TRain: T-cell receptor automated immunoinformatics

Abstract

Background: The scarcity of available structural data makes characterizing the binding of T-cell receptors (TCRs) to peptide-Major Histocompatibility Complexes (pMHCs) very challenging. The recent surge in sequencing data makes TCRs an ideal target for protein structure modeling. Through these 3D models, researchers can potentially identify key motifs on the TCR's binding regions. Furthermore, computational methods can be employed to pair a TCR structure with a pMHC, leading to predictions of docked TCRpMHC structures. However, going from sequence to predicted 3D TCRpMHC complexes requires a non-trivial amount of steps and specialized immunoinformatics expertise.

Results: We developed a Python tool named TRain (T-cell Receptor Automated ImmunoiNformatics) to streamline this process by: (1) converting single-cell sequencing data into full TCR amino acid sequences; (2) efficiently submitting TCR amino acid sequences to existing TCR-specific modeling pipelines; (3) pairing modeled TCR structures with existing crystal structures of pMHC complexes in a non-biased manner before docking; (3) automating the preparation and submission process of TCRs and pMHCs for docking using the RosettaDock tool; and (4) providing scripts to analyze the predicted TCRpMHC interface. We illustrate the basic functionality of TRain with a case study, while further information can be found in a dedicated manual.

Conclusions: We introduced an open-source tool that streamlines going from full TCR sequence information to predicted 3D TCRpMHC complexes, using well-established tools. Analyzing these predicted complexes can provide deeper insights into the binding properties of TCRs, and can help shed light on one of the key steps in adaptive immune responses.

Keywords: Immunoinformatics; TCR modeling; protein docking.

Fig. 1

Structure of the TCRpMHC complex Panel A highlights the TCR and antigen interaction between a TCR and an pMHC, inserted in their respective membranes. Panel B displays an exploded view of the TCRpMHC complex. The chains displayed are TCR Alpha (orange), TCR Beta (purple), MHC (blue), Beta-2 Microglobulin (gray), and peptide (pink). Panel C is a rotated view of the binding interfaces of the TCR and the pMHC. The white labels mark the CDR 1–3 loops of the alpha and beta chains. The black labels mark the residues of the peptide; residues not labeled have side chains oriented inside the binding cleft of the MHC. Panel D is a representation of the TCRpMHC complex based on the positioning in the crystal structure 3GSN [7]

Fig. 2

TRain Pipeline Flowchart. There are five different components in the TRain pipeline, starting with the input step, where TCR chain segments are assembled into complete amino acid sequences for the TCR alpha and beta chains. This step yields two FASTA files as outputs, one for each chain. In the modeling step, these FASTA files are used to build a 3D structure of the TCR. This modeling is performed using the TCRmodel program, which is part of the Rosetta suite [11]. In the pairing step, the PDB files produced in the modeling step are used to pair the TCRs with pMHC structures derived from crystal structures, setting the stage for TCRpMHC docking. In the docking step, the PDBs from the pairing step are docked using the RosettaDock protocol (version 4.0 [12]). The final analysis step allows the user to explore the TCRpMHC interface

Fig. 3

Investigating biologically relevant binding poses with TRain. The sequence selected for ModelEngine.py modeling is obtained in the form of segments + CDR sequences, with the single AA difference from JM22 highlighted in white (A). This TCR is paired with the pMHC from crystal structures 1OGA and 3HG1 using TurnTable.py and docked in a series of five TCRcoupler.py runs. The RMSD values between the top scoring pose and the top 200 scoring poses (with the first point having a value of 0Å as it represents the RMSD between the top-scoring pose and itself) (B) and RMSD MSD clusters (C) show more consistent predicted binding poses for the biologically relevant TCR-1OGA pair, shown here for the first out of five total runs. Plots for the other four runs are available in the github repository). Using DataDepot.py, top binding pose structures for both TCR-pMHC pairs were assessed for the model quality metric ’Fnat’ along with interface RMSD (D), interface score (E), and total score (F)