Antibody specific B-cell epitope predictions: leveraging information from antibody–antigen protein complexes
Open accessAbstract
B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Arbitrary molecular surfaces appear to be indistinguishable from epitope regions, suggesting that predictions should be performed with reference to a particular antibody.
“However, arbitrary molecular surfaces appear to be indistinguishable from epitope regions [167–169] suggesting that predictions should be performed with reference to a particular antibody [116, 118–120].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [120]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 20,617 · see citing works
The PyMOL Molecular Graphics System
DeLano, W. L. (2002) · Medical Entomology and Zoology · Cited by 18,147 · see citing works
SWISS-MODEL: homology modelling of protein structures and complexes
Waterhouse, A. M., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T., Rempfer, C., Bordoli, L., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky427 · Cited by 13,005 · see citing works
Markov Chain Monte Carlo in Practice
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927 · see citing works
I-TASSER server for protein 3D structure prediction
Zhang, Y. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-40 · Cited by 5,124 · see citing works
The ClusPro web server for protein–protein docking
Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., & Vajda, S. (2017) · Nature Protocols · doi:10.1038/nprot.2016.169 · Cited by 3,297 · see citing works
Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network
Saha, S., & Raghava, G. P. S. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21078 · Cited by 1,758 · 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
The immune epitope database (IEDB) 3.0
Vita, R., Overton, J. A., Greenbaum, J. A., Ponomarenko, J., Clark, J., Cantrell, J. R., Wheeler, D., Gabbard, J., Hix, D., Sette, A., et al. (2014) · Nucleic Acids Res. · doi:10.1093/nar/gku938 · Cited by 1,086 · see citing works
Predicting protein–protein interactions based only on sequences information
Shen, J., Zhang, J., Luo, X., Zhu, W., Yu, K., Chen, K., Li, Y., & Jiang, H. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0607879104 · Cited by 996 · see citing works
Selection of representative protein data sets
Hobohm, U., Scharf, M. E., Schneider, R., & Sander, C. (1992) · Protein Science · doi:10.1002/pro.5560010313 · Cited by 818 · see citing works
Improved methods for predicting peptide binding affinity to MHC class II molecules
Jensen, K. K., Andreatta, M., Marcatili, P., Buus, S., Greenbaum, J., Zhen, Y., Sette, A., Peters, B., & Nielsen, M. (2018) · Immunology · doi:10.1111/imm.12889 · Cited by 805 · 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
Self-citationPrediction 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
Nielsen, M., & Andreatta, M. (2016) · Genome Medicine · doi:10.1186/s13073-016-0288-x · Cited by 521 · see citing works
Identification of conformational B-cell Epitopes in an antigen from its primary sequence
Ansari, H. R., & Raghava, G. P. S. (2010) · Immunome Research · doi:10.1186/1745-7580-6-6 · Cited by 362 · see citing works
Structural analysis of B-cell epitopes in antibody: protein complexes
Kringelum, J. V., Nielsen, M., Padkjær, S. B., & Lund, O. (2013) · Molecular Immunology · doi:10.1016/j.molimm.2012.06.001 · Cited by 305 · see citing works
Modeling and docking of antibody structures with Rosetta
Weitzner, B. D., Jeliazkov, J. R., Lyskov, S., Marze, N. A., Kuroda, D., Frick, R., Adolf‐Bryfogle, J., Biswas, N., Dunbrack, R. L., & Gray, J. J. (2017) · Nature Protocols · doi:10.1038/nprot.2016.180 · Cited by 266 · see citing works
Rep‐Seq: uncovering the immunological repertoire through next‐generation sequencing
Benichou, J. I. C., Ben‐Hamo, R., Louzoun, Y., & Efroni, S. (2011) · Immunology · doi:10.1111/j.1365-2567.2011.03527.x · Cited by 233 · see citing works
