Antibody i-Patch prediction of the antibody binding site improves rigid local antibody–antigen docking
Abstract
Antibodies are a class of proteins indispensable for the vertebrate immune system. The general architecture of all antibodies is very similar, but they contain a hypervariable region which allows millions of antibody variants to exist, each of which can bind to different molecules. This binding malleability means that antibodies are an increasingly important category of biopharmaceuticals and biomarkers. We present Antibody i-Patch, a method that annotates the most likely antibody residues to be in contact with the antigen. We show that our predictions correlate with energetic importance and thus we argue that they may be useful in guiding mutations in the artificial affinity maturation process. Using our predictions as constraints for a rigid-body docking algorithm, we are able to obtain high-quality results in minutes. Our annotation method and re-scoring system for docking achieve their predictive power by using antibody-specific statistics. Antibody i-Patch is available from http://www.stats.ox.ac.uk/research/proteins/resources.
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Modelled antibody structures can be used at the Lead Identification stage to select surface exposed paratope residues for mutations or to characterize binding with respect to the cognate epitope.
“Modelled structures can be used at the Lead Identification stage to select surface exposed paratope residues for mutations [110] or to characterize the binding with respect to the cognate epitope [119].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [110]
Antibody i-Patch assigns a score to each residue with respect to its propensity to be part of the paratope, with high-scoring residues offering potential candidates for mutagenesis.
“For instance, statistical approaches such as Antibody i-Patch [110] assign a score to each residue with respect to its propensity to be part of the paratope, with high-scoring residues offering potential candidates for mutagenesis.”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [110]
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- Authors
- Krawczyk, K., Baker, T., Shi, J., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- UCB Pharma (United Kingdom) (GB)
- Chinese Academy of Sciences (CN)
- Venue
- Protein Engineering Design and Selection
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-09-04
- Volume
- 26
- Issue
- 10
- Pages
- 621-629
- ISSN
- 0269-2139 (ISSN-L)
- 1741-0126
- 1741-0134
- OpenAlex
- W2117012297
- Cited by
- 87
- References
- 29
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 4 · 2023: 9 · 2022: 8 · 2021: 11 · 2020: 12 · 2019: 7 · 2018: 13 · 2017: 6 · 2016: 2 · 2015: 3 · 2014: 4 · 2013: 1
- FWCI
- 1.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Protein Engineering Design and Selection (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- 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 docking, rigid-body docking, hypervariable region, affinity maturation, antibody design
- MeSH terms
- Algorithms, Antibodies, Antigens, Binding Sites, Computational Biology, Complementarity Determining Regions, Molecular Docking Simulation
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