Development and validation of an epitope prediction tool for swine (PigMatrix) based on the pocket profile method

BMC Bioinformatics

Anne S. De Groot³  Leonard Moise³  Frances Terry³  Chris Bailey-Kellogg¹  William D. Martin³  Andres H. Gutiérrez² 

[1]Department of Computer Science, Dartmouth College, Hanover 03755, NH, USA

[2]Institute for Immunology and Informatics, CMB/CELS, University of Rhode Island, Providence 02903, RI, USA

[3]EpiVax, Inc., Providence 02860, RI, USA

关键词: T cell epitope;    Genome-derived vaccine;    Influenza;    PRRSV;    Porcine;    Class II;    Class I;    MHC;    SLA;    HLA;    Epitope prediction;    Computational vaccinology;    EpiMatrix;    PigMatrix;   

Others  :  1229462
DOI  :  10.1186/s12859-015-0724-8

 received in 2015-06-29, accepted in 2015-08-26,  发布年份 2015

【 摘 要 】

Background

T cell epitope prediction tools and associated vaccine design algorithms have accelerated the development of vaccines for humans. Predictive tools for swine and other food animals are not as well developed, primarily because the data required to develop the tools are lacking. Here, we overcome a lack of T cell epitope data to construct swine epitope predictors by systematically leveraging available human information. Applying the “pocket profile method”, we use sequence and structural similarities in the binding pockets of human and swine major histocompatibility complex proteins to infer Swine Leukocyte Antigen (SLA) peptide binding preferences.

We developed epitope-prediction matrices (PigMatrices), for three SLA class I alleles (SLA-1*0401, 2*0401 and 3*0401) and one class II allele (SLA-DRB1*0201), based on the binding preferences of the best-matched Human Leukocyte Antigen (HLA) pocket for each SLA pocket. The contact residues involved in the binding pockets were defined for class I based on crystal structures of either SLA (SLA-specific contacts, Ssc) or HLA supertype alleles (HLA contacts, Hc); for class II, only Hc was possible. Different substitution matrices were evaluated (PAM and BLOSUM) for scoring pocket similarity and identifying the best human match. The accuracy of the PigMatrices was compared to available online swine epitope prediction tools such as PickPocket and NetMHCpan.

Results

PigMatrices that used Ssc to define the pocket sequences and PAM30 to score pocket similarity demonstrated the best predictive performance and were able to accurately separate binders from random peptides. For SLA-1*0401 and 2*0401, PigMatrix achieved area under the receiver operating characteristic curves (AUC) of 0.78 and 0.73, respectively, which were equivalent or better than PickPocket (0.76 and 0.54) and NetMHCpan version 2.4 (0.41 and 0.51) and version 2.8 (0.72 and 0.71). In addition, we developed the first predictive SLA class II matrix, obtaining an AUC of 0.73 for existing SLA-DRB1*0201 epitopes. Notably, PigMatrix achieved this level of predictive power without training on SLA binding data.

Conclusion

Overall, the pocket profile method combined with binding preferences from HLA binding data shows significant promise for developing T cell epitope prediction tools for pigs. When combined with existing vaccine design algorithms, PigMatrix will be useful for developing genome-derived vaccines for a range of pig pathogens for which no effective vaccines currently exist (e.g. porcine reproductive and respiratory syndrome, influenza and porcine epidemic diarrhea).

【 授权许可】

   
2015 Gutiérrez et al.

【 图 表 】

Fig. 1.

Figure 2.

Fig. 3.

Fig. 4.

Fig. 5.

