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

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.

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

FIGURE 1

Flowchart of the method implementation. From left to right (as described in the text in the Methodological sketch section): TCR modelling and peptide building (based on PeptideBuilder) (Tien et al., 2013); rigid body docking of the TCR and peptides (based on HADDOCK) (Dominguez et al., 2003); statistical analysis and peptide list generation. The scheme implementation is commented in the Supplementary Material.

FIGURE 2

(A) Schematic representation of the amino acids sequential nomenclature along the sequence of the antigen; a global 3D geometrical view of the interaction surface of TCR chains α (green) and β (red) with antigens is reported alongside its nomenclature; (B) simplified chemical alphabet of amino acids (SCAA) adopted in the present work, with the derivation from 20 amino acids scheme to the 6-letters alphabet (in red) adopted in the present work according to the clustering principles presented in (Murphy et al., 2000); (C) ambiguous restraints used for docking calculation of the antigen pose on the surface of T-cell receptor (TCR); the distances are expressed in Angstrom, the Cα and Cβ atoms are highlighted as spheres and CDR3 α and β chains are indicated. The molecular graphics has been realized using Visual Molecular Dynamics 1.9.3 (Humphrey et al., 1996).

FIGURE 3

(A) Energy vs contact maps relative to the benchmarks 2ian, 3mbe and 1zgl. The other benchmarks used in the article are reported in Supplementary Figure S2. Every black dot represents the average of 100 poses for a single peptide; the blue dots correspond to values of energies, E_i, and contacts, C_i, that fall within intervals of ±σ centered with respect to average values of the plotted property; the red cross corresponds to the original triad of amino acids in positions P3, P5 and P8; the yellow star corresponds to the translated triad of amino acids in positions P3, P5, P8; (B) Energy vs contact maps relative to the benchmarks 2ian, 3mbe and 1zgl: every dot is colored according to the RMSD with respect to the original experimental position. The other benchmarks used in the article are reported in Supplementary Figure S3; (C–E); E_i, C_i and RMSD distribution of the peptides for 2ian, 3mbe and 1zgl benchmarks. The other benchmarks used in the article are reported in Supplementary Figures S4–S6.

FIGURE 4

Energy residual content heatmaps for the benchmarks 2ian, 3mbe and 1zgl with respect to the SCAA class (vertical axis) and the three positions along the antigen sequence P3, P5 and P8 (horizontal axis). The other benchmarks used in the article are reported in Supplementary Figure S7.