A systems biology perspective on rational design of peptide vaccine against virus infections.

With the recent onset of influenza A (H1N1) pandemic, the need for improved vaccines against virus infections has become an international priority. Strategies for vaccine development have changed over time, from whole-virus to immunogenic proteins and further to antigenic viral peptides. Various algorithms and bioinformatics tools have been developed to predict immunogenic peptide regions in an antigenic protein sequence. Recent advances in next-generation sequencing technologies, as represented by real time DNA sequencing, provide increased throughput and yield of data on viral pathogens and host cells. This enables us to 'mine' the genomic sequence for putative vaccine candidates or targets, allowing a more rational approach to the peptide vaccine design. This review first describes current computational tools available for the rational design of peptide vaccines and then addresses recent attempts to define pathogenic peptides at '- omics' level. As there are interplay between antibody and T cells, as well as intersection between viruses and hosts, the vaccine-mediated immunity are orchestrated by multiple factors within an interaction network. Therefore, single viral peptide alone fails to provide optimal immunity. Systems biology offers a systems-level perspective of how the various arms of the immune response are integrated to give immune response, as well as how host and virus interact, thereby providing an integrated approach to select the most promising candidates for peptide vaccines development. We highlight in this article the system-level application of rational peptide vaccine design, which may be a general paradigm for future viral vaccine development.

[1]  D. Anderson,et al.  Identification of potential target genes for the neuron-restrictive silencer factor. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[2]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[3]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[4]  Gajendra P.S. Raghava,et al.  A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes , 2007, Journal of Biosciences.

[5]  Qing-Yu He,et al.  Serum biomarkers of hepatitis B virus infected liver inflammation: A proteomic study , 2003, Proteomics.

[6]  M. Kanehisa,et al.  Expert system for predicting protein localization sites in gram‐negative bacteria , 1991, Proteins.

[7]  S. Brunak,et al.  Predicting proteasomal cleavage sites: a comparison of available methods. , 2003, International immunology.

[8]  S. Gygi,et al.  Correlation between Protein and mRNA Abundance in Yeast , 1999, Molecular and Cellular Biology.

[9]  Anne S De Groot,et al.  Bioinformatics tools for identifying class I-restricted epitopes. , 2003, Methods.

[10]  Jean-Luc Pellequer,et al.  BEPITOPE: predicting the location of continuous epitopes and patterns in proteins , 2003, Journal of molecular recognition : JMR.

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

[12]  K. R. Woods,et al.  Prediction of protein antigenic determinants from amino acid sequences. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[13]  G. McFadden,et al.  Poxviruses and immune evasion. , 2003, Annual review of immunology.

[14]  Bastian R. Angermann,et al.  Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses , 2008, The Journal of experimental medicine.

[15]  Bjoern Peters,et al.  A roadmap for the immunomics of category A-C pathogens. , 2005, Immunity.

[16]  Chung-Lin Liao,et al.  Vaccinia Virus Proteome: Identification of Proteins in Vaccinia Virus Intracellular Mature Virion Particles , 2006, Journal of Virology.

[17]  H. Rammensee,et al.  SYFPEITHI: database for MHC ligands and peptide motifs , 1999, Immunogenetics.

[18]  O. Lund,et al.  The Immune Epitope Database and Analysis Resource: From Vision to Blueprint , 2005, PLoS biology.

[19]  Ulisses Braga-Neto,et al.  From Functional Genomics to Functional Immunomics: New Challenges, Old Problems, Big Rewards , 2006, PLoS Comput. Biol..

[20]  J. Schlom,et al.  A triad of costimulatory molecules synergize to amplify T-cell activation. , 1999, Cancer research.

[21]  Martin T. Swain,et al.  An automated approach to modelling class II MHC alleles and predicting peptide binding , 2001, Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001).

[22]  S. Brunak,et al.  Prediction of proteasome cleavage motifs by neural networks. , 2002, Protein engineering.