【 参考文献 】

• [1]Neefjes J, Jongsma MLM, Paul P, Bakke O. Towards a systems understanding of MHC class I and MHC class II antigen presentation. Nat Rev Immunol. 2011; 11:823-36.
• [2]Zhang N, Qi J, Feng S, Gao F, Liu J, Pan X, Chen R, Li Q, Chen Z, Li X, Xia C, Gao GF. Crystal structure of swine major histocompatibility complex class I SLA-1 0401 and identification of 2009 pandemic swine-origin influenza A H1N1 virus cytotoxic T lymphocyte epitope peptides. J Virol. 2011; 85:11709-24.
• [3]Wee LJK, Lim SJ, Ng LFP, Tong JC. Immunoinformatics: how in silico methods are re-shaping the investigation of peptide immune specificity. Front Biosci Elite Ed. 2012; 4:311-9.
• [4]Zhang Q, Wang P, Kim Y, Haste-Andersen P, Beaver J, Bourne PE, Bui H-H, Buus S, Frankild S, Greenbaum J, Lund O, Lundegaard C, Nielsen M, Ponomarenko J, Sette A, Zhu Z, Peters B. Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Res. 2008; 36(Web Server issue):W513-8.
• [5]Jojic N, Reyes-Gomez M, Heckerman D, Kadie C, Schueler-Furman O. Learning MHC I--peptide binding. Bioinforma Oxf Engl. 2006; 22:e227-35.
• [6]Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, Røder G, Peters B, Sette A, Lund O, Buus S. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE. 2007; 2: Article ID e796
• [7]Jacob L, Vert J-P. Efficient peptide-MHC-I binding prediction for alleles with few known binders. Bioinforma Oxf Engl. 2008; 24:358-66.
• [8]Bordner AJ, Mittelmann HD. MultiRTA: a simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes. BMC Bioinformatics. 2010; 11:482. BioMed Central Full Text
• [9]Zhang L, Chen Y, Wong H-S, Zhou S, Mamitsuka H, Zhu S. TEPITOPEpan: Extending TEPITOPE for Peptide Binding Prediction Covering over 700 HLA-DR Molecules. PLoS ONE. 2012; 7: Article ID e30483
• [10]Guo L, Luo C, Zhu S. MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction. BMC Genomics. 2013; 14 Suppl 5:S11. BioMed Central Full Text
• [11]Karosiene E, Rasmussen M, Blicher T, Lund O, Buus S, Nielsen M. NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ. Immunogenetics. 2013; 65:711-24.
• [12]Hoof I, Peters B, Sidney J, Pedersen LE, Sette A, Lund O, Buus S, Nielsen M. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics. 2009; 61:1-13.
• [13]Pedersen LE, Harndahl M, Rasmussen M, Lamberth K, Golde WT, Lund O, Nielsen M, Buus S. Porcine major histocompatibility complex (MHC) class I molecules and analysis of their peptide-binding specificities. Immunogenetics. 2011; 63:821-34.
• [14]Pedersen LE, Jungersen G, Buus S, Golde WT: Analysis of Swine Leukocyte Antigen peptide binding profiles and the identification of T cell epitopes by tetramer staining. Ph.D. thesis. Technical University of Denmark; 2012.
• [15]Pedersen LE, Harndahl M, Nielsen M, Patch JR, Jungersen G, Buus S, Golde WT. Identification of peptides from foot-and-mouth disease virus structural proteins bound by class I swine leukocyte antigen (SLA) alleles, SLA-1*0401 and SLA-2*0401. Anim Genet. 2013; 44:251-8.
• [16]Pedersen LE, Breum SO, Riber U, Larsen LE, Jungersen G. Identification of swine influenza virus epitopes and analysis of multiple specificities expressed by cytotoxic T cell subsets. Virol J. 2014; 11:163. BioMed Central Full Text
• [17]Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Braxenthaler M, Gallazzi F, Protti MP, Sinigaglia F, Hammer J. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol. 1999; 17:555-61.
• [18]Zhang H, Lund O, Nielsen M. The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Bioinformatics. 2009; 25:1293-9.
• [19]Chelvanayagam G. A roadmap for HLA-A, HLA-B, and HLA-C peptide binding specificities. Immunogenetics. 1996; 45:15-26.
• [20]Chelvanayagam G. A Roadmap for HLA-DR Peptide Binding Specificities. Hum Immunol. 1997; 58:61-9.
• [21]Rammensee H-G, Bachmann J, Stevanovic S. MHC Ligands and Peptide Motifs. Landes Bioscience, Georgetown, TX; 1997.
