see citing works

Designing a Recombinant Vaccine against Providencia rettgeri Using Immunoinformatics Approach

Gul, S., Ahmad, S., Ullah, A., Ismail, S., Khurram, M., Qamar, M. T. U., Hakami, A. R., Alkhathami, A. G., Alrumaihi, F., & Allemailem, K. S. (2022) · Vaccines · doi:10.3390/vaccines10020189 · Cited by 58 · see citing works

Designing multi-epitope vaccine against human cytomegalovirus integrating pan-genome and reverse vaccinology pipelines

Biswas, R., Swetha, R. G., Basu, S., Roy, A., Ramaiah, S., & Anbarasu, A. (2024) · Biologicals · doi:10.1016/j.biologicals.2024.101782 · Cited by 58 · see citing works

Main Strategies for the Identification of Neoantigens

Gopanenko, A. V., Kosobokova, E. N., & Kosorukov, V. S. (2020) · Cancers · doi:10.3390/cancers12102879 · Cited by 57 · see citing works

An integrative reverse vaccinology, immunoinformatic, docking and simulation approaches towards designing of multi-epitopes based vaccine against monkeypox virus

Ullah, A., Shahid, F., Haq, M. U., Qamar, M. T. U., Irfan, M., Shaker, B., Ahmad, S., Alrumaihi, F., Allemailem, K. S., & Almatroudi, A. (2022) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2022.2125441 · Cited by 56 · see citing works

Modulating the quantity of HIV Env-specific CD4 T cell help promotes rare B cell responses in germinal centers

Lee, J. H., Hu, J. K., Georgeson, E., Nakao, C., Gröschel, B., Dileepan, T., Jenkins, M. K., Seumois, G., Vijayanand, P., Schief, W. R., et al. (2020) · The Journal of Experimental Medicine · doi:10.1084/jem.20201254 · Cited by 56 · see citing works

Immunoinformatics, molecular docking and dynamics simulation approaches unveil a multi epitope-based potent peptide vaccine candidate against avian leukosis virus

Elshafei, S. O., Mahmoud, N. A., & Almofti, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-53048-6 · Cited by 53 · see citing works

Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools

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Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

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