Application of asymmetric statistical potentials to antibody-protein docking
Brenke, R., Hall, D., Chuang, G., Comeau, S. R., Bohnuud, T., Beglov, D., Schueler-Furman, O., Vajda, S., & Kozakov, D. (2012) · Bioinform. · doi:10.1093/bioinformatics/bts493 · Cited by 205 · see citing works
Improving B-cell epitope prediction and its application to global antibody–antigen docking
Krawczyk, K., Liu, X., Baker, T., Shi, J., & Deane, C. M. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu190 · Cited by 174 · see citing works
Parapred: antibody paratope prediction using convolutional and recurrent neural networks
Liberis, E., Veličković, P., Sormanni, P., Vendruscolo, M., & Liò, P. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty305 · Cited by 165 · see citing works
Predicting the accuracy of protein–ligand docking on homology models
Bordogna, A., Pandini, A., & Bonati, L. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21601 · Cited by 107 · see citing works
Prediction of site-specific interactions in antibody–antigen complexes: the proABC method and server
Olimpieri, P. P., Chailyan, A., Tramontano, A., & Marcatili, P. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt369 · Cited by 90 · 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
Krawczyk, K., Baker, T., Shi, J., & Deane, C. M. (2013) · Protein Engineering Design and Selection · doi:10.1093/protein/gzt043 · Cited by 87 · see citing works
Antibody informatics for drug discovery
Shirai, H., Prades, C., Vita, R., Marcatili, P., Popovic, B., Xu, J., Overington, J. P., Hirayama, K., Soga, S., Tsunoyama, K., et al. (2014) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2014.07.006 · Cited by 73 · see citing works
PEASE: predicting B-cell epitopes utilizing antibody sequence
Sela‐Culang, I., Ashkenazi, S., Peters, B., & Ofran, Y. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu790 · Cited by 71 · see citing works
Defining the limits of homology modeling in information‐driven protein docking
Rodrigues, J., Melquiond, A. S. J., Karaca, E., Trellet, M. E., van Dijk, M., van Zundert, G. C. P., Schmitz, C., de Vries, S. J., Bordogna, A., Bonati, L., et al. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24382 · Cited by 63 · see citing works
IEDB-3D: structural data within the immune epitope database
Ponomarenko, J. V., Papangelopoulos, N., Zajonc, D. M., Peters, B., Sette, A., & Bourne, P. E. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq888 · Cited by 62 · 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
Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server
Marcatili, P., Olimpieri, P. P., Chailyan, A., & Tramontano, A. L. (2014) · Nature Protocols · doi:10.1038/nprot.2014.189 · Cited by 58 · see citing works
Computationally-driven identification of antibody epitopes
Hua, C. K., Gacerez, A. T., Sentman, C. L., Ackerman, M. E., Choi, Y., & Bailey‐Kellogg, C. (2017) · eLife · doi:10.7554/elife.29023 · Cited by 44 · see citing works
Grandison, S., Roberts, C., & Morris, R. J. (2009) · Journal of Computational Biology · doi:10.1089/cmb.2008.0083 · Cited by 39 · see citing works
Structure-based cross-docking analysis of antibody–antigen interactions
Kilambi, K. P., & Gray, J. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-08414-y · Cited by 37 · see citing works
Rienzo, L. D., Milanetti, E., Lepore, R., Olimpieri, P. P., & Tramontano, A. L. (2017) · Scientific Reports · doi:10.1038/srep45053 · Cited by 29 · see citing works
Yoo, D. K., Lee, S. R., Jung, Y., Han, H., Lee, H. K., Han, J., Kim, S., Chae, J., Ryu, T., & Chung, J. (2020) · Biomolecules · doi:10.3390/biom10030421 · Cited by 19 · see citing works
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 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Computational approaches to therapeutic antibody design: established methods and emerging trends
Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219 · see citing works
