Structural Modeling of Lymphocyte Receptors and Their Antigens

Li, Songling; Wilamowski, Jan; Teraguchi, Shunsuke; Eerden, Floris J. van; Rozewicki, John; Davila, Ana; Xu, Zichang; Katoh, Kazutaka; Standley, Daron M.

doi:10.1007/978-1-4939-9728-2_17

Cited by 15 publications

(18 citation statements)

References 28 publications

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“…The fact that nearly 20% of the TCR CDRs that could show different conformations did so suggests that the canonical class model will struggle to accurately predict TCR CDR conformations. TCR modeling tools may provide more details through homology or de novo loop predictions (25, 50, 51). Overall, our results suggest that there are structural differences between TCR and antibody CDRs, and the differences we observe potentially help explain how these two receptors bind to their separate target antigens.…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of the CDR Loops of Antigen Receptors

2019

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The adaptive immune system uses two main types of antigen receptors: T-cell receptors (TCRs) and antibodies. While both proteins share a globally similar β-sandwich architecture, TCRs are specialized to recognize peptide antigens in the binding groove of the major histocompatibility complex, while antibodies can bind an almost infinite range of molecules. For both proteins, the main determinants of target recognition are the complementarity-determining region (CDR) loops. Five of the six CDRs adopt a limited number of backbone conformations, known as the “canonical classes”; the remaining CDR (β3in TCRs and H3 in antibodies) is more structurally diverse. In this paper, we first update the definition of canonical forms in TCRs, build an auto-updating sequence-based prediction tool (available at http://opig.stats.ox.ac.uk/resources) and demonstrate its application on large scale sequencing studies. Given the global similarity of TCRs and antibodies, we then examine the structural similarity of their CDRs. We find that TCR and antibody CDRs tend to have different length distributions, and where they have similar lengths, they mostly occupy distinct structural spaces. In the rare cases where we found structural similarity, the underlying sequence patterns for the TCR and antibody version are different. Finally, where multiple structures have been solved for the same CDR sequence, the structural variability in TCR loops is higher than that in antibodies, suggesting TCR CDRs are more flexible. These structural differences between TCR and antibody CDRs may be important to their different biological functions.

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Section: Discussionmentioning

confidence: 99%

Comparative Analysis of the CDR Loops of Antigen Receptors

2019

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“…Compared to other tools, TCRpMHCmodels is attractive in its ability to produce a quick prediction of the entire TCR-pMHC complex, without relying on docking methods, which still need to be optimised for this task (Peacock and Chain, 2021 ). Another interesting tool for this is ImmuneScape (Li et al, 2019 ), which also separately models the TCR and the pMHC, and combines them using a docking template. However, the complex biology of the system might always be a limiting factor for structure and binding prediction, from structural and/or sequence features.…”

Section: Discussionmentioning

confidence: 99%

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

2021

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The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.

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“…Next, using ImmuneScape, a tool that is capable of modeling MHC, peptide, and T cell receptor (TCR) complexes 15 , we evaluated the energetic stability of the pMHC complex. All complexes were modeled using 17 distinct TCRs from the Protein Data Bank (PDB) (see the description in the Methods section).…”

Section: Resultsmentioning

confidence: 99%

“…The pMHC complexes were modeled for each combination of 7 immunodominant AQP4 peptides (p11–30, p61–80, p91–110, p131–150, p201–220, p211–230 and p261–280), 3 HLA-DQA1 alleles ( DQA1*01:03 , DQA1*03:03 and DQA1*05:03 ), and 1 DQB allele ( DQB1*03:01 ) using a customized version of the ImmuneScape 15 workflow. ImmuneScape requires not only MHC and epitope, but also TCR sequences; the program then returns a 3D model of the TCR-peptide-MHC complex.…”

Section: Methodsmentioning

confidence: 99%

High cell surface expression and peptide binding affinity of HLA-DQA1*05:03, a susceptible allele of neuromyelitis optica spectrum disorders (NMOSD)

Beppu

Kinoshita

Wilamowski

et al. 2022

Sci Rep

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Neuromyelitis optica spectrum disorder (NMOSD) is a relapsing autoimmune disease characterized by the presence of pathogenic autoantibodies, anti-aquaporin 4 (AQP4) antibodies. Recently, HLA-DQA1*05:03 was shown to be significantly associated with NMOSD in a Japanese patient cohort. However, the specific mechanism by which HLA-DQA1*05:03 is associated with the development of NMOSD has yet to be elucidated. In the current study, we revealed that HLA-DQA1*05:03 exhibited significantly higher cell surface expression levels compared to other various DQA1 alleles, and that its expression strongly depended on the amino acid sequence of the α1 domain, with a preference for leucine at position 75. Moreover, in silico analysis indicated that the HLA-DQ encoded by HLA-DQA1*05:03 preferentially presents immunodominant AQP4 peptides, and that the peptide major histocompatibility complexes (pMHCs) are more energetically stable in the presence of HLA-DQA1*05:03 than other HLA-DQA1 alleles. In silico 3D structural models were also applied to investigate the validity of the energetic stability of pMHCs. Taken together, our findings indicate that HLA-DQA1*05:03 possesses a distinct property to play a pathogenic role in the development of NMOSD.

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Structural Modeling of Lymphocyte Receptors and Their Antigens

Cited by 15 publications

References 28 publications

Comparative Analysis of the CDR Loops of Antigen Receptors

Comparative Analysis of the CDR Loops of Antigen Receptors

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

High cell surface expression and peptide binding affinity of HLA-DQA1*05:03, a susceptible allele of neuromyelitis optica spectrum disorders (NMOSD)

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