SAbPred: a structure-based antibody prediction server

By James Dunbar, Konrad Krawczyk, Jinwoo Leem, Claire Marks, Jaroslaw Nowak, Cristian Regep, Guy Georges, Sebastian Kelm, B. Popovic, Charlotte M. Deane. 2016

197 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

AbPredict 2: A server for accurate and unstrained structure prediction of antibody variable domains (2018, methodology): “Automated web-servers, such as RosettaAntibody(Sircar, Kim, & Gray, 2009), PIGS(Marcatili, Rosi, & Tramontano, 2008), and SAbPred(Dunbar et al., 2016), start by identifying the segments corresponding to the CDRs and framework in the query sequence; template structures from the Protein Data Bank…”
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies (2022, methodology): “For each benchmark target, we predicted 137 structures using ABodyBuilder (37), DeepAb (14), ABlooper (15), and AlphaFold-Multimer (13).”