Akbar, R., Robert, P. A., Pavlović, M., Jeliazkov, J. R., Snapkov, I., Slabodkin, A., Weber, C. R., Scheffer, L., Miho, E., Haff, I. H., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108856 · Cited by 182 · see citing works
Animal immunization, in vitro display technologies, and machine learning for antibody discovery
Laustsen, A. H., Greiff, V., Karatt-Vellatt, A., Muyldermans, S., & Jenkins, T. P. (2021) · Trends in Biotechnology · doi:10.1016/j.tibtech.2021.03.003 · Cited by 157 · see citing works
DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations
Høie, M. H., Gade, F. S., Johansen, J. M., Würtzen, C., Winther, O., Nielsen, M., & Marcatili, P. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1322712 · Cited by 151 · see citing works
Galanis, K. A., Nastou, K. C., Papandreou, N. C., Petichakis, G. N., Pigis, D. G., & Iconomidou, V. A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22063210 · Cited by 129 · see citing works
Guest, J. D., Vreven, T., Zhou, J., Moal, I. H., Jeliazkov, J. R., Gray, J. J., Weng, Z., & Pierce, B. G. (2020) · Structure · doi:10.1016/j.str.2021.01.005 · Cited by 119 · see citing works
Akbar, R., Bashour, H., Rawat, P., Robert, P. A., Smorodina, E., Cotet, T., Karlsen, K. F., Frank, R., Mehta, B. B., Vu, M. H., et al. (2022) · mAbs · doi:10.1080/19420862.2021.2008790 · Cited by 117 · see citing works
Designing a multi-epitope vaccine to provoke the robust immune response against influenza A H7N9
Tarrahimofrad, H., Rahimnahal, S., Zamani, J., Jahangirian, E., & Aminzadeh, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-03932-2 · Cited by 102 · see citing works
Khalid, K., & Poh, C. L. (2023) · Vaccines · doi:10.3390/vaccines11071264 · Cited by 99 · see citing works
Critical review of conformational B-cell epitope prediction methods
Cia, G., Pucci, F., & Rooman, M. J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac567 · Cited by 100 · see citing works
Greiff, V., Yaari, G., & Cowell, L. G. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.10.010 · Cited by 89 · see citing works
Khuat, T. T., Bassett, R., Otte, E. A., Grevis-James, A., & Gabryś, B. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108585 · Cited by 98 · see citing works
Bukhari, S. N. H., Jain, A., Haq, E., Mehbodniya, A., & Webber, J. L. (2022) · Pathogens · doi:10.3390/pathogens11020146 · Cited by 87 · see citing works
Guarra, F., & Colombo, G. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00513 · Cited by 79 · see citing works
Dey, J., Mahapatra, S. R., Singh, P. K., Patro, S., Kushwaha, G. S., Misra, N., & Suar, M. (2021) · Microbial Pathogenesis · doi:10.1016/j.micpath.2021.105171 · Cited by 73 · see citing works
A Review of Deep Learning Methods for Antibodies
Graves, J., Byerly, J., Priego, E., Makkapati, N., Parish, S., Medellin, B. P., & Berrondo, M. (2020) · Antibodies · doi:10.3390/antib9020012 · Cited by 73 · see citing works
How repertoire data are changing antibody science
Marks, C., & Deane, C. M. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.rev120.010181 · Cited by 68 · see citing works
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
Robert, P. A., Akbar, R., Frank, R., Pavlović, M., Widrich, M., Snapkov, I., Slabodkin, A., Chernigovskaya, M., Scheffer, L., Smorodina, E., et al. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00372-4 · Cited by 64 · 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
Local and Global Rigidification Upon Antibody Affinity Maturation
Fernández‐Quintero, M. L., Loeffler, J. R., Bacher, L. M., Waibl, F., Seidler, C. A., & Liedl, K. R. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00182 · Cited by 59 · see citing works
Li, T., Li, Y., Zhu, X., He, Y., Wu, Y., Ying, T., & Xie, Z. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.007 · Cited by 59 · see citing works
Reliable Serological Diagnostic Tests for Arboviruses: Feasible or Utopia?
