Prediction of site-specific interactions in antibody–antigen complexes: the proABC method and server
Open accessAbstract
MOTIVATION: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode. RESULTS: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen. AVAILABILITY: http://www.biocomputing.it/proABC. CONTACT: anna.tramontano@uniroma1.it or paolo.marcatili@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 [112]
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- Authors
- Olimpieri, P. P., Chailyan, A., Tramontano, A., & Marcatili, P.
- Affiliations
- Istituto Pasteur (IT)
- Corresponding author
- Tramontano, Anna
- Marcatili, Paolo
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-06-26
- Volume
- 29
- Issue
- 18
- Pages
- 2285-2291
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3753563
- OpenAlex
- W2132580736
- Cited by
- 90
- References
- 40
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 8 · 2023: 5 · 2022: 7 · 2021: 12 · 2020: 13 · 2019: 8 · 2018: 12 · 2017: 7 · 2016: 5 · 2015: 2 · 2014: 6
- FWCI
- 1.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion, other-oa)
- Funders
- King Abdullah University of Science and Technology (KUK-I1-012-43, Award No. KUK-I1-012-43)
- 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
- antigen-antibody reaction, antibody binding sites, antibody sequences, random forest, contact prediction, molecular docking, antibody structure
- MeSH terms
- Antigen-Antibody Complex, Artificial Intelligence, Models, Molecular, Software, Internet, Sequence Analysis, Protein
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