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MAbs. 2016 May-Jun;8(4):751-60. doi: 10.1080/19420862.2016.1158370.

Length-independent structural similarities enrich the antibody CDR canonical class model.

Author information

1
a Department of Statistics , University of Oxford , Peter Medawar Building, Oxford , UK.
2
b Doctoral Training Center , University of Oxford , Rex Richards Building, Oxford , UK.
3
c Informatics Department , UCB Pharma , Slough , UK.
4
d Roche Pharma Research and Early Development , Therapeutic Modalities, Roche Innovation Center , Penzberg , Germany.
5
e Roche Pharma Research and Early Development , PRED Informatics, Roche Innovation Center , Penzberg , Germany.
6
f Department of Antibody Discovery and Protein Engineering , MedImmune Ltd , Granta Park, Cambridge , UK.

Abstract

Complementarity-determining regions (CDRs) are antibody loops that make up the antigen binding site. Here, we show that all CDR types have structurally similar loops of different lengths. Based on these findings, we created length-independent canonical classes for the non-H3 CDRs. Our length variable structural clusters show strong sequence patterns suggesting either that they evolved from the same original structure or result from some form of convergence. We find that our length-independent method not only clusters a larger number of CDRs, but also predicts canonical class from sequence better than the standard length-dependent approach. To demonstrate the usefulness of our findings, we predicted cluster membership of CDR-L3 sequences from 3 next-generation sequencing datasets of the antibody repertoire (over 1,000,000 sequences). Using the length-independent clusters, we can structurally classify an additional 135,000 sequences, which represents a ∼20% improvement over the standard approach. This suggests that our length-independent canonical classes might be a highly prevalent feature of antibody space, and could substantially improve our ability to accurately predict the structure of novel CDRs identified by next-generation sequencing.

KEYWORDS:

Canonical class; clustering; complementarity determining regions; length-independent; loop modeling

PMID:
26963563
PMCID:
PMC4966832
DOI:
10.1080/19420862.2016.1158370
[Indexed for MEDLINE]
Free PMC Article

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