Kerkhof, K., Falconi‐Agapito, F., Esbroeck, M. V., Talledo, M., & Ariën, K. K. (2019) · Trends in Microbiology · doi:10.1016/j.tim.2019.11.005 · Cited by 57 · see citing works
Accelerating therapeutic protein design with computational approaches toward the clinical stage
Chen, Z., Wang, X., Chen, X., Huang, J., Wang, C., Wang, J., & Wang, Z. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.04.027 · Cited by 49 · see citing works
Toward real-world automated antibody design with combinatorial Bayesian optimization
Khan, A. A., Cowen-Rivers, A. I., Grosnit, A., Deik, D.-G.-X., Robert, P. A., Greiff, V., Smorodina, E., Rawat, P., Akbar, R., Dreczkowski, K., et al. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100374 · Cited by 54 · see citing works
Structural Aspects of the Allergen-Antibody Interaction
Pomés, A., Mueller, G., & Chruszcz, M. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.02067 · Cited by 40 · see citing works
Predicting antibody binders and generating synthetic antibodies using deep learning
Lim, Y. W., Adler, A. S., & Johnson, D. S. (2022) · mAbs · doi:10.1080/19420862.2022.2069075 · Cited by 49 · see citing works
Akhtar, N., Magdaleno, J. S. L., Ranjan, S., Wani, A. K., Grewal, R. K., Oliva, R., Shaikh, A. R., Cavallo, L., & Chawla, M. (2023) · Vaccines · doi:10.3390/vaccines11020364 · Cited by 45 · see citing works
The ClusPro AbEMap web server for the prediction of antibody epitopes
Desta, I., Kotelnikov, S., Jones, G. S., Ghani, U., Abyzov, M., Kholodov, Y. A., Standley, D. M., Beglov, D., Vajda, S., & Kozakov, D. (2023) · Nature Protocols · doi:10.1038/s41596-023-00826-7 · Cited by 45 · see citing works
Multi‐perspectives and challenges in identifying B‐cell epitopes
Kumar, N., Bajiya, N., Patiyal, S., & Raghava, G. P. S. (2023) · Protein Science · doi:10.1002/pro.4785 · Cited by 40 · see citing works
Recent Progress in Antibody Epitope Prediction
Zeng, X., Bai, G., Sun, C., & Ma, B. (2023) · Antibodies · doi:10.3390/antib12030052 · Cited by 39 · see citing works
Motamedi, H., Ari, M. M., Shahlaei, M., Moradi, S., Farhadikia, P., Alvandi, A., & Abiri, R. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05197-0 · Cited by 37 · see citing works
Mullin, M. J., McClory, J., Haynes, W. A., Grace, J., Robertson, N. J., & Heeke, G. V. (2024) · mAbs · doi:10.1080/19420862.2024.2341443 · Cited by 34 · see citing works
Fatoba, A. J., Maharaj, L., Adeleke, V. T., Okpeku, M., Adeniyi, A. A., & Adeleke, M. A. (2021) · Vaccine · doi:10.1016/j.vaccine.2021.01.003 · Cited by 34 · see citing works
Kim, D. N., McNaughton, A. D., & Kumar, N. (2024) · Bioengineering · doi:10.3390/bioengineering11020185 · Cited by 34 · see citing works
Methods for sequence and structural analysis of B and T cell receptor repertoires
Teraguchi, S., Saputri, D. S., Llamas-Covarrubias, M. A., Davila, A., Díez, D., Nazlica, S. A., Rozewicki, J., Ismanto, H. S., Wilamowski, J., Xie, J., et al. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.07.008 · Cited by 33 · see citing works
Design of multi-epitope vaccine candidate against Brucella type IV secretion system (T4SS)
Yin, Z., Li, M., Niu, C., Yu, M., Xie, X., Haimiti, G., Guo, W., Shi, J., He, Y., Ding, J., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0286358 · Cited by 32 · see citing works
Phan, I. Q., Subramanian, S., Kim, D. E., Murphy, M., Pettie, D., Carter, L. P., Anishchenko, I. V., Barrett, L. K., Craig, J. K., Tillery, L. M., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-83730-y · Cited by 30 · see citing works
