Journal article
SAbPred: a structure-based antibody prediction server
- Abstract:
- SAbPred is a server that makes predictions of the properties of antibodies focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules. SAbPred is a single platform containing multiple applications which can: number and align sequences; automatically generate antibody variable fragment homology models; annotate such models with estimated accuracy alongside sequence and structural properties including potential developability issues; predict paratope residues; and predict epitope patches on protein antigens. The server is available at http://opig.stats.ox.ac.uk/webapps/sabpred.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.0MB, Terms of use)
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(Supplementary materials, zip, 791.7KB, Terms of use)
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- Publisher copy:
- 10.1093/nar/gkw361
Authors
+ Engineering and Physical Sciences Research Council
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- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/G037280/1
- Publisher:
- Oxford University Press
- Journal:
- Nucleic Acids Research More from this journal
- Volume:
- 44
- Issue:
- W1
- Pages:
- W474–W478
- Publication date:
- 2016-04-29
- Acceptance date:
- 2016-04-24
- DOI:
- EISSN:
-
1362-4962
- Language:
-
English
- Pubs id:
-
pubs:619219
- UUID:
-
uuid:79f9f2a1-4e98-4715-b72c-9065791bb814
- Local pid:
-
pubs:619219
- Source identifiers:
-
619219
- Deposit date:
-
2016-05-03
- ARK identifier:
Terms of use
- Copyright holder:
- Dunbar et al
- Copyright date:
- 2016
- Rights statement:
- © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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