Parapred: antibody paratope prediction using convolutional and recurrent neural networks
Open accessPublished version of a preprint
Abstract
Motivation: Antibodies play essential roles in the immune system of vertebrates and are powerful tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). Results: In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method significantly improves on the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. Availability and implementation: The Parapred method is freely available as a webserver at http://www-mvsoftware.ch.cam.ac.uk/and for download at https://github.com/eliberis/parapred. Supplementary information: Supplementary information is available at Bioinformatics online.
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?
Antibody i-Patch and Paratome were outperformed by machine learning approaches such as proABC, AntibodyInterfacePrediction, Parapred and AG-Fast-Parapred.
“Recently, Antibody i-Patch and Paratome were outperformed by machine learning approaches such as the random forest-based proABC [112], support-vector machine-based AntibodyInterfacePrediction [114] and the deep learning-based, Parapred [113] and AG-Fast-Parapred [115].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [113]
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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
(2014) · Cited by 33,419 · see citing works
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works
(2016) · Cited by 11,679 · see citing works
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · see citing works
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873 · see citing works
The therapeutic monoclonal antibody market
Ecker, D. M., Jones, S. D., & Levine, H. L. (2014) · mAbs · doi:10.4161/19420862.2015.989042 · Cited by 1,292 · see citing works
Efficient Unbound Docking of Rigid Molecules
Duhovny, D., Nussinov, R., & Wolfson, H. J. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45784-4_14 · Cited by 729 · see citing works
Standard conformations for the canonical structures of immunoglobulins 1 1Edited by I. A. Wilson
Al‐Lazikani, B., Lesk, A. M., & Chothia, C. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1354 · Cited by 707 · see citing works
SAbDab: the structural antibody database
Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., & Deane, C. M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1043 · Cited by 674 · see citing works
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
Agarwal, A., Negahban, S., & Wainwright, M. J. (2012) · The Annals of Statistics · doi:10.1214/12-aos1000 · Cited by 669 · see citing works
Assessment of blind predictions of protein–protein interactions: Current status of docking methods
Mendez, R., Leplae, R., Maria, L. D., & Wodak, S. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10393 · Cited by 438 · see citing works
ANARCI: antigen receptor numbering and receptor classification
Dunbar, J., & Deane, C. M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv552 · Cited by 421 · see citing works
Meiler, J., Zeidler, A., Schm�schke, F., & M�ller, M. (2001) · Journal of Molecular Modeling · doi:10.1007/s008940100038 · Cited by 271 · see citing works
Reichert, J. M. (2016) · mAbs · doi:10.1080/19420862.2016.1269580 · Cited by 244 · see citing works
Therapeutic antibodies: past, present and future
Leavy, O. (2010) · Nature reviews. Immunology · doi:10.1038/nri2763 · Cited by 216 · see citing works
Origins of specificity and affinity in antibody–protein interactions
Peng, H., Lee, K. H., Jian, J.-W., & Yang, A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1401131111 · Cited by 209 · see citing works
Engineering antibody therapeutics
Chiu, M. L., & Gilliland, G. L. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.07.012 · Cited by 165 · see citing works
Rapid and accurate in silico solubility screening of a monoclonal antibody library
Sormanni, P., Amery, L., Ekizoglou, S., Vendruscolo, M., & Popovic, B. (2017) · Scientific Reports · doi:10.1038/s41598-017-07800-w · Cited by 134 · see citing works
Self-citationProgress and challenges in predicting protein interfaces
Esmaielbeiki, R., Krawczyk, K., Knapp, B., Nebel, J., & Deane, C. M. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv027 · Cited by 130 · see citing works
Structural consensus among antibodies defines the antigen binding site
Kunik, V., Peters, B., & Ofran, Y. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002388 · Cited by 117 · 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
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
Tsuchiya, Y., & Mizuguchi, K. (2016) · Protein Science · doi:10.1002/pro.2874 · Cited by 86 · see citing works
Li, R., Si, D., Zeng, T., Ji, S., & He, J. J. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822490 · Cited by 65 · see citing works
Protein Structure, Neighbor Effect, and a New Index of Amino Acid Dissimilarities
Xia, X., & Xie, Z. (2002) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a003982 · Cited by 64 · see citing works
Towards recognition of protein function based on its structure using deep convolutional networks
Tavanaei, A., Maida, A. S., Kaniymattam, A., et al. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822509 · Cited by 23 · see citing works
X-CNN: Cross-modal convolutional neural networks for sparse datasets
Velickovic, P., Wang, D., Lane, N. D., et al. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7849978 · Cited by 19 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Janin, J., Henrick, K., Moult, J., Eyck, L. T., Sternberg, M. J., Vajda, S., ... & Wodak, S. J. (2003). CAPRI: a critical assessment of predicted interactions. Proteins: Structure, Function, and Bioinformatics, 52(1), pp. 2-9.