Designing a multi-epitope vaccine against coxsackievirus B based on immunoinformatics approaches
Huang, S., Zhang, C., Li, J., Dai, Z., Huang, J., Deng, F., Wang, X., Yue, X., Hu, X., Li, Y., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.933594 · Cited by 31 · see citing works
AbAdapt: an adaptive approach to predicting antibody–antigen complex structures from sequence
Davila, A., Xu, Z., Li, S., Rozewicki, J., Wilamowski, J., Kotelnikov, S., Kozakov, D., Teraguchi, S., & Standley, D. M. (2022) · Bioinformatics Advances · doi:10.1093/bioadv/vbac015 · Cited by 30 · see citing works
Human Monoclonal IgE Antibodies—a Major Milestone in Allergy
Smith, S. A., Chruszcz, M., Chapman, M. D., & Pomés, A. (2022) · Current Allergy and Asthma Reports · doi:10.1007/s11882-022-01055-w · Cited by 30 · see citing works
Habib, A., Liang, Y., Xu, X., Zhu, N., & Xie, J. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25042432 · Cited by 27 · see citing works
Kumar, N., Tripathi, S., Sharma, N., Patiyal, S., Naorem, L. D., & Raghava, G. P. S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108083 · Cited by 27 · see citing works
Qiao, X., Qu, L., Guo, Y., & Hoshino, T. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c05997 · Cited by 26 · see citing works
Accurate prediction of immunoglobulin proteins using machine learning model
Ghulam, A., Sikander, R., Ali, F., Swati, Z. N. K., Unar, A., & Talpur, D. B. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100885 · Cited by 26 · see citing works
Shah, M., Jaan, S., Fatima, B., Javed, M. S., Amjad, A., Khan, A., Afridi, S. G., Nishan, U., Iqbal, A., & Nawaz, H. (2020) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-020-10076-w · Cited by 25 · see citing works
A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features
Lu, S., Li, Y., Ma, Q., Nan, X., & Zhang, S. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.890943 · Cited by 23 · see citing works
Israeli, S., & Louzoun, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae084 · Cited by 24 · see citing works
Lim, H. X., Masomian, M., Khalid, K., Kumar, A. U., MacAry, P. A., & Poh, C. L. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23084341 · Cited by 22 · see citing works
AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding
Clifford, J. N., Richardson, E., Peters, B., & Nielsen, M. (2025) · Science Advances · doi:10.1126/sciadv.adu1823 · Cited by 22 · see citing works
Clark, J. J., Gilray, J. A., Orton, R. J., Baird, M., Wilkie, G. S., Filipe, A. D. S., Johnson, N., McInnes, C. J., Kohl, A., & Biek, R. (2020) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0008133 · Cited by 20 · see citing works
Yoo, D. K., Lee, S. R., Jung, Y., Han, H., Lee, H. K., Han, J., Kim, S., Chae, J., Ryu, T., & Chung, J. (2020) · Biomolecules · doi:10.3390/biom10030421 · Cited by 19 · see citing works
Riding the wave of innovation: immunoinformatics in fish disease control
Razali, S. A., Shamsir, M. S., Ishak, N. F., Low, C. F., & Azemin, W.-A. (2023) · PeerJ · doi:10.7717/peerj.16419 · Cited by 19 · see citing works
Computational Methods to Predict Conformational B-Cell Epitopes
Carroll, M., Rosenbaum, E., & Viswanathan, R. (2024) · Biomolecules · doi:10.3390/biom14080983 · Cited by 19 · see citing works
SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs
Qiu, T., Zhang, L., Chen, Z., Wang, Y., Mao, T., Wang, C., Cun, Y., Zheng, G., Yan, D., Zhou, M., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad427 · Cited by 20 · see citing works
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 · see citing works
