An introduction to epitope prediction methods and software

In this paper, current prediction methods and algorithms for both T‐ and B cell epitopes are reviewed, and a comprehensive summary of epitope prediction software and databases currently available online is also provided. This review can offer researchers in this field a sense of direction and insights for future work. However, our main purpose is to introduce clinical and basic biomedical researchers who are not familiar with these biological analysis tools and databases to these online resources and to provide guidance on how to use them effectively. Copyright © 2008 John Wiley & Sons, Ltd.

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

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

[3]  C. Chothia,et al.  The atomic structure of protein-protein recognition sites. , 1999, Journal of molecular biology.

[4]  L. T. Ten Eyck,et al.  Protein docking using continuum electrostatics and geometric fit. , 2001, Protein engineering.

[5]  Thomas Lengauer,et al.  DynaPred: A structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations , 2006, ISMB.

[6]  Gajendra P. S. Raghava,et al.  Detection of Orientation of MHC Class II Binding Peptides Using Bioinformatics Tools , 2002 .

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

[8]  John Sidney,et al.  A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach , 2008, PLoS Comput. Biol..

[9]  Ruth Nussinov,et al.  Taking geometry to its edge: Fast unbound rigid (and hinge‐bent) docking , 2003, Proteins.

[10]  Vladimir Brusic,et al.  Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms , 2007, BMC Bioinformatics.

[11]  K. Dyer,et al.  Eosinophil Cationic Protein and Eosinophil-derived Neurotoxin , 1995, Journal of Biological Chemistry.

[12]  K C Chou,et al.  An analysis of protein folding type prediction by seed-propagated sampling and jackknife test , 1995, Journal of protein chemistry.

[13]  Manoj Bhasin,et al.  Prediction of promiscuous and high-affinity mutated MHC binders. , 2003, Hybridoma and hybridomics.

[14]  Pingping Guan,et al.  MHCPred: bringing a quantitative dimension to the online prediction of MHC binding. , 2003, Applied bioinformatics.

[15]  V. Brusic,et al.  Evaluation of MHC class I peptide binding prediction servers: Applications for vaccine research , 2008, BMC Immunology.

[16]  Uthaman Gowthaman,et al.  In silico tools for predicting peptides binding to HLA-class II molecules: more confusion than conclusion. , 2008, Journal of proteome research.

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

[18]  E. Reinherz,et al.  Prediction of MHC class I binding peptides using profile motifs. , 2002, Human immunology.

[19]  Gajendra P. S. Raghava,et al.  Analysis and prediction of antibacterial peptides , 2007, BMC Bioinformatics.

[20]  John Hawkins,et al.  Detecting and sorting targeting peptides with neural networks and support vector machines. , 2006, Journal of bioinformatics and computational biology.

[21]  Gajendra P. S. Raghava,et al.  HaptenDB: a comprehensive database of haptens, carrier proteins and anti-hapten antibodies , 2006, Bioinform..

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

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

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

[25]  Irini A. Doytchinova,et al.  JenPep: a database of quantitative functional peptide data for immunology , 2002, Bioinform..

[26]  S. Jones,et al.  Principles of protein-protein interactions. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[27]  C. DeLisi,et al.  Computing the structure of bound peptides. Application to antigen recognition by class I major histocompatibility complex receptors. , 1993, Journal of molecular biology.

[28]  O. Lund,et al.  The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage , 2005, Immunogenetics.

[29]  Yang Dai,et al.  Prediction of MHC class II binding peptides based on an iterative learning model , 2005, Immunome research.

[30]  Y. Z. Chen,et al.  Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties. , 2007, Molecular immunology.

[31]  Andrew J. Martin,et al.  Antibody-antigen interactions: contact analysis and binding site topography. , 1996, Journal of molecular biology.

[32]  Pingping Guan,et al.  EpiJen: a server for multistep T cell epitope prediction , 2006, BMC Bioinformatics.

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

[34]  Jonathan W. Yewdell,et al.  Immune recognition of a human renal cancer antigen through post-translational protein splicing , 2004, Nature.

[35]  A. Casrouge,et al.  A Direct Estimate of the Human αβ T Cell Receptor Diversity , 1999 .

[36]  N. K. Jerne,et al.  The Generative Grammar of the Immune System , 1993, The EMBO journal.

[37]  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.

[38]  A. Giuliani,et al.  A computational approach identifies two regions of Hepatitis C Virus E1 protein as interacting domains involved in viral fusion process , 2009, BMC Structural Biology.

[39]  Anthony Kusalik,et al.  Strength in numbers: achieving greater accuracy in MHC-I binding prediction by combining the results from multiple prediction tools , 2007, Immunome research.

[40]  Avner Schlessinger,et al.  Epitome: database of structure-inferred antigenic epitopes , 2005, Nucleic Acids Res..

[41]  J. C. Almagro,et al.  Identification of differences in the specificity‐determining residues of antibodies that recognize antigens of different size: implications for the rational design of antibody repertoires , 2004, Journal of molecular recognition : JMR.