[23]  K. Hadeler,et al.  PAProC: a prediction algorithm for proteasomal cleavages available on the WWW , 2001, Immunogenetics.

[24]  J. Stone,et al.  HLA-restricted epitope identification and detection of functional T cell responses by using MHC-peptide and costimulatory microarrays. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Judy Lieberman,et al.  Mapping cross-clade HIV-1 vaccine epitopes using a bioinformatics approach. , 2003, Vaccine.

[26]  Nikos Kyrpides,et al.  The Genomes On Line Database (GOLD) v.2: a monitor of genome projects worldwide , 2005, Nucleic Acids Res..

[27]  Kun Yu,et al.  Methods for Prediction of Peptide Binding to MHC Molecules: A Comparative Study , 2002, Molecular medicine.

[28]  O. Lund,et al.  An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions , 2005, European journal of immunology.

[29]  Massimo Bernaschi,et al.  ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization , 2008, Briefings Bioinform..

[30]  A. D. De Groot,et al.  An interactive Web site providing major histocompatibility ligand predictions: application to HIV research. , 1997, AIDS research and human retroviruses.

[31]  Arne Elofsson,et al.  Prediction of MHC class I binding peptides, using SVMHC , 2002, BMC Bioinformatics.

[32]  M F del Guercio,et al.  Prominent roles of secondary anchor residues in peptide binding to HLA-A24 human class I molecules. , 1995, Journal of immunology.

[33]  Channa K. Hattotuwagama,et al.  AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data , 2005, Immunome research.

[34]  Ellis L. Reinherz,et al.  Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles , 2004, Immunogenetics.

[35]  H. Perreault,et al.  Mass Spectrometric Characterization of Proteins from the SARS Virus , 2003, Molecular & Cellular Proteomics.

[36]  Ferry Ossendorp,et al.  Efficient Identification of Novel Hla-A*0201–Presented Cytotoxic T Lymphocyte Epitopes in the Widely Expressed Tumor Antigen Prame by Proteasome-Mediated Digestion Analysis , 2001, The Journal of experimental medicine.

[37]  Rino Rappuoli,et al.  Identification of iron-activated and -repressed Fur-dependent genes by transcriptome analysis of Neisseria meningitidis group B , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[38]  R. Lal,et al.  Human T-lymphotropic virus type 1 peptides in chimeric and multivalent constructs with promiscuous T-cell epitopes enhance immunogenicity and overcome genetic restriction , 1995, Journal of virology.

[39]  N. Kato,et al.  Humoral immune response to hypervariable region 1 of the putative envelope glycoprotein (gp70) of hepatitis C virus , 1993, Journal of virology.

[40]  Morten Nielsen,et al.  NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11 , 2008, Nucleic Acids Res..

[41]  K. Wiesmüller,et al.  Efficacy of synthetic vaccines in the induction of cytotoxic T lymphocytes. Comparison of the costimulating support provided by helper T cells and lipoamino acid. , 1994, Journal of immunological methods.

[42]  Tin Wee Tan,et al.  Structural bioinformatics Prediction of HLA-DQ 3 . 2 b Ligands : evidence of multiple registers in class II binding peptides , 2006 .

[43]  D. Flower,et al.  Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201. , 2001, Journal of medicinal chemistry.

[44]  A. D. De Groot,et al.  Prediction of well-conserved HIV-1 ligands using a matrix-based algorithm, EpiMatrix. , 1998, Vaccine.

[45]  Chee Keong Kwoh,et al.  Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes , 2008, BMC Bioinformatics.

[46]  J. Altman,et al.  Human effector and memory CD8+ T cell responses to smallpox and yellow fever vaccines. , 2008, Immunity.

[47]  Alessandro Sette,et al.  Structural characteristics of an antigen required for its interaction with Ia and recognition by T cells , 1987, Nature.

[48]  Morten Nielsen,et al.  Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method , 2007, BMC Bioinformatics.

[49]  Vladimir Brusic,et al.  MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides , 2005, Nucleic Acids Res..