Rahman, S., & Das, A. K. (2021) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-021-10287-9 · Cited by 18 · see citing works
AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning
Breimann, S., Kamp, F., Steiner, H., & Frishman, D. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168717 · Cited by 15 · see citing works
Enzyme Engineering Strategies for the Bioenhancement of l-Asparaginase Used as a Biopharmaceutical
Miranda, J., Lefin, N., Beltrán, J. F., Belén, L. H., Tsipa, A., Farías, J. G., & Zamorano, M. (2023) · BioDrugs · doi:10.1007/s40259-023-00622-5 · Cited by 17 · see citing works
Cuevas, S. A. P., Etchebest, C., & da Silva, F. L. B. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00895 · Cited by 17 · see citing works
Organism-specific training improves performance of linear B-cell epitope prediction
Ashford, J., Reis-Cunha, J. L., Lobo, I., Lobo, F. P., & Campelo, F. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab536 · Cited by 16 · see citing works
Advances in antibody discovery from human BCR repertoires
Xu, Z., Ismanto, H. S., Zhou, H., Saputri, D. S., Sugihara, F., & Standley, D. M. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.1044975 · Cited by 15 · see citing works
Designing a Multi-Epitope Subunit Vaccine against VP1 Major Coat Protein of JC Polyomavirus
Kanse, S., Khandelwal, M., Pandey, R. K., Khokhar, M., Desai, N. S., & Kumbhar, B. V. (2023) · Vaccines · doi:10.3390/vaccines11071182 · Cited by 15 · see citing works
In silico design of a multi-epitope vaccine against Mycobacterium avium subspecies paratuberculosis
Guo, W., Wang, X., Hu, J., Zhang, B., Zhao, L., Zhang, G., Qi, J., Wei, Z., Bao, Y., Tian, M., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1505313 · Cited by 15 · see citing works
Lastwika, K. J., Kunihiro, A. G., Solan, J. L., Zhang, Y., Taverne, L. R., Shelley, D., Rho, J.-H., Randolph, T. W., Li, C. I., Grogan, E. L., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.add8469 · Cited by 15 · see citing works
Immunoinformatic-driven design and evaluation of multi-epitope mRNA vaccine targeting HIV-1 gp120
Ahmed, M. Z., Rao, T., Mutahir, Z., Ahmed, S., Ullah, N., & Ojha, S. C. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1480025 · Cited by 14 · see citing works
Biased anti-idiotype response in rabbits leads to high-affinity monoclonal antibodies to biologics
Großerichter-Wagener, C., Kos, D., van Leeuwen, A., Dijk, L., Jeremiasse, J., Loeff, F. C., & Rispens, T. (2020) · mAbs · doi:10.1080/19420862.2020.1814661 · Cited by 14 · see citing works
Hermawan, A., Damai, F. I., Martin, L., Chrisdianto, M., Julianto, N. M., Pramanda, I. T., & Gustiananda, M. (2022) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-022-10467-1 · Cited by 13 · see citing works
Shi, H., Zhu, Y., Shang, K., Tian, T., Yin, Z., Shi, J., He, Y., Ding, J., Wang, Q., & Zhang, F. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307877 · Cited by 13 · see citing works
Krause, T., Röckendorf, N., Meckelein, B., Sinnecker, H., Schwager, C., Möckel, S., Jappe, U., & Frey, A. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.565243 · Cited by 13 · see citing works
Akbar, R., Robert, P. A., Pavlović, M., Jeliazkov, J. R., Snapkov, I., Slabodkin, A., Weber, C. R., Scheffer, L., Miho, E., Haff, I. H., et al. (2021) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000475562 · Cited by 16 · see citing works
Integrating Bioinformatics Strategies in Cancer Immunotherapy: Current and Future Perspectives
