SAbPred: a structure-based antibody prediction server

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.

[1]  V. Giudicelli,et al.  IMGT unique numbering for immunoglobulin and T cell receptor variable domains and Ig superfamily V-like domains. , 2003, Developmental and comparative immunology.

[2]  Paolo Marcatili,et al.  PIGS: automatic prediction of antibody structures , 2008, Bioinform..

[3]  Morten Nielsen,et al.  Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking , 2012, PLoS Comput. Biol..

[4]  Roland L. Dunbrack,et al.  proteins STRUCTURE O FUNCTION O BIOINFORMATICS Improved prediction of protein side-chain conformations with SCWRL4 , 2022 .

[5]  H. Kettenberger,et al.  Developability assessment during the selection of novel therapeutic antibodies. , 2015, Journal of pharmaceutical sciences.

[6]  C. Deane,et al.  ABangle: characterising the VH-VL orientation in antibodies. , 2013, Protein engineering, design & selection : PEDS.

[7]  Charlotte M Deane,et al.  Predicting antibody complementarity determining region structures without classification. , 2011, Molecular bioSystems.

[8]  Luca Varani,et al.  Computational Docking of Antibody-Antigen Complexes, Opportunities and Pitfalls Illustrated by Influenza Hemagglutinin , 2011, International journal of molecular sciences.

[9]  J. Reichert,et al.  Development trends for human monoclonal antibody therapeutics , 2010, Nature Reviews Drug Discovery.

[10]  Juan C Almagro,et al.  Second antibody modeling assessment (AMA‐II) , 2014, Proteins.

[11]  G. P. Smith,et al.  Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface. , 1985, Science.

[12]  Haruki Nakamura,et al.  Kotai Antibody Builder: automated high-resolution structural modeling of antibodies , 2014, Bioinform..

[13]  Haruki Nakamura,et al.  Computer-aided antibody design , 2012, Protein engineering, design & selection : PEDS.

[14]  Charlotte M. Deane,et al.  ANARCI: antigen receptor numbering and receptor classification , 2015, Bioinform..

[15]  Qifang Xu,et al.  PyIgClassify: a database of antibody CDR structural classifications , 2014, Nucleic Acids Res..

[16]  Janice M Reichert,et al.  Antibodies to watch in 2015 , 2015, mAbs.

[17]  Mathieu Rouard,et al.  IMGT unique numbering for immunoglobulin and T cell receptor constant domains and Ig superfamily C-like domains. , 2005, Developmental and comparative immunology.

[18]  C. Milstein,et al.  Continuous cultures of fused cells secreting antibody of predefined specificity , 1975, Nature.

[19]  John P. Overington,et al.  Antibody informatics for drug discovery. , 2014, Biochimica et biophysica acta.

[20]  A. Lesk,et al.  Standard conformations for the canonical structures of immunoglobulins. , 1997, Journal of molecular biology.

[21]  A. Plückthun,et al.  Yet another numbering scheme for immunoglobulin variable domains: an automatic modeling and analysis tool. , 2001, Journal of molecular biology.

[22]  P. Labute,et al.  Antibody modeling assessment , 2011, Proteins.

[23]  Jiye Shi,et al.  SAbDab: the structural antibody database , 2013, Nucleic Acids Res..

[24]  E. Kabat,et al.  Sequences of proteins of immunological interest , 1991 .

[25]  Andrew C. R. Martin,et al.  Analysis and improvements to Kabat and structurally correct numbering of antibody variable domains. , 2008, Molecular immunology.

[26]  T. Blundell,et al.  Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.

[27]  Jiye Shi,et al.  Improving B-cell epitope prediction and its application to global antibody-antigen docking , 2014, Bioinform..

[28]  Brian D. Weitzner,et al.  Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization , 2014, Proteins.

[29]  François Ehrenmann,et al.  IMGT/DomainGapAlign: the IMGT® tool for the analysis of IG, TR, MH, IgSF, and MhSF domain amino acid polymorphism. , 2012, Methods in molecular biology.

[30]  Yanay Ofran,et al.  Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure , 2012, Nucleic Acids Res..

[31]  Andrew C. R. Martin.  Viewing multiple sequence alignments with the JavaScript Sequence Alignment Viewer (JSAV) , 2014, F1000Research.

[32]  C. Deane,et al.  Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking. , 2013, Protein engineering, design & selection : PEDS.