[50]  Abdul Salam Jarrah,et al.  BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm044 Systems biology Simulating Epstein-Barr virus infection with C-ImmSim , 2022 .

[51]  H. Bui,et al.  Structural prediction of peptides binding to MHC class I molecules , 2006, Proteins.

[52]  K. Parker,et al.  Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. , 1994, Journal of immunology.

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

[54]  Jaideep P. Sundaram,et al.  Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial "pan-genome". , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[55]  Kay Hofmann,et al.  Tmbase-A database of membrane spanning protein segments , 1993 .

[56]  C. Fraser,et al.  Application of microbial genomic science to advanced therapeutics. , 2005, Annual review of medicine.

[57]  Ellis L. Reinherz,et al.  PEPVAC: a web server for multi-epitope vaccine development based on the prediction of supertypic MHC ligands , 2005, Nucleic Acids Res..

[58]  H. Robinson,et al.  T cell vaccines for microbial infections , 2005, Nature Medicine.

[59]  D. Zaller,et al.  Prediction of peptide affinity to HLA DRB1*0401. , 1994, International archives of allergy and immunology.

[60]  Christian von Mering,et al.  STRING 8—a global view on proteins and their functional interactions in 630 organisms , 2008, Nucleic Acids Res..

[61]  G. Dougan,et al.  The Key Role of Genomics in Modern Vaccine and Drug Design for Emerging Infectious Diseases , 2009, PLoS genetics.

[62]  O. Schueler‐Furman,et al.  Structure‐based prediction of binding peptides to MHC class I molecules: Application to a broad range of MHC alleles , 2000, Protein science : a publication of the Protein Society.

[63]  Z. Nagy,et al.  Precise prediction of major histocompatibility complex class II-peptide interaction based on peptide side chain scanning , 1994, The Journal of experimental medicine.

[64]  Morten Nielsen,et al.  Improved method for predicting linear B-cell epitopes , 2006, Immunome research.

[65]  J. Radolf,et al.  Decorin-Binding Protein A (DbpA) of Borrelia burgdorferi Is Not Protective When Immunized Mice Are Challenged via Tick Infestation and Correlates with the Lack of DbpA Expression by B. burgdorferi in Ticks , 2000, Infection and Immunity.

[66]  Peter D. Karp,et al.  MetaCyc: a multiorganism database of metabolic pathways and enzymes. , 2004, Nucleic acids research.

[67]  Victor Ciesielski,et al.  Application of Genetic Search in Derivation of Matrix Models of Peptide Binding to MHC Molecules , 1997, ISMB.

[68]  Hans-Georg Rammensee,et al.  MHC ligands and peptide motifs: first listing , 2004, Immunogenetics.

[69]  Gajendra P. S. Raghava,et al.  ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST , 2004, Nucleic Acids Res..

[70]  I. Bozic,et al.  Prediction of supertype-specific HLA class I binding peptides using support vector machines. , 2007, Journal of immunological methods.

[71]  S. Turner,et al.  Real-Time DNA Sequencing from Single Polymerase Molecules , 2009, Science.

[72]  J. Boyce,et al.  Analysis of the Pasteurella multocida outer membrane sub‐proteome and its response to the in vivo environment of the natural host , 2006, Proteomics.

[73]  Roded Sharan,et al.  BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btm493 Structural bioinformatics Pepitope: epitope mapping from affinity-selected peptides , 2022 .

[74]  Vladimir Brusic,et al.  Large‐scale computational identification of HIV T‐cell epitopes , 2002, Immunology and cell biology.

[75]  S. Bron,et al.  Signal Peptide-Dependent Protein Transport inBacillus subtilis: a Genome-Based Survey of the Secretome , 2000, Microbiology and Molecular Biology Reviews.

[76]  Xinxia Peng,et al.  Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers , 2009, BMC Genomics.

[77]  Pingping Guan,et al.  MHCPred: a server for quantitative prediction of peptide-MHC binding , 2003, Nucleic Acids Res..

