Literature DB >> 33718327

A Simplified Amino Acidic Alphabet to Unveil the T-Cells Receptors Antigens: A Computational Perspective.

Raffaele Iannuzzi1, Grazisa Rossetti2, Andrea Spitaleri3, Raoul J P Bonnal2, Massimiliano Pagani2,4, Luca Mollica4.   

Abstract

The exposure to pathogens triggers the activation of adaptive immune responses through antigens bound to surface receptors of antigen presenting cells (APCs). T cell receptors (TCR) are responsible for initiating the immune response through their physical direct interaction with antigen-bound receptors on the APCs surface. The study of T cell interactions with antigens is considered of crucial importance for the comprehension of the role of immune responses in cancer growth and for the subsequent design of immunomodulating anticancer drugs. RNA sequencing experiments performed on T cells represented a major breakthrough for this branch of experimental molecular biology. Apart from the gene expression levels, the hypervariable CDR3α/β sequences of the TCR loops can now be easily determined and modelled in the three dimensions, being the portions of TCR mainly responsible for the interaction with APC receptors. The most direct experimental method for the investigation of antigens would be based on peptide libraries, but their huge combinatorial nature, size, cost, and the difficulty of experimental fine tuning makes this approach complicated time consuming, and costly. We have implemented in silico methodology with the aim of moving from CDR3α/β sequences to a library of potentially antigenic peptides that can be used in immunologically oriented experiments to study T cells' reactivity. To reduce the size of the library, we have verified the reproducibility of experimental benchmarks using the permutation of only six residues that can be considered representative of all ensembles of 20 natural amino acids. Such a simplified alphabet is able to correctly find the poses and chemical nature of original antigens within a small subset of ligands of potential interest. The newly generated library would have the advantage of leading to potentially antigenic ligands that would contribute to a better understanding of the chemical nature of TCR-antigen interactions. This step is crucial in the design of immunomodulators targeted towards T-cells response as well as in understanding the first principles of an immune response in several diseases, from cancer to autoimmune disorders.
Copyright © 2021 Iannuzzi, Rossetti, Spitaleri, Bonnal, Pagani and Mollica.

Entities:  

Keywords:  T-cell receptor (TCR); antigen recognition; ligand rational design; molecular mechanisms of adaptive immunity; receptor-peptide interaction

Year:  2021        PMID: 33718327      PMCID: PMC7947793          DOI: 10.3389/fchem.2021.598802

Source DB:  PubMed          Journal:  Front Chem        ISSN: 2296-2646            Impact factor:   5.221


  43 in total

1.  Amino acid alphabet reduction preserves fold information contained in contact interactions in proteins.

Authors:  Armando D Solis
Journal:  Proteins       Date:  2015-12

Review 2.  Regulatory T cells and immune tolerance.

Authors:  Shimon Sakaguchi; Tomoyuki Yamaguchi; Takashi Nomura; Masahiro Ono
Journal:  Cell       Date:  2008-05-30       Impact factor: 41.582

3.  Docking, scoring, and affinity prediction in CAPRI.

Authors:  Marc F Lensink; Shoshana J Wodak
Journal:  Proteins       Date:  2013-10-17

Review 4.  Regulatory T cells: mechanisms of differentiation and function.

Authors:  Steven Z Josefowicz; Li-Fan Lu; Alexander Y Rudensky
Journal:  Annu Rev Immunol       Date:  2012-01-06       Impact factor: 28.527

5.  Deconstructing the peptide-MHC specificity of T cell recognition.

Authors:  Michael E Birnbaum; Juan L Mendoza; Dhruv K Sethi; Shen Dong; Jacob Glanville; Jessica Dobbins; Engin Ozkan; Mark M Davis; Kai W Wucherpfennig; K Christopher Garcia
Journal:  Cell       Date:  2014-05-22       Impact factor: 41.582

6.  A single autoimmune T cell receptor recognizes more than a million different peptides.

Authors:  Linda Wooldridge; Julia Ekeruche-Makinde; Hugo A van den Berg; Anna Skowera; John J Miles; Mai Ping Tan; Garry Dolton; Mathew Clement; Sian Llewellyn-Lacey; David A Price; Mark Peakman; Andrew K Sewell
Journal:  J Biol Chem       Date:  2011-11-18       Impact factor: 5.157

7.  Contacts-based prediction of binding affinity in protein-protein complexes.

Authors:  Anna Vangone; Alexandre Mjj Bonvin
Journal:  Elife       Date:  2015-07-20       Impact factor: 8.140

8.  DynaDom: structure-based prediction of T cell receptor inter-domain and T cell receptor-peptide-MHC (class I) association angles.

Authors:  Thomas Hoffmann; Antoine Marion; Iris Antes
Journal:  BMC Struct Biol       Date:  2017-02-02

Review 9.  Single Cell T Cell Receptor Sequencing: Techniques and Future Challenges.

Authors:  Marco De Simone; Grazisa Rossetti; Massimiliano Pagani
Journal:  Front Immunol       Date:  2018-07-18       Impact factor: 7.561

10.  TCRmodel: high resolution modeling of T cell receptors from sequence.

Authors:  Ragul Gowthaman; Brian G Pierce
Journal:  Nucleic Acids Res       Date:  2018-07-02       Impact factor: 16.971

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