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Computational identification of CDR3 sequence archetypes among immunoglobulin sequences in chronic lymphocytic leukemia.

ABSTRACT: The leukemia cells of unrelated patients with chronic lymphocytic leukemia (CLL) display a restricted repertoire of immunoglobulin (Ig) gene rearrangements with preferential usage of certain Ig gene segments. We developed a computational method to rigorously quantify biases in Ig sequence similarity in large patient databases and to identify groups of patients with unusual levels of sequence similarity. We applied our method to sequences from 1577 CLL patients through the CLL Research Consortium (CRC), and identified 67 similarity groups into which roughly 20% of all patients could be assigned. Immunoglobulin light chain class was highly correlated within all groups and light chain gene usage was similar within sets. Surprisingly, over 40% of the identified groups were composed of somatically mutated genes. This study significantly expands the evidence that antigen selection shapes the Ig repertoire in CLL.

SUBMITTER: Messmer BT

PROVIDER: S-EPMC2692898 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Computational identification of CDR3 sequence archetypes among immunoglobulin sequences in chronic lymphocytic leukemia.

Messmer Bradley T BT Raphael Benjamin J BJ Aerni Sarah J SJ Widhopf George F GF Rassenti Laura Z LZ Gribben John G JG Kay Neil E NE Kipps Thomas J TJ

Leukemia research 20080721 3

The leukemia cells of unrelated patients with chronic lymphocytic leukemia (CLL) display a restricted repertoire of immunoglobulin (Ig) gene rearrangements with preferential usage of certain Ig gene segments. We developed a computational method to rigorously quantify biases in Ig sequence similarity in large patient databases and to identify groups of patients with unusual levels of sequence similarity. We applied our method to sequences from 1577 CLL patients through the CLL Research Consortium ...[more]

PMID: 18640719

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Project description:BackgroundAlthough the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing.ResultsHere we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors.ConclusionsThe experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.

Dataset Information

Computational identification of CDR3 sequence archetypes among immunoglobulin sequences in chronic lymphocytic leukemia.

Publications

Computational identification of CDR3 sequence archetypes among immunoglobulin sequences in chronic lymphocytic leukemia.

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