Genome-wide structural modelling of TCR-pMHC interactions

BackgroundThe adaptive immune response is antigen-specific and triggered by pathogen recognition through T cells. Although the interactions and mechanisms of TCR-peptide-MHC (TCR-pMHC) have been studied over three decades, the biological basis for these processes remains controversial. As an increasing number of high-throughput binding epitopes and available TCR-pMHC complex structures, a fast genome-wide structural modelling of TCR-pMHC interactions is an emergent task for understanding immune interactions and developing peptide vaccines.ResultsWe first constructed the PPI matrices and iMatrix, using 621 non-redundant PPI interfaces and 398 non-redundant antigen-antibody interfaces, respectively, for modelling the MHC-peptide and TCR-peptide interfaces, respectively. The iMatrix consists of four knowledge-based scoring matrices to evaluate the hydrogen bonds and van der Waals forces between sidechains or backbones, respectively. The predicted energies of iMatrix are high correlated (Pearson's correlation coefficient is 0.6) to 70 experimental free energies on antigen-antibody interfaces. To further investigate iMatrix and PPI matrices, we inferred the 701,897 potential peptide antigens with significant statistic from 389 pathogen genomes and modelled the TCR-pMHC interactions using available TCR-pMHC complex structures. These identified peptide antigens keep hydrogen-bond energies and consensus interactions and our TCR-pMHC models can provide detailed interacting models and crucial binding regions.ConclusionsExperimental results demonstrate that our method can achieve high precision for predicting binding affinity and potential peptide antigens. We believe that iMatrix and our template-based method can be useful for the binding mechanisms of TCR-pMHC complexes and peptide vaccine designs.

[1]  M. Lefranc IMGT, the International ImMunoGeneTics Information System. , 2011, Cold Spring Harbor protocols.

[2]  Jinn-Moon Yang,et al.  PAComplex: a web server to infer peptide antigen families and binding models from TCR–pMHC complexes , 2011, Nucleic Acids Res..

[3]  R. Russell,et al.  Protein complexes: structure prediction challenges for the 21st century. , 2005, Current opinion in structural biology.

[4]  A. Singer,et al.  αβ T cell receptors that do not undergo major histocompatibility complex-specific thymic selection possess antibody-like recognition specificities. , 2012, Immunity.

[5]  D. Wiley,et al.  Two human T cell receptors bind in a similar diagonal mode to the HLA-A2/Tax peptide complex using different TCR amino acids. , 1998, Immunity.

[6]  María Martín,et al.  The Universal Protein Resource (UniProt) in 2010 , 2010 .

[7]  Alessandro Sette,et al.  The Immune Epitope Database 2.0 , 2009, Nucleic Acids Res..

[8]  Kurt S. Thorn,et al.  ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions , 2001, Bioinform..

[9]  Gajendra P. S. Raghava,et al.  MHCBN: a comprehensive database of MHC binding and non-binding peptides , 2003, Bioinform..

[10]  Vladimir Brusic,et al.  MHCPEP, a database of MHC-binding peptides: update 1996 , 1997, Nucleic Acids Res..

[11]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[12]  K. Jeang,et al.  Molecular mechanisms of cellular transformation by HTLV-1 Tax , 2005, Oncogene.

[13]  F. Kashanchi,et al.  Transcriptional and post-transcriptional gene regulation of HTLV-1 , 2005, Oncogene.

[14]  Patrick Aloy,et al.  Interrogating protein interaction networks through structural biology , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Jinn-Moon Yang,et al.  3D-interologs: an evolution database of physical protein- protein interactions across multiple genomes , 2010, BMC Genomics.

[16]  Stephen F. Altschul,et al.  The construction of amino acid substitution matrices for the comparison of proteins with non-standard compositions , 2005, Bioinform..

[17]  Rhonald C. Lua PyKnot: a PyMOL tool for the discovery and analysis of knots in proteins , 2012, Bioinform..

[18]  R. Grassmann,et al.  Interleukin-13 Overexpression by Tax Transactivation: a Potential Autocrine Stimulus in Human T-Cell Leukemia Virus-Infected Lymphocytes , 2004, Journal of Virology.

[19]  Hui Lu,et al.  MULTIPROSPECTOR: An algorithm for the prediction of protein–protein interactions by multimeric threading , 2002, Proteins.

