Dataset Information

VDJdb: a curated database of T-cell receptor sequences with known antigen specificity.

ABSTRACT: The ability to decode antigen specificities encapsulated in the sequences of rearranged T-cell receptor (TCR) genes is critical for our understanding of the adaptive immune system and promises significant advances in the field of translational medicine. Recent developments in high-throughput sequencing methods (immune repertoire sequencing technology, or RepSeq) and single-cell RNA sequencing technology have allowed us to obtain huge numbers of TCR sequences from donor samples and link them to T-cell phenotypes. However, our ability to annotate these TCR sequences still lags behind, owing to the enormous diversity of the TCR repertoire and the scarcity of available data on T-cell specificities. In this paper, we present VDJdb, a database that stores and aggregates the results of published T-cell specificity assays and provides a universal platform that couples antigen specificities with TCR sequences. We demonstrate that VDJdb is a versatile instrument for the annotation of TCR repertoire data, enabling a concatenated view of antigen-specific TCR sequence motifs. VDJdb can be accessed at https://vdjdb.cdr3.net and https://github.com/antigenomics/vdjdb-db.

SUBMITTER: Shugay M

PROVIDER: S-EPMC5753233 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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VDJdb: a curated database of T-cell receptor sequences with known antigen specificity.

Shugay Mikhail M Bagaev Dmitriy V DV Zvyagin Ivan V IV Vroomans Renske M RM Crawford Jeremy Chase JC Dolton Garry G Komech Ekaterina A EA Sycheva Anastasiya L AL Koneva Anna E AE Egorov Evgeniy S ES Eliseev Alexey V AV Van Dyk Ewald E Dash Pradyot P Attaf Meriem M Rius Cristina C Ladell Kristin K McLaren James E JE Matthews Katherine K KK Clemens E Bridie EB Douek Daniel C DC Luciani Fabio F van Baarle Debbie D Kedzierska Katherine K Kesmir Can C Thomas Paul G PG Price David A DA Sewell Andrew K AK Chudakov Dmitriy M DM

Nucleic acids research 20180101 D1

The ability to decode antigen specificities encapsulated in the sequences of rearranged T-cell receptor (TCR) genes is critical for our understanding of the adaptive immune system and promises significant advances in the field of translational medicine. Recent developments in high-throughput sequencing methods (immune repertoire sequencing technology, or RepSeq) and single-cell RNA sequencing technology have allowed us to obtain huge numbers of TCR sequences from donor samples and link them to T ...[more]

PMID: 28977646

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Project description:IntroductionT-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. While a large number of TCR sequences specific to different antigenic peptides are known to date, the structural data describing the conformation and contacting residues for TCR-peptide-MHC complexes is relatively limited. In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of these complexes using TCR sequences with known specificity.MethodsIdentification of CDR3 sequences and their further clustering, based on available spatial structures, V- and J-genes of corresponding T-cell receptors, and epitopes, was performed using the VDJdb database. Modeling of the selected CDR3 loops was conducted using a stepwise introduction of single amino acid substitutions to the template PDB structures, followed by optimization of the TCR-peptide-MHC contacting interface using the Rosetta package applications. Statistical analysis and recursive feature elimination procedures were carried out on computed energy values and properties of contacting amino acid residues between CDR3 loops and peptides, using R.ResultsUsing the set of 29 complex templates (including a template with SARS-CoV-2 antigen) and 732 specificity records, we built a database of 1585 model structures carrying substitutions in either TCRα or TCRβ chains with some models representing the result of different mutation pathways for the same final structure. This database allowed us to analyze features of amino acid contacts in TCR - peptide interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues.ConclusionOur results provide a methodology for creating high-quality TCR-peptide-MHC models for antigens of interest that can be utilized to predict TCR specificity.

Dataset Information

VDJdb: a curated database of T-cell receptor sequences with known antigen specificity.

Publications

VDJdb: a curated database of T-cell receptor sequences with known antigen specificity.

Similar Datasets

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