[42]  S Forbes,et al.  The MHC haplotype project: a resource for HLA-linked association studies. , 2002, Tissue antigens.

[43]  Darren R Flower,et al.  Proteomics in Vaccinology and Immunobiology: An Informatics Perspective of the Immunone , 2003, Journal of biomedicine & biotechnology.

[44]  Jun Zeng,et al.  Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach , 2001, J. Comput. Aided Mol. Des..

[45]  I. Lasters,et al.  Flexible docking of peptide ligands to proteins. , 2000, Methods in molecular biology.

[46]  Bjoern Peters,et al.  Identifying MHC Class I Epitopes by Predicting the TAP Transport Efficiency of Epitope Precursors , 2003, The Journal of Immunology.

[47]  Ruurd van der Zee,et al.  Prediction of sequential antigenic regions in proteins , 1985, FEBS letters.

[48]  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.

[49]  Tin Wee Tan,et al.  MPID-T , 2006 .

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

[51]  J. Bodmer,et al.  IMGT/HLA Database - a sequence database for the human major histocompatibility complex , 2000, Nucleic Acids Res..

[52]  Ruben Abagyan,et al.  PIER: Protein interface recognition for structural proteomics , 2007, Proteins.

[53]  Bing Zhao,et al.  A novel MHCp binding prediction model. , 2003, Human immunology.

[54]  Bing Zhao,et al.  MHC-Peptide binding prediction for epitope based vaccine design , 2007 .

[55]  P. Kloetzel,et al.  Modeling the MHC class I pathway by combining predictions of proteasomal cleavage,TAP transport and MHC class I binding , 2005, Cellular and Molecular Life Sciences CMLS.

[56]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[57]  Didier Rognan,et al.  Recovery of known T-cell epitopes by computational scanning of a viral genome , 2002, J. Comput. Aided Mol. Des..

[58]  E Westhof,et al.  Predicting location of continuous epitopes in proteins from their primary structures. , 1991, Methods in enzymology.

[59]  Tal Pupko,et al.  Structural Genomics , 2005 .

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

[61]  Anne S. De Groot,et al.  In silico predictions; in vivo veritas , 1999, Nature Biotechnology.

[62]  G. Snell,et al.  The Nobel Lectures in Immunology. Lecture for the Nobel Prize for Physiology or Medicine, 1980: Studies in histocompatibility. , 1992, Scandinavian journal of immunology.

[63]  Werner Braun,et al.  SDAP: database and computational tools for allergenic proteins , 2003, Nucleic Acids Res..

[64]  Hong Zhang,et al.  EPIMHC: a curated database of MHC-binding peptides for customized computational vaccinology , 2005, Bioinform..

[65]  Vladimir Brusic,et al.  Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network , 1998, Bioinform..

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

[67]  Oliver Kohlbacher,et al.  SVMHC: a server for prediction of MHC-binding peptides , 2006, Nucleic Acids Res..

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

[69]  Morten Nielsen,et al.  A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules , 2006, PLoS Comput. Biol..

[70]  Tin Wee Tan,et al.  Methods and protocols for prediction of immunogenic epitopes , 2006, Briefings Bioinform..

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

[72]  Pierre Baldi,et al.  PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure , 2008, Bioinform..

[73]  C. DeLisi,et al.  Free energy mapping of class I MHC molecules and structural determination of bound peptides , 1996, Protein science : a publication of the Protein Society.

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

[75]  Avner Schlessinger,et al.  Towards a consensus on datasets and evaluation metrics for developing B‐cell epitope prediction tools , 2007, Journal of molecular recognition : JMR.

[76]  Rick Reitmaier,et al.  Review of immunoinformatic approaches to in-silico B-cell epitope prediction , 2007 .

[77]  T. Hanai,et al.  Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. , 2002, Journal of bioscience and bioengineering.

[78]  M. Bhasin,et al.  Bcipep: A database of B-cell epitopes , 2005, BMC Genomics.

[79]  M Karplus,et al.  Modeling of the TCR-MHC-peptide complex. , 2000, Journal of molecular biology.

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

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

[82]  E. Huarte,et al.  Specific and general HLA-DR binding motifs: comparison of algorithms. , 2000, Human immunology.

[83]  J L Cornette,et al.  Prediction of immunodominant helper T cell antigenic sites from the primary sequence. , 1987, Journal of immunology.

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

[85]  R. R. Mallios,et al.  Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta- algorithm , 1999, Bioinform..

[86]  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.

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

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

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

[90]  A. Alix,et al.  Predictive estimation of protein linear epitopes by using the program PEOPLE. , 1999, Vaccine.

[91]  Karina Yusim,et al.  Immunoinformatics Comes of Age , 2006, PLoS Comput. Biol..

[92]  Shoba Ranganathan,et al.  Modeling the structure of bound peptide ligands to major histocompatibility complex , 2004, Protein science : a publication of the Protein Society.