- Pei, J., & Grishin, N. V. (2001). AL2CO: calculation of positional conservation in a protein sequence alignment. Bioinformatics, 17(8), pp. 700-712.
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
“To the best of our knowledge AntibodyInterfacePrediction and AG-FastParapred are currently the best performing paratope prediction methods as compared to previously available methods; however, they were not compared against one another.”
InfluentialPrihoda, D., Maamary, J., Waight, A., Juan, V., Fayadat-Dilman, L., Svozil, D., & Bitton, D. A. (2022) · mAbs · doi:10.1080/19420862.2021.2020203 · Cited by 190 · see citing works
Antibody complementarity determining region design using high-capacity machine learning
Liu, G., Zeng, H., Mueller, J., Carter, B., Wang, Z., Schilz, J., Horny, G., Birnbaum, M. E., Ewert, S., & Gifford, D. K. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz895 · Cited by 157 · see citing works
Jespersen, M. C., Mahajan, S., Peters, B., Nielsen, M., & Marcatili, P. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00298 · Cited by 148 · see citing works
DLAB-deep learning methods for structure-based virtual screening of antibodies
Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab660 · Cited by 102 · see citing works
Antibody interface prediction with 3D Zernike descriptors and SVM
Daberdaku, S., & Ferrari, C. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty918 · Cited by 88 · see citing works
Brown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59 · 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
Lai, P.-K., Fernando, A., Cloutier, T. K., Kingsbury, J. S., Gokarn, Y., Halloran, K. T., Calero-Rubio, C., & Trout, B. L. (2021) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2020.12.014 · Cited by 45 · 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
“…(Eshak and Goupil-Lamy 2025; Hitawala and Gray 2024), co-folding (Boitreaud et al. 2024; Wohlwend et al. 2024), diffusive design (Bennett et al. 2024), docking (Ambrosetti et al. 2020; Sircar and Gray 2010; Jeliazkov et al. 2021) or binding prediction (Jespersen et al. 2019; Liberis et al. 2018).”
Information-driven modelling of antibody–antigen complexes
Ambrosetti, F., Jiménez-García, B., Roel-Touris, J., & Bonvin, A. M. J. J. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3362436 · Cited by 2 · see citing works
- Authors
- Liberis, E., Veličković, P., Sormanni, P., Vendruscolo, M., & Liò, P.
- Affiliations
- University of Cambridge (GB)
- Department of Computer Science and Technology, University of Cambridge, UK
- Corresponding author
- Liberis, Edgar
- Sormanni, Pietro
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-04-16
- Volume
- 34
- Issue
- 17
- Pages
- 2944-2950
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2801965704
- Cited by
- 165
- References
- 31
- Citations by year
- 2026: 17 · 2025: 18 · 2024: 25 · 2023: 29 · 2022: 25 · 2021: 25 · 2020: 15 · 2019: 10 · 2018: 1
- FWCI
- 4.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- 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
- Keywords
- antibody-antigen binding, hypervariable region, sequence-based prediction, convolutional neural networks, recurrent neural networks, probabilistic machine learning, rigid docking, web server
- MeSH terms
- Machine Learning, Deep Learning, Algorithms, Amino Acid Sequence, Antibodies, Binding Sites, Antibody, Models, Molecular, Neural Networks, Computer
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