[78]  Steven C. Lawlor,et al.  GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways , 2002, Nature Genetics.

[79]  O. Lund,et al.  Prediction of residues in discontinuous B‐cell epitopes using protein 3D structures , 2006, Protein science : a publication of the Protein Society.

[80]  S. Wold,et al.  Peptide quantitative structure-activity relationships, a multivariate approach. , 1987, Journal of medicinal chemistry.

[81]  Gajendra P. S. Raghava,et al.  Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences , 2005, Nucleic Acids Res..

[82]  F. Zuckermann,et al.  Use of interleukin 12 to enhance the cellular immune response of swine to an inactivated herpesvirus vaccine. , 1999, Advances in veterinary medicine.

[83]  Andrew C. R. Martin,et al.  SACS-Self-maintaining database of antibody crystal structure information , 2002, Bioinform..

[84]  Mark M Davis,et al.  Detection and Characterizationof Cellular Immune Responses Using Peptide–MHC Microarrays , 2003, PLoS biology.

[85]  R. Zagursky,et al.  Bioinformatics: use in bacterial vaccine discovery. , 2001, BioTechniques.

[86]  Itay Mayrose,et al.  Epitopia: a web-server for predicting B-cell epitopes , 2009, BMC Bioinformatics.

[87]  Vladimir Brusic,et al.  Computational methods for prediction of T-cell epitopes--a framework for modelling, testing, and applications. , 2004, Methods.

[88]  William Martin,et al.  Identification of immunogenic HLA-B7 "Achilles' heel" epitopes within highly conserved regions of HIV. , 2008, Vaccine.

[89]  Urmila Kulkarni-Kale,et al.  CEP: a conformational epitope prediction server , 2005, Nucleic Acids Res..

[90]  Bjoern Peters,et al.  Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications , 2005, Immunogenetics.

[91]  Gajendra P. S. Raghava,et al.  BcePred: Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-chemical Properties , 2004, ICARIS.

[92]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[93]  Jian Huang,et al.  CED: a conformational epitope database , 2006, BMC Immunology.

[94]  Matthias Niedrig,et al.  Development of viremia and humoral and cellular parameters of immune activation after vaccination with yellow fever virus strain 17D: A model of human flavivirus infection , 1998, Journal of medical virology.

[95]  G. Jung,et al.  Comparison of proteome between hepatitis B virus- and hepatitis C virus-associated hepatocellular carcinoma. , 2003, Clinical cancer research : an official journal of the American Association for Cancer Research.

[96]  R. Zagursky,et al.  Application of genomics and proteomics for identification of bacterial gene products as potential vaccine candidates. , 2000, Vaccine.

[97]  Pierre Baldi,et al.  Profiling the humoral immune response to infection by using proteome microarrays: high-throughput vaccine and diagnostic antigen discovery. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[98]  Yiming Shao,et al.  Enhanced: The Need for a Global HIV Vaccine Enterprise , 2003, Science.

[99]  Gajendra P. S. Raghava,et al.  ProPred1: Prediction of Promiscuous MHC Class-I Binding Sites , 2003, Bioinform..

[100]  Jianli Dong,et al.  Emerging Pathogens: Challenges and Successes of Molecular Diagnostics , 2008, The Journal of Molecular Diagnostics.

[101]  Gajendra P. S. Raghava,et al.  ProPred: prediction of HLA-DR binding sites , 2001, Bioinform..

[102]  S Brunak,et al.  Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach. , 2003, Tissue antigens.

[103]  Feng Yang,et al.  Proteomic Analysis of the Major Envelope and Nucleocapsid Proteins of White Spot Syndrome Virus , 2006, Journal of Virology.

[104]  Sudipto Saha,et al.  Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network , 2006, Proteins.

[105]  Michael A. Gonzalez,et al.  From genome to vaccine: in silico predictions, ex vivo verification. , 2001, Vaccine.