[20]  P. Kloetzel,et al.  MAPPP: MHC class I antigenic peptide processing prediction. , 2003, Applied bioinformatics.

[21]  Vladimir Brusic,et al.  MHCPEP, a database of MHC-binding peptides: update 1996 , 1997, Nucleic Acids Res..

[22]  Ian A Wilson,et al.  The specificity of TCR/pMHC interaction. , 2002, Current opinion in immunology.

[23]  Yao-Tseng Chen,et al.  A testicular antigen aberrantly expressed in human cancers detected by autologous antibody screening. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Mark Johnson,et al.  NCBI BLAST: a better web interface , 2008, Nucleic Acids Res..

[25]  S. Henikoff,et al.  Amino acid substitution matrices from protein blocks. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Philippa Marrack,et al.  Evolutionarily conserved amino acids that control TCR-MHC interaction. , 2008, Annual review of immunology.

[27]  Thomas Lengauer,et al.  Predicting MHC class I epitopes in large datasets , 2010, BMC Bioinformatics.

[28]  Andrew Sewell,et al.  Structural and kinetic basis for heightened immunogenicity of T cell vaccines , 2005, The Journal of experimental medicine.

[29]  L. K. Ely,et al.  The molecular basis of TCR germline bias for MHC is surprisingly simple , 2009, Nature Immunology.

[30]  G. Crooks,et al.  WebLogo: a sequence logo generator. , 2004, Genome research.

[31]  Ian A Wilson,et al.  Structural and thermodynamic correlates of T cell signaling. , 2002, Annual review of biophysics and biomolecular structure.

[32]  Ruth Nussinov,et al.  A method for simultaneous alignment of multiple protein structures , 2004, Proteins.

[33]  R. J. Cohen,et al.  Promiscuous binding of extracellular peptides to cell surface class I MHC protein , 2012, Proceedings of the National Academy of Sciences.

[34]  Patrick Aloy,et al.  Ten thousand interactions for the molecular biologist , 2004, Nature Biotechnology.

[35]  Baris E. Suzek,et al.  The Universal Protein Resource (UniProt) in 2010 , 2009, Nucleic Acids Res..

[36]  A Sette,et al.  A structure-based algorithm to predict potential binding peptides to MHC molecules with hydrophobic binding pockets. , 1997, Human immunology.

[37]  S. Altschul,et al.  The compositional adjustment of amino acid substitution matrices , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Zlatko Trajanoski,et al.  Histopathologic-based prognostic factors of colorectal cancers are associated with the state of the local immune reaction. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[39]  Jinn-Moon Yang,et al.  3D-partner: a web server to infer interacting partners and binding models , 2007, Nucleic Acids Res..

[40]  A. Bogan,et al.  Anatomy of hot spots in protein interfaces. , 1998, Journal of molecular biology.

[41]  Tim J. P. Hubbard,et al.  Data growth and its impact on the SCOP database: new developments , 2007, Nucleic Acids Res..

[42]  Ettore Appella,et al.  A correlation between TCR Valpha docking on MHC and CD8 dependence: implications for T cell selection. , 2003, Immunity.

[43]  Jinyan Li,et al.  Mining for the antibody-antigen interacting associations that predict the B cell epitopes , 2010, BMC Structural Biology.

[44]  Mathias M Schuler,et al.  SYFPEITHI: database for searching and T-cell epitope prediction. , 2007, Methods in molecular biology.

[45]  N. Ben-Tal,et al.  Residue frequencies and pairing preferences at protein–protein interfaces , 2001, Proteins.

[46]  G. Crooks,et al.  WebLogo: A sequence logo generator, Genome Research, , 2004 .

[47]  Alessandro Sette,et al.  Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method , 2005, BMC Bioinformatics.

[48]  Debasisa Mohanty,et al.  MODPROPEP: a program for knowledge-based modeling of protein–peptide complexes , 2007, Nucleic Acids Res..

[49]  P. Bork,et al.  Proteome survey reveals modularity of the yeast cell machinery , 2006, Nature.

[50]  Cathy H. Wu,et al.  The Universal Protein Resource (UniProt) , 2004, Nucleic Acids Res..

[51]  P. Bork,et al.  Structure-Based Assembly of Protein Complexes in Yeast , 2004, Science.