• [22]De Groot AS, Jesdale BM, Szu E, Schafer JR, Chicz RM, Deocampo G. An interactive Web site providing major histocompatibility ligand predictions: application to HIV research. AIDS Res Hum Retroviruses. 1997; 13:529-31.
• [23]McMurry JA, Kimball S, Lee JH, Rivera D, Martin W, Weiner DB, Kutzler M, Sherman DR, Kornfeld H, De Groot AS. Epitope-driven TB vaccine development: a streamlined approach using immuno-informatics, ELISpot assays, and HLA transgenic mice. Curr Mol Med. 2007; 7:351-68.
• [24]Gregory SH, Mott S, Phung J, Lee J, Moise L, McMurry JA, Martin W, De Groot AS. Epitope-based vaccination against pneumonic tularemia. Vaccine. 2009; 27:5299-306.
• [25]Moise L, Buller RM, Schriewer J, Lee J, Frey SE, Weiner DB, Martin W, De Groot AS. VennVax, a DNA-prime, peptide-boost multi-T-cell epitope poxvirus vaccine, induces protective immunity against vaccinia infection by T cell response alone. Vaccine. 2011; 29:501-11.
• [26]Moss SF, Moise L, Lee DS, Kim W, Zhang S, Lee J, Rogers AB, Martin W, De Groot AS. HelicoVax: epitope-based therapeutic Helicobacter pylori vaccination in a mouse model. Vaccine. 2011; 29:2085-91.
• [27]De Groot AS, Martin W. Reducing risk, improving outcomes: bioengineering less immunogenic protein therapeutics. Clin Immunol. 2009; 131:189-201.
• [28]De Groot AS, Jesdale B, Martin W, Saint Aubin C, Sbai H, Bosma A, Lieberman J, Skowron G, Mansourati F, Mayer KH. Mapping cross-clade HIV-1 vaccine epitopes using a bioinformatics approach. Vaccine. 2003; 21:4486-504.
• [29]Moise L, McMurry JA, Buus S, Frey S, Martin WD, De Groot AS. In silico-accelerated identification of conserved and immunogenic variola/vaccinia T-cell epitopes. Vaccine. 2009; 27:6471-9.
• [30]Mishra S, Losikoff PT, Self AA, Terry F, Ardito MT, Tassone R, Martin WD, De Groot AS, Gregory SH. Peptide-pulsed dendritic cells induce the hepatitis C viral epitope-specific responses of naïve human T cells. Vaccine. 2014; 32:3285-92.
• [31]De Groot AS, Nene V, Hegde NR, Srikumaran S, Rayner J, Martin W. T cell epitope identification for bovine vaccines: an epitope mapping method for BoLA A-11. Int J Parasitol. 2003; 33:641-53.
• [32]Smith DM, Lunney JK, Martens GW, Ando A, Lee J-H, Ho C-S, Schook L, Renard C, Chardon P. Nomenclature for factors of the SLA class-I system, 2004. Tissue Antigens. 2005; 65:136-49.
• [33]Smith DM, Lunney JK, Ho C-S, Martens GW, Ando A, Lee J-H, Schook L, Renard C, Chardon P. Nomenclature for factors of the swine leukocyte antigen class II system, 2005. Tissue Antigens. 2005; 66:623-39.
• [34]Ho C-S, Franzo-Romain MH, Lee YJ, Lee JH, Smith DM. Sequence-based characterization of swine leucocyte antigen alleles in commercially available porcine cell lines. Int J Immunogenet. 2009; 36:231-4.
• [35]Ho C-S, Lunney JK, Franzo-Romain MH, Martens GW, Lee Y-J, Lee J-H, Wysocki M, Rowland RRR, Smith DM. Molecular characterization of swine leucocyte antigen class I genes in outbred pig populations. Anim Genet. 2009; 40:468-78.
• [36]Ho C-S, Lunney JK, Lee J-H, Franzo-Romain MH, Martens GW, Rowland RRR, Smith DM. Molecular characterization of swine leucocyte antigen class II genes in outbred pig populations. Anim Genet. 2010; 41:428-32.
• [37]Ober BT, Summerfield A, Mattlinger C, Wiesmüller KH, Jung G, Pfaff E, Saalmüller A, Rziha HJ. Vaccine-induced, pseudorabies virus-specific, extrathymic CD4 + CD8+ memory T-helper cells in swine. J Virol. 1998; 72:4866-73.
• [38]Armengol E, Wiesmüller K-H, Wienhold D, Büttner M, Pfaff E, Jung G, Saalmüller A. Identification of T-cell epitopes in the structural and non-structural proteins of classical swine fever virus. J Gen Virol. 2002; 83:551-60.
• [39]Gerner W, Denyer MS, Takamatsu H-H, Wileman TE, Wiesmüller K-H, Pfaff E, Saalmüller A. Identification of novel foot-and-mouth disease virus specific T-cell epitopes in c/c and d/d haplotype miniature swine. Virus Res. 2006; 121:223-8.
