NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data

Montemurro, Alessandro; Schuster, Viktoria; Povlsen, Helle Rus; Bentzen, Amalie Kai; Jurtz, Vanessa Isabell; Chronister, William D.; Crinklaw, Austin; Hadrup, Sine Reker; Winther, Ole; Peters, Bjoern; Jessen, Leon Eyrich; Nielsen, Morten

doi:10.1038/s42003-021-02610-3

Cited by 159 publications

(273 citation statements)

References 36 publications

(61 reference statements)

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“…Future work combining longitudinal sampling with single-cell techniques could help explore the relationship between neutral clonal dynamics and cell type. Additionally, we know that TCR with similar sequences form clusters that often respond to similar stimulants [17,39], and methods are being developped to annotate repertoire with cluster membership [40] or specificity [41][42][43][44][45]. As these annotation become comprehensive, one will be able to study the dynamics of specificity clusters, and to assess the persistence of specific immune memories across different immune challenges.…”

Section: Discussionmentioning

confidence: 99%

Inferring the T-cells repertoire dynamics of healthy individuals

Koraichi

Ferri

Walczak

et al. 2022

Preprint

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The adaptive immune system is a diverse ecosystem that responds to pathogens by selecting cells with specific receptors. While clonal expansion in response to particular immune challenges has been extensively studied, we do not know the neutral dynamics that drive the immune system in absence of strong stimuli. Here we learn the parameters that underlie the clonal dynamics of the T-cell repertoire in healthy individuals of different ages, by applying Bayesian inference to longitudinal immune repertoire sequencing (RepSeq) data. Quantifying the experimental noise accurately for a given RepSeq technique allows us to disentangle real changes in clonal frequencies from noise. We find that the data are consistent with clone sizes following a geometric Brownian motion, and show that its predicted steady state is in quantitative agreement with the observed power-law behaviour of the clone-size distribution. The inferred turnover time scale of the repertoire increases substantially with patient age, and depends on the clone size in some individuals.

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Section: Discussionmentioning

confidence: 99%

Inferring the T-cells repertoire dynamics of healthy individuals

Koraichi

Ferri

Walczak

et al. 2022

Preprint

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“…A further implication is that no sequence feature will unambiguously distinguish TCRs from pre and post-selected repertoires. Many efforts have been made to connect TCR sequences to peptide recognition [25, 35, 36]. However, these approaches cannot yet be used to define the target peptidome of entire repertoires.…”

Section: Discussionmentioning

confidence: 99%

Population based selection shapes the T cell receptor repertoire during thymic development

Camaglia

Ryvkin

Greenstein

et al. 2022

Preprint

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One of the feats of adaptive immunity is its ability to recognize foreign pathogens while sparing the self. During maturation in the thymus, T cells are selected through the binding properties of their antigen-specific T-cell receptor (TCR), through the elimination of both weakly (positive selection) and strongly (negative selection) self-reactive receptors. However, the impact of thymic selection on the TCR repertoire is poorly understood. Here, we use transgenic Nur77-mice expressing a T-cell activation reporter to study the repertoires of thymic T cells at various stages of their development, including cells that do not pass selection. We combine high-throughput repertoire sequencing with statistical inference techniques to charactarize the selection of the TCR in these distinct subsets. We find small but significant differences in the TCR repertoire parameters between the maturation stages, which recapitulate known differentiation pathways leading to the CD4+ and CD8+ subtypes. These differences can be simulated by simple models of selection acting linearly on the sequence features. We find no evidence of specific sequences or sequence motifs or features that are suppressed by negative selection. These results are consistent with a collective or statistical model for T-cell specificity, where negative selection biases the repertoire away from self recognition, rather than ensuring lack of self-reactivity at the single-cell level.

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“…Several groups are interested in systematically studying the binding of TCR to peptides/MHC. Gielis et al (2020 ) developed a web tool TCRex for the prediction of T-cell receptor sequence epitope specificity, which allows users to upload TCR sequences and predict interaction with multiple known epitopes; Montemurro et al (2021 ) developed NetTCR-2.0, which enables accurate prediction of TCR–peptide binding by the “shallow” convolutional neural network; Jokinen et al (2021 ) developed TCRGP, a novel Gaussian process method that predicts recognition between T-cell receptors and epitopes, which has better performance in algorithm evaluation than existing state-of-the-art methods in epitope specificity predictions. Some databases have been built for curating such research; for example, the VDJdb database ( Bagaev et al, 2020 ) curates TCR sequences with known antigen specificities.…”

Section: Usage About Datasetsmentioning

confidence: 99%

CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration

Yu¹,

Wang²,

Kong

et al. 2022

Front. Bioeng. Biotechnol.

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Cancer vaccines have gradually attracted attention for their tremendous preclinical and clinical performance. With the development of next-generation sequencing technologies and related algorithms, pipelines based on sequencing and machine learning methods have become mainstream in cancer antigen prediction; of particular focus are neoantigens, mutation peptides that only exist in tumor cells that lack central tolerance and have fewer side effects. The rapid prediction and filtering of neoantigen peptides are crucial to the development of neoantigen-based cancer vaccines. However, due to the lack of verified neoantigen datasets and insufficient research on the properties of neoantigens, neoantigen prediction algorithms still need to be improved. Here, we recruited verified cancer antigen peptides and collected as much relevant peptide information as possible. Then, we discussed the role of each dataset for algorithm improvement in cancer antigen research, especially neoantigen prediction. A platform, Cancer Antigens Database (CAD, http://cad.bio-it.cn/), was designed to facilitate users to perform a complete exploration of cancer antigens online.

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NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data

Cited by 159 publications

References 36 publications

Inferring the T-cells repertoire dynamics of healthy individuals

Inferring the T-cells repertoire dynamics of healthy individuals

Population based selection shapes the T cell receptor repertoire during thymic development

CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration

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