Can AlphaFold’s breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

McMaster, Benjamin; Thorpe, Christopher; Ogg, Graham; Deane, Charlotte M.; Koohy, Hashem

doi:10.1038/s41592-024-02240-7

Cited by 8 publications

(3 citation statements)

References 92 publications

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“…Fine-tuning of the AlphaFold model, or additional sampling or scoring strategies in AlphaFold2, AlphaFold3, and other deep learning structure prediction methods, may enable improved predictive success for TCR–pMHC complexes. Such advances would be applicable toward the major challenge of modeling and mapping of T cell specificities on a large scale (26).…”

Section: Discussionmentioning

confidence: 99%

“…The deep learning method AlphaFold (22) has shown impressive performance in predictive modeling (23) and has been adapted and tested for modeling TCR–pMHC complex structures (24, 25), which as noted recently can potentially be utilized for large-scale T cell specificity prediction (26). Given that immune recognition (including TCR–pMHC recognition) is generally not as accurately modeled as other protein complexes by AlphaFold (27), and that others have noted concerns about AlphaFold’s accuracy and utility in some scenarios (28), we tested the capability of AlphaFold to model TCR N17.1.2 in complex with its neoantigen targets, and also tested it for predictive modeling of complexes for other TCRs known to bind NRAS Q61K –HLA-A*01.…”

Section: Introductionmentioning

confidence: 99%

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Structural characterization and AlphaFold modeling of human T cell receptor recognition of NRAS cancer neoantigens

Wu,

Yin,

Chen

et al. 2024

Preprint

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T cell receptors (TCRs) that recognize cancer neoantigens are important for anti-cancer immune responses and immunotherapy. Understanding the structural basis of TCR recognition of neoantigens provides insights into their exquisite specificity and can enable design of optimized TCRs. We determined crystal structures of a human TCR in complex with NRAS Q61K and Q61R neoantigen peptides and HLA-A1 MHC, revealing the molecular underpinnings for dual recognition and specificity versus wild-type NRAS peptide. We then used multiple versions of AlphaFold to model the corresponding complex structures, given the challenge of immune recognition for such methods. Interestingly, one implementation of AlphaFold2 (TCRmodel2) was able to generate accurate models of the complexes, while AlphaFold3 also showed strong performance, although success was lower for other complexes. This study provides insights into TCR recognition of a shared cancer neoantigen, as well as the utility and practical considerations for using AlphaFold to model TCR-peptide-MHC complexes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structural characterization and AlphaFold modeling of human T cell receptor recognition of NRAS cancer neoantigens

Wu,

Yin,

Chen

et al. 2024

Preprint

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“…Currently, most TCR:pMHC specificity predictors use sequence based features only [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The incorporation of structural information offers a compelling strategy to encourage models to learn more generalisable physicochemical principles of complementarity [24][25][26]. However, accessing and generating structural information for TCR:pMHC specificity prediction poses new challenges, which is perhaps why only a handful of structure-aware methods exist to date [24,27].…”

Section: Introductionmentioning

confidence: 99%

T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity

Quast,

Abanades,

Guloglu

et al. 2024

Preprint

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T-cell receptor (TCR) structures are currently under-utilised in early-stage drug discovery and repertoire-scale informatics. Here, we leverage a large dataset of solved TCR structures from Immunocore to evaluate the current state-of-the-art for TCR structure prediction, and identify which regions of the TCR remain challenging to model. Through clustering analyses and the training of a TCR-specific model capable of large-scale structure prediction, we find that the alpha chain VJ-recombined loop (CDRA3) is as structurally diverse and correspondingly difficult to predict as the beta chain VDJ-recombined loop (CDRB3). This differentiates TCR variable domain loops from the genetically analogous antibody loops and supports the conjecture that both TCR alpha and beta chains are deterministic of antigen specificity. We hypothesise that the larger number of alpha chain joining genes compared to beta chain joining genes compensates for the lack of a diversity gene segment. Overall, our study demonstrates that valuable structure-function relationships can lie in alpha chains despite their simpler junctions. We also provide over 1.5M predicted TCR structures to enable repertoire structural analysis and elucidate strategies towards improving the accuracy of future TCR structure predictors.

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Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

O’Donnell,

Kanduri,

Isacchini

et al. 2024

Cell Systems

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Can AlphaFold’s breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

Cited by 8 publications

References 92 publications

Structural characterization and AlphaFold modeling of human T cell receptor recognition of NRAS cancer neoantigens

Structural characterization and AlphaFold modeling of human T cell receptor recognition of NRAS cancer neoantigens

T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

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