Washah, H. N., Salifu, E. Y., Soremekun, O. S., El-Rashedy, A. A. E., Munsamy, G., Olotu, F. A., & Soliman, M. E. S. (2020) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207323666200427113734 · Cited by 12 · see citing works
Akbar, R., Robert, P. A., Pavlović, M., Jeliazkov, J. R., Snapkov, I., Slabodkin, A., Weber, C. R., Scheffer, L., Miho, E., Haff, I. H., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/759498 · Cited by 11 · see citing works
Kwon, H., Ko, S., Ha, K., Lee, J. K., & Choi, Y. (2024) · PLoS ONE · doi:10.1371/journal.pone.0306254 · Cited by 12 · see citing works
Angaitkar, P., Aljrees, T., Pandey, S. K., Kumar, A., Janghel, R. R., Sahu, T. P., Singh, K. U., & Singh, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-41179-1 · Cited by 9 · see citing works
Computational detection of antigen-specific B cell receptors following immunization
Abbate, M. F., Dupic, T., Vigne, E., Shahsavarian, M. A., Walczak, A. M., & Mora, T. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2401058121 · Cited by 9 · see citing works
Computational prediction of multiple antigen epitopes
Viswanathan, R., Carroll, M., Roffe, A., Fajardo, J. E., & Fiser, A. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae556 · Cited by 10 · see citing works
Ghanbarpour, A., Jiang, M., Foster, D., & Chai, Q. (2023) · iScience · doi:10.1016/j.isci.2023.106036 · Cited by 9 · see citing works
Physicochemical determinants of antibody-protein interactions
Karadag, M., Arslan, M., Kaleli, N. E., & Kalyoncu, S. (2019) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2019.08.011 · Cited by 9 · see citing works
Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm
Kadam, K., Peerzada, N., Karbhal, R., Sawant, S., Valadi, J. K., & Kulkarni‐Kale, U. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.709951 · Cited by 9 · see citing works
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian OptimisationPreprint
Khan, A., Cowen-Rivers, A. I., Deik, D.-G.-X., Grosnit, A., Robert, P. A., Greiff, V., Smorodina, E., Rawat, P., Akbar, R., Dreczkowski, K., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4115860 · Cited by 9 · see citing works
Angaitkar, P., Janghel, R. R., & Sahu, T. P. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01294-8 · Cited by 8 · see citing works
Allemailem, K. S. (2025) · Pharmaceuticals · doi:10.3390/ph18091258 · Cited by 7 · see citing works
Woolums, A. R., & Swiderski, C. (2021) · Current Issues in Molecular Biology · doi:10.21775/cimb.042.605 · Cited by 7 · see citing works
In silico Techniques for Prospecting and Characterizing Monoclonal Antibodies
Manieri, T. M., Magalhães, C. G., Takata, D. Y., Batalha-Carvalho, J. V., & Moro, A. M. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.94366 · Cited by 7 · see citing works
Liu, M., Ren, T., Zhang, L., Li, P., Zhong, Z., Zhou, L., Qin, Y., Ouyang, K., Chen, Y., Huang, W., et al. (2024) · Virology · doi:10.1016/j.virol.2024.110315 · Cited by 6 · see citing works
Lutz, T., Breuck, J. D., Salehi, S., & Leiske, M. N. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202513765 · Cited by 6 · see citing works
Improving B-cell epitope prediction
Yu, H., Joseph‐McCarthy, D., & Vajda, S. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104489 · Cited by 11 · see citing works
Pratiwi, S. E., Ysrafil, Y., Mardhia, M., Mahyarudin, M., Ilmiawan, M. I., Trianto, H. F., Liana, D. F., & Amia, Y. (2024) · Bioimpacts · doi:10.34172/bi.2024.27846 · Cited by 6 · see citing works
Computational epitope binning reveals functional equivalence of sequence-divergent paratopes
