PLAbDab-nano: a database of camelid and shark nanobodies from patents and literature.


Journal

Nucleic acids research
ISSN: 1362-4962
Titre abrégé: Nucleic Acids Res
Pays: England
ID NLM: 0411011

Informations de publication

Date de publication:
10 Oct 2024
Historique:
accepted: 03 10 2024
revised: 13 09 2024
received: 06 08 2024
medline: 10 10 2024
pubmed: 10 10 2024
entrez: 10 10 2024
Statut: aheadofprint

Résumé

Nanobodies are essential proteins of the adaptive immune systems of camelid and shark species, complementing conventional antibodies. Properties such as their relatively small size, solubility and high thermostability make VHH (variable heavy domain of the heavy chain) and VNAR (variable new antigen receptor) modalities a promising therapeutic format and a valuable resource for a wide range of biological applications. The volume of academic literature and patents related to nanobodies has risen significantly over the past decade. Here, we present PLAbDab-nano, a nanobody complement to the Patent and Literature Antibody Database (PLAbDab). PLAbDab-nano is a self-updating, searchable repository containing ∼5000 annotated VHH and VNAR sequences. We describe the methods used to curate the entries in PLAbDab-nano, and highlight how PLAbDab-nano could be used to design diverse libraries, as well as find sequences similar to known patented or therapeutic entries. PLAbDab-nano is freely available as a searchable web server (https://opig.stats.ox.ac.uk/webapps/plabdab-nano/).

Identifiants

pubmed: 39385626
pii: 7816862
doi: 10.1093/nar/gkae881
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Medical Research Council
ID : MR/N013468/1
Pays : United Kingdom
Organisme : Exscientia
Organisme : Engineering and Physical Sciences Research Council
ID : EP/S024093/1
Organisme : Twist Bioscience
Organisme : University of Oxford

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.

Auteurs

Gemma L Gordon (GL)

Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK.

Alexander Greenshields-Watson (A)

Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK.

Parth Agarwal (P)

Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK.

Ashley Wong (A)

Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK.

Fergus Boyles (F)

Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK.

Alissa Hummer (A)

Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK.

Ana G Lujan Hernandez (AG)

Twist Bioscience, 681 Gateway Blvd, South San Francisco, CA 94080, USA.

Charlotte M Deane (CM)

Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK.

Classifications MeSH