Dataset Information

Limiting CDR-H3 diversity abrogates the antibody response to the bacterial polysaccharide ? 1?3 dextran.

ABSTRACT: Anti-polysaccharide Ab responses in mice are often oligoclonal, and the mechanisms involved in Ag-specific clone production and selection remain poorly understood. We evaluated the relative contribution of D(H) germline content versus N nucleotide addition in a classic oligoclonal, T-independent Ab response (? 1?3 dextran [DEX]) by challenging adult TdT-sufficient (TdT(+/+)) and TdT-deficient (TdT(-/-)) gene-targeted mice, limited to the use of a single D(H) gene segment (D-limited mice), with Enterobacter cloacae. D-limited mice achieved anti-DEX-specific levels of Abs that were broadly comparable to those of wild-type (WT) BALB/c mice. Sequence analysis of the third CDR of the H chain intervals obtained by PCR amplification of V(H) domain DNA from DEX-specific plasmablasts revealed the near universal presence of an aspartic acid residue (D99) at the V-D junction, irrespective of the composition of the D(H) locus. Although WT mice were able to use germline D(H) (DQ52, DSP, or DST) gene segment sequence, TdT activity, or both to produce D99, all three D-limited mouse strains relied exclusively on N addition. Additionally, in the absence of TdT, D-limited mice failed to produce a DEX response. Coupled with previous studies demonstrating a reduced response to DEX in TdT(-/-) mice with a WT D(H) locus, we concluded that in the case of the anti-DEX repertoire, which uses a short third CDR of the H chain, the anti-DEX response relies more intensely on sequences created by postnatal N nucleotide addition than on the germline sequence of the D(H).

SUBMITTER: Mahmoud TI

PROVIDER: S-EPMC3210069 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Limiting CDR-H3 diversity abrogates the antibody response to the bacterial polysaccharide α 1→3 dextran.

Mahmoud Tamer I TI Schroeder Harry W HW Kearney John F JF

Journal of immunology (Baltimore, Md. : 1950) 20110615 2

Anti-polysaccharide Ab responses in mice are often oligoclonal, and the mechanisms involved in Ag-specific clone production and selection remain poorly understood. We evaluated the relative contribution of D(H) germline content versus N nucleotide addition in a classic oligoclonal, T-independent Ab response (α 1→3 dextran [DEX]) by challenging adult TdT-sufficient (TdT(+/+)) and TdT-deficient (TdT(-/-)) gene-targeted mice, limited to the use of a single D(H) gene segment (D-limited mice), with E ...[more]

PMID: 21677133

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Project description:Antibodies are proteins generated by the adaptive immune system to recognize and counteract a plethora of pathogens through specific binding. This adaptive binding is mediated by structural diversity in the six complementary determining region (CDR) loops (H1, H2, H3, L1, L2 and L3), which also makes accurate structural modeling of CDRs challenging. Both homology and de novo modeling approaches have been used; to date, the former has achieved greater accuracy for the non-H3 loops. The homology modeling of non-H3 CDRs is more accurate because non-H3 CDR loops of the same length and type can be grouped into a few structural clusters. Most antibody-modeling suites utilize homology modeling for the non-H3 CDRs, differing only in the alignment algorithm and how/if they utilize structural clusters. While RosettaAntibody and SAbPred do not explicitly assign query CDR sequences to clusters, two other approaches, PIGS and Kotai Antibody Builder, utilize sequence-based rules to assign CDR sequences to clusters. While the manually curated sequence rules can identify better structural templates, because their curation requires extensive literature search and human effort, they lag behind the deposition of new antibody structures and are infrequently updated. In this study, we propose a machine learning approach (Gradient Boosting Machine [GBM]) to learn the structural clusters of non-H3 CDRs from sequence alone. The GBM method simplifies feature selection and can easily integrate new data, compared to manual sequence rule curation. We compare the classification results using the GBM method to that of RosettaAntibody in a 3-repeat 10-fold cross-validation (CV) scheme on the cluster-annotated antibody database PyIgClassify and we observe an improvement in the classification accuracy of the concerned loops from 84.5% ± 0.24% to 88.16% ± 0.056%. The GBM models reduce the errors in specific cluster membership misclassifications when the involved clusters have relatively abundant data. Based on the factors identified, we suggest methods that can enrich structural classes with sparse data to further improve prediction accuracy in future studies.

Dataset Information

Limiting CDR-H3 diversity abrogates the antibody response to the bacterial polysaccharide ? 1?3 dextran.

Publications

Limiting CDR-H3 diversity abrogates the antibody response to the bacterial polysaccharide α 1→3 dextran.

Similar Datasets

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