Mahita, J., Kim, D., Son, S., Choi, Y., Kim, H., & Bailey‐Kellogg, C. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.04.036 · Cited by 6 · see citing works
Sahu, T. K., Meher, P. K., Choudhury, N. K., & Rao, A. R. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac356 · Cited by 6 · see citing works
Mora-Ochoa, Y. I., & Ramírez-Cando, L. (2024) · The Veterinary Journal · doi:10.1016/j.tvjl.2024.106240 · Cited by 5 · see citing works
Matsuzaka, Y., & Yashiro, R. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4030101 · Cited by 5 · see citing works
Khalaj‐Hedayati, A., Chua, C., Smooker, P. M., Moosavi, S., Manta, O. P., Helal, M. H., Ibrahim, M., El-Bahy, Z. M., Supriyanto, G., & Lee, K. W. (2023) · Nanomaterials · doi:10.3390/nano13202796 · Cited by 5 · see citing works
Linguistics-based formalization of the antibody language as a basis for antibody language models
Vu, M. H., Robert, P. A., Akbar, R., Świątczak, B., Sandve, G. K., Haug, D. T. T., & Greiff, V. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00642-3 · Cited by 15 · see citing works
Chomicz, D., Dudzic, P., Wrobel, S., Gawłowski, T., Demharter, S., Spreafico, R., Minoux, H., Phillips, A., & Krawczyk, K. (2025) · doi:10.1101/2025.10.17.683056 · Cited by 4 · see citing works
“However, including antibody information has been shown to provide more information than the antigen alone for epitope prediction (Jespersen et al. 2019; Krawczyk et al. 2014).”
Chomicz, D., Dudzic, P., Wrobel, S., Gawlowski, T., Demharter, S., Spreafico, R., Minoux, H., Phillips, A., & Krawczyk, K. (2026) · mAbs · doi:10.1080/19420862.2026.2630438 · Cited by 4 · see citing works
A Comprehensive in Silico Analysis for Identification of Immunotherapeutic Epitopes of HPV-18
Gupta, B., Kumar, A., & Sridevi, P. (2021) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-021-10285-x · Cited by 4 · see citing works
Wang, F., Wang, S., Tong, L., Wang, H., Chen, H.-T., Wang, X., Yin, X., Sun, Y., Gao, X., & Ren, S. (2025) · Virology · doi:10.1016/j.virol.2025.110700 · Cited by 3 · see citing works
Chae, J. P., Vasquez, R., Song, J. H., Pajarillo, E. A. B., Hwang, I., & Kang, D. (2024) · Journal of Animal Science and Technology · doi:10.5187/jast.2024.e72 · Cited by 3 · see citing works
- Authors
- Jespersen, M. C., Mahajan, S., Peters, B., Nielsen, M., & Marcatili, P.
- Affiliations
- Technical University of Denmark (DK)
- La Jolla Institute for Immunology (US)
- National University of General San Martín (AR)
- Corresponding author
- Nielsen, Morten
- Marcatili, Paolo
- Venue
- Frontiers in Immunology
- Publisher
- Frontiers Media SA
- Published
- 2019-02-26
- Volume
- 10
- Pages
- 298-298
- ISSN
- 1664-3224 (ISSN-L)
- PMC
- PMC6399414
- OpenAlex
- W2917097596
- Cited by
- 148
- References
- 38
- Citations by year
- 2026: 13 · 2025: 19 · 2024: 25 · 2023: 26 · 2022: 20 · 2021: 20 · 2020: 17 · 2019: 7
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Immunology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (HHSN272201200010C)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- B-cell epitope prediction, antibody binding sites, immune complexes, physicochemical properties, geometric features, B cell receptor, antibody specificity, humoral immunity, machine learning prediction
- MeSH terms
- Protein Domains, Algorithms, Amino Acid Sequence, Animals, Antibodies, Antibody Specificity, Antigen-Antibody Complex, Binding Sites, Antibody, Humans, Models, Molecular, Monte Carlo Method, Reproducibility of Results, Neural Networks, Computer, Epitope Mapping, Epitopes, B-Lymphocyte
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