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

[94]  Morten Nielsen,et al.  Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction , 2007, BMC Bioinformatics.

[95]  Chuan Yi Tang,et al.  A reinforced merging methodology for mapping unique peptide motifs in members of protein families , 2006, BMC Bioinformatics.

[96]  Alessandro Sette,et al.  Development of a DNA Vaccine Designed to Induce Cytotoxic T Lymphocyte Responses to Multiple Conserved Epitopes in HIV-1 1 , 2003, The Journal of Immunology.

[97]  J. Hammer,et al.  Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE. , 2004, Methods.

[98]  Søren Brunak,et al.  Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach , 2004, Bioinform..

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

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

[101]  J. Yewdell,et al.  Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses. , 1999, Annual review of immunology.

[102]  V. Reyes,et al.  Hydrophobic strip-of-helix algorithm for selection of T cell-presented peptides. , 1987, Molecular immunology.

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

[104]  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.

[105]  P. Tongaonkar,et al.  A semi‐empirical method for prediction of antigenic determinants on protein antigens , 1990, FEBS letters.

[106]  Julia G. Bodmer,et al.  IMGT/HLA Database--a sequence database for the human major histocompatibility complex. , 2001, Nucleic acids research.

[107]  P. Dönnes,et al.  Integrated modeling of the major events in the MHC class I antigen processing pathway , 2005, Protein science : a publication of the Protein Society.

[108]  U. Şahin,et al.  Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices , 1999, Nature Biotechnology.

[109]  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).

[110]  Arno Lukas,et al.  Analysis and prediction of protective continuous B-cell epitopes on pathogen proteins , 2008, Immunome research.

[111]  D. Rognan,et al.  Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. , 1999, Journal of medicinal chemistry.

[112]  Limsoon Wong,et al.  FIMM, a database of functional molecular immunology , 2000, Nucleic Acids Res..

[113]  Anne S. De Groot,et al.  Immunomics: discovering new targets for vaccines and therapeutics , 2006 .

[114]  O. Lund,et al.  novel sequence representations Reliable prediction of T-cell epitopes using neural networks with , 2003 .

[115]  P. Kloetzel,et al.  A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20 S proteasome. , 1999, Journal of molecular biology.

[116]  R. Raz,et al.  ProMate: a structure based prediction program to identify the location of protein-protein binding sites. , 2004, Journal of molecular biology.

[117]  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..

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

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

[120]  S. Jones,et al.  Prediction of protein-protein interaction sites using patch analysis. , 1997, Journal of molecular biology.

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

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

[123]  J A Koziol,et al.  Prediction of binding to MHC class I molecules. , 1995, Journal of immunological methods.

[124]  David R. Westhead,et al.  Improved prediction of protein-protein binding sites using a support vector machines approach. , 2005, Bioinformatics.

[125]  John B. Anderson,et al.  MMDB: Entrez's 3D-structure database , 2002, Nucleic Acids Res..

[126]  John Sidney,et al.  Examining the independent binding assumption for binding of peptide epitopes to MHC-I molecules , 2003, Bioinform..

[127]  Tun-Wen Pai,et al.  Unique Peptide Identification of Rnasea Superfamily Sequences Based on Reinforced Merging Algorithms , 2006, J. Bioinform. Comput. Biol..

[128]  L C Harrison,et al.  MHCPEP: a database of MHC-binding peptides. , 1994, Nucleic acids research.

[129]  R. Hodges,et al.  New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. , 1986, Biochemistry.

[130]  Qing Zhang,et al.  Immune epitope database analysis resource (IEDB-AR) , 2008, Nucleic Acids Res..

[131]  H. Margalit,et al.  Ranking potential binding peptides to MHC molecules by a computational threading approach. , 1995, Journal of molecular biology.

[132]  Manoj Bhasin,et al.  Analysis and prediction of affinity of TAP binding peptides using cascade SVM , 2004, Protein science : a publication of the Protein Society.

[133]  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.

[134]  W. Taylor,et al.  A sequence pattern common to T cell epitopes. , 1988, The EMBO journal.

[135]  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.

[136]  Channa K. Hattotuwagama,et al.  Quantitative online prediction of peptide binding to the major histocompatibility complex. , 2004, Journal of molecular graphics & modelling.

[137]  A S Kolaskar,et al.  Prediction of three-dimensional structure and mapping of conformational epitopes of envelope glycoprotein of Japanese encephalitis virus. , 1999, Virology.

[138]  Gajendra P.S. Raghava,et al.  Prediction of CTL epitopes using QM, SVM and ANN techniques. , 2004, Vaccine.

[139]  Sandor Vajda,et al.  ClusPro: an automated docking and discrimination method for the prediction of protein complexes , 2004, Bioinform..

[140]  Patrice Duroux,et al.  IMGT/LIGM-DB, the IMGT® comprehensive database of immunoglobulin and T cell receptor nucleotide sequences , 2005, Nucleic Acids Res..