Comparative analysis of nanobody sequence and structure data
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Nanobodies are a class of antigen-binding protein derived from camelids that achieve comparable binding affinities and specificities to classical antibodies, despite comprising only a single 15 kDa variable domain. Their reduced size makes them an exciting target molecule with which we can explore the molecular code that underpins binding specificity-how is such high specificity achieved? Here, we use a novel dataset of 90 nonredundant, protein-binding nanobodies with antigen-bound crystal structures to address this question. To provide a baseline for comparison we construct an analogous set of classical antibodies, allowing us to probe how nanobodies achieve high specificity binding with a dramatically reduced sequence space. Our analysis reveals that nanobodies do not diversify their framework region to compensate for the loss of the VL domain. In addition to the previously reported increase in H3 loop length, we find that nanobodies create diversity by drawing their paratope regions from a significantly larger set of aligned sequence positions, and by exhibiting greater structural variation in their H1 and H2 loops.
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Recent computational analyses of large sets of antibody and nanobody sequences and structures demonstrate that non-trivial systematic differences between these molecules exist.
“Recent computational analyses of large sets of antibody and nanobody sequences and structures demonstrate that non-trivial systematic differences between these molecules exist [231, 232].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [232]
Nanobodies exhibit less sequence and structure variation across their framework regions, and similar levels of sequence variation as classical antibodies within the CDRH1 and CDRH2 loops.
“Specifically, nanobodies were found to exhibit less sequence and structure variation across their framework regions, and similar levels of sequence variation as classical antibodies within the CDRH1 and CDRH2 loops [232].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [232]
Nanobodies translate the same level of sequence variation into increased structural diversity across the CDRH1 and CDRH2 loops, which are not classifiable by established canonical rules, presenting additional challenges to computational structural modelling tools compared to classical antibodies.
“However, nanobodies translate this same level of sequence variation into increased structural diversity across the CDRH1 and CDRH2 loops, which are not classifiable by established canonical rules, presenting additional challenges to computational structural modelling tools compared to classical antibodies [232, 235].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [232]
Nanobody paratopes contain on average nearly three additional residues, and the paratope is drawn from a wider set of aligned sequence positions than classical antibody VH domain paratopes, comparable to the set used by the entire VH-VL paratope.
“Perhaps more significant in terms of challenges to computational modelling tools is that nanobody paratopes contain on average nearly three additional residues, and moreover the paratope is drawn from a wider set of aligned sequence positions than classical antibody VH domain paratopes, comparable to the set used by the entire VH-VL paratope [230, 232].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [232]
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- Authors
- Mitchell, L. S., & Colwell, L. J.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Colwell, Lucy J.
- Venue
- Proteins Structure Function and Bioinformatics
- Publisher
- Wiley
- Published
- 2018-04-15
- Volume
- 86
- Issue
- 7
- Pages
- 697-706
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC6033041
- OpenAlex
- W2793899290
- Cited by
- 233
- References
- 40
- Citations by year
- 2026: 31 · 2025: 34 · 2024: 40 · 2023: 31 · 2022: 27 · 2021: 31 · 2020: 16 · 2019: 16 · 2018: 7
- FWCI
- 12.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Biotechnology and Biological Sciences Research Council (1501548)
- Research Executive Agency (631609)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- 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
- Keywords
- nanobodies, camelid single-domain antibodies, antigen specificity, antibody binding sites, framework regions, protein crystal structure, binding affinity
- MeSH terms
- Antibodies, Antibody Specificity, Binding Sites, Antibody, Models, Molecular, Protein Conformation, Structure-Activity Relationship, Sequence Alignment, Single-Domain Antibodies
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