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      Large Scale Characterization of the LC13 TCR and HLA-B8 Structural Landscape in Reaction to 172 Altered Peptide Ligands: A Molecular Dynamics Simulation Study

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          Abstract

          The interplay between T cell receptors (TCRs) and peptides bound by major histocompatibility complexes (MHCs) is one of the most important interactions in the adaptive immune system. Several previous studies have computationally investigated their structural dynamics. On the basis of these simulations several structural and dynamical properties have been proposed as effectors of the immunogenicity. Here we present the results of a large scale Molecular Dynamics simulation study consisting of 100 ns simulations of 172 different complexes. These complexes consisted of all possible point mutations of the Epstein Barr Virus peptide FLRGRAYGL bound by HLA-B*08:01 and presented to the LC13 TCR. We compare the results of these 172 structural simulations with experimental immunogenicity data. We found that simulations with more immunogenic peptides and those with less immunogenic peptides are in fact highly similar and on average only minor differences in the hydrogen binding footprints, interface distances, and the relative orientation between the TCR chains are present. Thus our large scale data analysis shows that many previously suggested dynamical and structural properties of the TCR/peptide/MHC interface are unlikely to be conserved causal factors for peptide immunogenicity.

          Author Summary

          Immune cells in the human body screen other cells for possible infections. The binding of T-cell receptors (TCR) and parts of pathogens bound by major histocompatibility complexes (MHC) is one of the activation mechanisms of the immune system. There have been many hypotheses as to when such binding will activate the immune system. In this study we performed the, to our knowledge, largest set of Molecular Dynamics simulations of TCR-MHC complexes. We performed 172 simulations each of 100 ns in length. By performing a large number of simulations we obtain insight about which structural features are frequently present in immune system activating and non-activating TCR-MHC complexes. We show that many previously suggested structural features are unlikely to be causal for the activation of the human immune system.

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          Most cited references49

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          SYFPEITHI: database for MHC ligands and peptide motifs.

          The first version of the major histocompatibility complex (MHC) databank SYFPEITHI: database for MHC ligands and peptide motifs, is now available to the general public. It contains a collection of MHC class I and class II ligands and peptide motifs of humans and other species, such as apes, cattle, chicken, and mouse, for example, and is continuously updated. All motifs currently available are accessible as individual entries. Searches for MHC alleles, MHC motifs, natural ligands, T-cell epitopes, source proteins/organisms and references are possible. Hyperlinks to the EMBL and PubMed databases are included. In addition, ligand predictions are available for a number of MHC allelic products. The database content is restricted to published data only.
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            How TCRs bind MHCs, peptides, and coreceptors.

            Since the first crystal structure determinations of alphabeta T cell receptors (TCRs) bound to class I MHC-peptide (pMHC) antigens in 1996, a sizable database of 24 class I and class II TCR/pMHC complexes has been accumulated that now defines a substantial degree of structural variability in TCR/pMHC recognition. Recent determination of free and bound gammadelta TCR structures has enabled comparisons of the modes of antigen recognition by alphabeta and gammadelta T cells and antibodies. Crystal structures of TCR accessory (CD4, CD8) and coreceptor molecules (CD3epsilondelta, CD3epsilongamma) have further advanced our structural understanding of most of the components that constitute the TCR signaling complex. Despite all these efforts, the structural basis for MHC restriction and signaling remains elusive as no structural features that define a common binding mode or signaling mechanism have yet been gleaned from the current set of TCR/pMHC complexes. Notwithstanding, the impressive array of self, foreign (microbial), and autoimmune TCR complexes have uncovered the diverse ways in which antigens can be specifically recognized by TCRs.
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              Further development and validation of empirical scoring functions for structure-based binding affinity prediction.

              New empirical scoring functions have been developed to estimate the binding affinity of a given protein-ligand complex with known three-dimensional structure. These scoring functions include terms accounting for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. A special feature is that three different algorithms have been implemented to calculate the hydrophobic effect term, which results in three parallel scoring functions. All three scoring functions are calibrated through multivariate regression analysis of a set of 200 protein-ligand complexes and they reproduce the binding free energies of the entire training set with standard deviations of 2.2 kcal/mol, 2.1 kcal/mol, and 2.0 kcal/mol, respectively. These three scoring functions are further combined into a consensus scoring function, X-CSCORE. When tested on an independent set of 30 protein-ligand complexes. X-CSCORE is able to predict their binding free energies with a standard deviation of 2.2 kcal/mol. The potential application of X-CSCORE to molecular docking is also investigated. Our results show that this consensus scoring function improves the docking accuracy considerably when compared to the conventional force field computation used for molecular docking.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                August 2014
                7 August 2014
                : 10
                : 8
                : e1003748
                Affiliations
                [1]Department of Statistics, Protein Informatics Group, University of Oxford, Oxford, United Kingdom
                Baltimore, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: BK. Performed the experiments: BK. Analyzed the data: BK. Contributed reagents/materials/analysis tools: CMD BK JD. Wrote the paper: BK. Revised the manuscript critically: CMD JD.

                Article
                PCOMPBIOL-D-14-00429
                10.1371/journal.pcbi.1003748
                4125040
                25101830
                8f4547dc-7dc6-49be-8ffe-e4eb9b143485
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 12 March 2014
                : 28 May 2014
                Page count
                Pages: 14
                Funding
                This work was supported by the 2020 Science Programme (EPSRC Cross-Discipline Interface Programme, EP/I017909/1). The authors would like to acknowledge the use of the Oxford Advanced Research Computing (ARC) facility carrying out this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Immunology
                Antigen Processing and Recognition
                Computer and Information Sciences
                Computerized Simulations
                Physical Sciences
                Chemistry
                Computational Chemistry
                Molecular Dynamics
                Custom metadata
                The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. The data used to create the figures in this manuscript are available at http://dx.doi.org/10.6084/m9.figshare.1048876. The full data will be made available at http://www.2020science.net/ within six months of publication, and are available on request from the authors ( bernhard.knapp@ 123456stats.ox.ac.uk ) prior to this due to their size.

                Quantitative & Systems biology
                Quantitative & Systems biology

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