• [40]Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 2003; 31:3784-8.
• [41]Sidney J, Peters B, Frahm N, Brander C, Sette A. HLA class I supertypes: a revised and updated classification. BMC Immunol. 2008; 9:1. BioMed Central Full Text
• [42]Southwood S, Sidney J, Kondo A, del Guercio MF, Appella E, Hoffman S, Kubo RT, Chesnut RW, Grey HM, Sette A. Several common HLA-DR types share largely overlapping peptide binding repertoires. J Immunol. 1998; 160:3363-73.
• [43]Stern LJ, Brown JH, Jardetzky TS, Gorga JC, Urban RG, Strominger JL, Wiley DC. Crystal structure of the human class II MHC protein HLA-DR1 complexed with an influenza virus peptide. Nature. 1994; 368:215-21.
• [44]Stern LJ, Calvo-Calle JM. HLA-DR: molecular insights and vaccine design. Curr Pharm Des. 2009; 15:3249-61.
• [45]Dayhoff MO, Schwartz RM. A model of evolutionary change in proteins. Atlas of protein sequence and structure. 1978.
• [46]Altschul SF. Amino acid substitution matrices from an information theoretic perspective. J Mol Biol. 1991; 219:555-65.
• [47]Madden DR. The three-dimensional structure of peptide-MHC complexes. Annu Rev Immunol. 1995; 13:587-622.
• [48]Blanco E, McCullough K, Summerfield A, Fiorini J, Andreu D, Chiva C, Borrás E, Barnett P, Sobrino F. Interspecies major histocompatibility complex-restricted Th cell epitope on foot-and-mouth disease virus capsid protein VP4. J Virol. 2000; 74:4902-7.
• [49]Blanco E, Garcia-Briones M, Sanz-Parra A, Gomes P, De Oliveira E, Valero ML, Andreu D, Ley V, Sobrino F. Identification of T-cell epitopes in nonstructural proteins of foot-and-mouth disease virus. J Virol. 2001; 75:3164-74.
• [50]García-Briones MM, Blanco E, Chiva C, Andreu D, Ley V, Sobrino F. Immunogenicity and T cell recognition in swine of foot-and-mouth disease virus polymerase 3D. Virology. 2004; 322:264-75.
• [51]Stevenson LS, Gilpin DF, Douglas A, McNeilly F, McNair I, Adair BM, Allan GM. T lymphocyte epitope mapping of porcine circovirus type 2. Viral Immunol. 2007; 20:389-98.
• [52]Vashisht K, Goldberg TL, Husmann RJ, Schnitzlein W, Zuckermann FA. Identification of immunodominant T-cell epitopes present in glycoprotein 5 of the North American genotype of porcine reproductive and respiratory syndrome virus. Vaccine. 2008; 26:4747-53.
• [53]Wang Y-X, Zhou Y-J, Li G-X, Zhang S-R, Jiang Y-F, Xu A-T, Yu H, Wang M-M, Yan L-P, Tong G-Z. Identification of immunodominant T-cell epitopes in membrane protein of highly pathogenic porcine reproductive and respiratory syndrome virus. Virus Res. 2011; 158:108-15.
• [54]Parida R, Choi I-S, Peterson DA, Pattnaik AK, Laegreid W, Zuckermann FA, Osorio FA. Location of T-cell epitopes in nonstructural proteins 9 and 10 of type-II porcine reproductive and respiratory syndrome virus. Virus Res. 2012; 169:13-21.
• [55]Díaz I, Pujols J, Ganges L, Gimeno M, Darwich L, Domingo M, Mateu E. In silico prediction and ex vivo evaluation of potential T-cell epitopes in glycoproteins 4 and 5 and nucleocapsid protein of genotype-I (European) of porcine reproductive and respiratory syndrome virus. Vaccine. 2009; 27:5603-11.
• [56]Zimic M, Gutiérrez AH, Gilman RH, López C, Quiliano M, Evangelista W, Gonzales A, García HH, Sheen P. Immunoinformatics prediction of linear epitopes from Taenia solium TSOL18. Bioinformation. 2011; 6:271-4.
• [57]Burgara-Estrella A, Díaz I, Rodríguez-Gómez IM, Essler SE, Hernández J, Mateu E. Predicted peptides from non-structural proteins of porcine reproductive and respiratory syndrome virus are able to induce IFN-γ and IL-10. Viruses. 2013; 5:663-77.
• [58]Terry FE, Moise L, Martin RF, Torres M, Pilotte N, Williams SA, De Groot AS. Time for T? Immunoinformatics addresses vaccine design for neglected tropical and emerging infectious diseases. Expert Rev Vaccines. 2015; 14:21-35.