TCRep 3D: An Automated In Silico Approach to Study the Structural Properties of TCR Repertoires

Abstract

TCRep 3D is an automated systematic approach for TCR-peptide-MHC class I structure prediction, based on homology and ab initio modeling. It has been considerably generalized from former studies to be applicable to large repertoires of TCR. First, the location of the complementary determining regions of the target sequences are automatically identified by a sequence alignment strategy against a database of TCR Vα and Vβ chains. A structure-based alignment ensures automated identification of CDR3 loops. The CDR are then modeled in the environment of the complex, in an ab initio approach based on a simulated annealing protocol. During this step, dihedral restraints are applied to drive the CDR1 and CDR2 loops towards their canonical conformations, described by Al-Lazikani et. al. We developed a new automated algorithm that determines additional restraints to iteratively converge towards TCR conformations making frequent hydrogen bonds with the pMHC. We demonstrated that our approach outperforms popular scoring methods (Anolea, Dope and Modeller) in predicting relevant CDR conformations. Finally, this modeling approach has been successfully applied to experimentally determined sequences of TCR that recognize the NY-ESO-1 cancer testis antigen. This analysis revealed a mechanism of selection of TCR through the presence of a single conserved amino acid in all CDR3β sequences. The important structural modifications predicted in silico and the associated dramatic loss of experimental binding affinity upon mutation of this amino acid show the good correspondence between the predicted structures and their biological activities. To our knowledge, this is the first systematic approach that was developed for large TCR repertoire structural modeling.

High-Throughput Analysis of the T Cell Receptor Beta Chain Repertoire in PBMCs from Chronic Hepatitis B Patients with HBeAg Seroconversion

“…Molecular structural analysis has revealed that CDR3 mainly recognizes MHC molecules bound to antigenic peptides [20, 21]. Hence, analysis of CDR3 can reveal changes in antigen-stimulated T cells [22, 23], the number of T cell clones, and T cell functioning [24, 25]. …”

Profiling the repertoire of T-cell receptor beta-chain variable genes in peripheral blood lymphocytes from subjects who have recovered from acute hepatitis B virus infection

“…19,20 Therefore, the analysis of a CDR3 profile can reveal changes in the T-cell population stimulated by a specific antigen, 21,22 the amount of a particular T-cell clone and the functional status of T cells. 23,24 T lymphocytes are primarily divided into the CD4 1 and CD8 1 subsets. In peripheral blood from HDs, the TCRs of more than 95% of T cells are composed of alpha and beta chain heterodimers.…”

T-Cell Receptors Binding Orientation over Peptide/MHC Class I Is Driven by Long-Range Interactions

“…500 models of the A6 TCR [23] were built using the homology module of the TCRep 3D approach, as described elsewhere [7]. TCRep 3D makes use of state of the art homology modeling using the Modeller 9v5 software [24], complemented by the use of additional dihedral restraints applied on CDR 1 and CDR2 to drive the loops towards their canonical conformations [25].…”

“…Understanding the molecular basis that governs TCR orientation over pMHC is still a considerable challenge, and also an important need in the field of TCRpMHC modeling [7] and, as a direct consequence, in the field of rational TCR design and adoptive cell transfer immunotherapy [8]. This questions has been recurrently discussed, but only a few studies have focused on predicting the actual binding mode of given TCRpMHC structures: the study from Varani et al made use of experimental data obtained from NMR chemical shift mapping to obtain lists of buried residues upon binding [9], while the recent study from Roomp and Domingues pedicted the contacts between the pMHC and the TCR, using a training set of TCRpMHC crystal structures [4].…”

“…Our scoring scheme, based on the CHARMM force field with the FACTS implicit solvation model [10], allowed the decomposition of the effective energy into residue contributions, and the study of the importance of the different CDR, the MHC and the peptide towards defining the overall TCR orientation. Ultimately, we briefly present and discuss an attempt to predict the TCR/pMHC binding mode using a TCR 3D structure obtained by homology modeling, to asses the efficacy of the approach as a component of a TCRpMHC structural modeling pipeline [7].…”