SCALOP: sequence-based antibody canonical loop structure annotation

Abstract Motivation Canonical forms of the antibody complementarity-determining regions (CDRs) were first described in 1987 and have been redefined on multiple occasions since. The canonical forms are often used to approximate the antibody binding site shape as they can be predicted from sequence. A rapid predictor would facilitate the annotation of CDR structures in the large amounts of repertoire data now becoming available from next generation sequencing experiments. Results SCALOP annotates CDR canonical forms for antibody sequences, supported by an auto-updating database to capture the latest cluster information. Its accuracy is comparable to that of a standard structural predictor but it is 800 times faster. The auto-updating nature of SCALOP ensures that it always attains the best possible coverage. Availability and implementation SCALOP is available as a web application and for download under a GPLv3 license at opig.stats.ox.ac.uk/webapps/scalop. Supplementary information Supplementary data are available at Bioinformatics online.

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

[2]  Roland L. Dunbrack,et al.  A new clustering of antibody CDR loop conformations. , 2011, Journal of molecular biology.

[3]  C. Deane,et al.  Length-independent structural similarities enrich the antibody CDR canonical class model , 2016, mAbs.

[4]  C. Deane,et al.  CODA: A combined algorithm for predicting the structurally variable regions of protein models , 2001, Protein science : a publication of the Protein Society.

[5]  Yoonjoo Choi,et al.  FREAD revisited: Accurate loop structure prediction using a database search algorithm , 2010, Proteins.

[6]  A. Lesk,et al.  Canonical structures for the hypervariable regions of immunoglobulins. , 1987, Journal of molecular biology.

[7]  Jérôme Lane,et al.  IMGT®, the international ImMunoGeneTics information system® , 2004, Nucleic Acids Res..

[8]  Jeffrey J. Gray,et al.  Non-H3 CDR template selection in antibody modeling through machine learning , 2018, PeerJ.

[9]  James Hetherington,et al.  abYsis: Integrated Antibody Sequence and Structure-Management, Analysis, and Prediction. , 2017, Journal of molecular biology.

[10]  C. Deane,et al.  Structurally Mapping Antibody Repertoires , 2018, Front. Immunol..

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

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