Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

Milighetti, Martina; Shawe‐Taylor, John; Chain, Benny

doi:10.3389/fphys.2021.730908

Cited by 16 publications

(25 citation statements)

References 75 publications

(125 reference statements)

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“…The expansion of peptide-TCR binding prediction to consider additional information including V(D)J gene families, TCR CDR3 of α-chain, and epitopes presented by MHC II molecules is an intriguing area for future research. Additionally, molecular biology studies have highlighted the importance of structural and physicochemical homology in TCR cross-reactivity ( Milighetti et al, 2021 ), which will be incorporated into current neoantigen identification pipelines to make a further improvement in our future work.…”

Section: Discussionmentioning

confidence: 99%

iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features

Zhang,

Jian,

et al. 2023

Front. Genet.

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Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/.

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Section: Discussionmentioning

confidence: 99%

iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features

Zhang,

Jian,

et al. 2023

Front. Genet.

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“…The polymorphic regions of TCRs, antibodies, and MHC molecules in humans are concentrated in select regions of these proteins, specifically in their key intermolecular interaction sites, while the rest of their three-dimensional structures are exceptionally well conserved. This structural conservation and localized variability appears to point towards molecular modeling as a key tool, but modern computational approaches are either too costly i.e., slow and inefficient, or too inaccurate to allow for detailed conclusions regarding the proximity of specific amino acids [28, 39, 59]. Many of the best performing machine learning approaches are “black box” algorithms that do not allow the user to determine how or why certain classifications are made.…”

Section: Discussionmentioning

confidence: 99%

“…Excellent software exist for the analysis of TCR sequences [23][24][25][26][27][28], antibodies [25,[29][30][31][32], and peptides [33][34][35]. Conversely, the analyses of viral sequences are largely dependent on multi-sequence alignments, phylogenetic analysis, or custom pipelines from researchers in a specific viral sub-field.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach to the Characterization of Immune Repertoires Using AIMS: An Automated Immune Molecule Separator

Boughter

Meier‐Schellersheim

2022

Preprint

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The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments - collectively referred to as antigens - initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS - Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.

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“…The recognition of peptide-MHC complexes by αβ T cell receptors (TCRs) forms the critical step in the initiation of a T cell-mediated adaptive immune response 1 . T cells acquire both the α and β chain of the TCR through somatic recombination of V(D)J gene segments and random non-templated nucleotide additions and deletions at the gene junctional boundaries, thereby forming the highly diverse CDR3α and CDR3β regions that make the most extensive physical contacts with the peptide antigen 2 (Fig. 1a).…”

Section: Introductionmentioning

confidence: 99%

STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data

Kwee

Messemaker

Marcus

et al. 2023

Preprint

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The prediction of peptide-MHC (pMHC) recognition by α&#946 T-cell receptors (TCRs) remains a major biomedical challenge. Here, we develop STAPLER (Shared TCR And Peptide Language bidirectional Encoder Representations from transformers), a transformer language model that uses a joint TCRalphabeta-peptide input to allow the learning of patterns within and between TCRalphabeta and peptide sequences that encode recognition. First, we demonstrate how data leakage during negative data generation can confound performance estimates of neural network-based models in predicting TCR-pMHC specificity. We then demonstrate that, because of its pre-training and fine-tuning masked language modeling tasks, STAPLER outperforms both neural network-based and distance-based ML models in predicting the recognition of known antigens in an independent dataset, in particular for antigens for which little related data is available. Based on this ability to efficiently learn from limited labeled TCR-peptide data, STAPLER is well-suited to utilize growing TCR-pMHC datasets to achieve accurate prediction of TCR-pMHC specificity.

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Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

Cited by 16 publications

References 75 publications

iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features

iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features

An Integrated Approach to the Characterization of Immune Repertoires Using AIMS: An Automated Immune Molecule Separator

STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data

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