Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Milighetti M

PROVIDER: S-EPMC8456106 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Milighetti Martina M Shawe-Taylor John J Chain Benny B

Frontiers in physiology 20210908

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 ...[more]

PMID: 34566692

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Project description:BACKGROUND:Identification of antigenic peptide epitopes is an essential prerequisite in T cell-based molecular vaccine design. Computational (sequence-based and structure-based) methods are inexpensive and efficient compared to experimental approaches in screening numerous peptides against their cognate MHC alleles. In structure-based protocols, suited to alleles with limited epitope data, the first step is to identify high-binding peptides using docking techniques, which need improvement in speed and efficiency to be useful in large-scale screening studies. We present pDOCK: a new computational technique for rapid and accurate docking of flexible peptides to MHC receptors and primarily apply it on a non-redundant dataset of 186 pMHC (MHC-I and MHC-II) complexes with X-ray crystal structures. RESULTS:We have compared our docked structures with experimental crystallographic structures for the immunologically relevant nonameric core of the bound peptide for MHC-I and MHC-II complexes. Primary testing for re-docking of peptides into their respective MHC grooves generated 159 out of 186 peptides with C? RMSD of less than 1.00 Å, with a mean of 0.56 Å. Amongst the 25 peptides used for single and variant template docking, the C? RMSD values were below 1.00 A for 23 peptides. Compared to our earlier docking methodology, pDOCK shows upto 2.5 fold improvement in the accuracy and is ~60% faster. Results of validation against previously published studies represent a seven-fold increase in pDOCK accuracy. CONCLUSIONS:The limitations of our previous methodology have been addressed in the new docking protocol making it a rapid and accurate method to evaluate pMHC binding. pDOCK is a generic method and although benchmarks against experimental structures, it can be applied to alleles with no structural data using sequence information. Our outcomes establish the efficacy of our procedure to predict highly accurate peptide structures permitting conformational sampling of the peptide in MHC binding groove. Our results also support the applicability of pDOCK for in silico identification of promiscuous peptide epitopes that are relevant to higher proportions of human population with greater propensity to activate T cells making them key targets for the design of vaccines and immunotherapies.

Dataset Information

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

Publications

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

Similar Datasets

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