En esta página encontrará recursos e informaciones sobre las aplicaciones bioinformáticas relacionadas con el sistema inmune.
BASES DE DATOS Y HERRAMIENTAS:
ALLERGOME: Base de datos sobre alergenos reportados que inducen respuesta IgE y otras fuentes de alergias, aunque no se haya identificado la molécula responsable. Requiere registro.
BIMAS: Permite predecir péptidos de una proteína a ser presentados por una molécula MHC particular.
Bcepred: Predicción de epítopes B lineales a partir de propiedades fisicoquímicas.
CTLPred: Predicción de epítopes CTL basado en máquina de soporte vectorial y red neuronal artificial.
dbMHC: base con datos clínicos y de secuencia del MHC (Major Histocompatibility Complex) humano.
EpiSearch: Busca epítopes conformacionales a partir de una secuencia.
FluKB: Propiedades inmunológicas de virus de la influenza.
HLA-DR4Pred: Predicción de péptidos con afinidad por HLA-DRB1*0401, basado en máquina de soporte vectorial y red neuronal artificial.
HLAPred: Predicción de péptidos con afinidad para MHC-I y -II.
HIV Molecular Immunology Database: colección de epítopes T, para CD4 y CD8, así como sitios de unión de anticuerpos para VIH-1.
IEDB: Immune Epitope Database and Analysis Resource, contiene datos sobre epítopes T y de anticuerpos de origen humano y de otras especies.
IMGT: Proyecto internacional ImMunoGeneTics, una colección de bases de datos integradas, especializadas en inmunoglobulinas, receptores de células T y el MHC de vertebrados.
ImmPort: Immunology Database and Analysis Portal, almacén de datos (data warehouse) obtenidos por investigadores del NIAID.
MAPP: Predice epítopes con afinidad por MHC-I de humanos y otras especies.
MHCBench: Interfase con varios algoritmos de predicción de péptidos con afinidad por MHC.
MMBPred: Predicción de secuencias mutadas de alta afinidad por MHC-I.
MOT: Técnica de optimización de matrices para la predicción de unión a MHC-II.
NetChop: Predice sitios de escisión por proteasoma.
NetMHCIIpan-2.0: Predice unión de péptidos a más de 500 alelos HLA-DR por medio de redes neurales artificiales.
nHLAPred: Predicción de péptidos con afinidad para MHC-I, basado en red neuronal.
PAProC: Predice sitios de escisión por el proteasoma.
ProPred-I: Servicio en línea para identificar regiones con afinidad por MHC-I en antígenos.
ProPred: Herramientas para identificar epítopes con afinidad por MHC-II.
SDAP – Structural Database of Allergenic Proteins: Base de datos de proteínas alergénicas con varias herramientas computacionales para estudios de alergenos.
SYFPEITHI: Base de datos con más de 7000 péptidos con afinidad por MHC I y II. Permite hacer predicción de péptidos a partir de una secuencia de proteína.
ARTÍCULOS DE INTERÉS:
– Clifford JN, Høie MH, Deleuran S, Peters B, Nielsen M, Marcatili P. BepiPred-3.0: Improved B-cell epitope prediction using protein language models. Protein Sci. 2022 Dec;31(12):e4497.
– Moghram BA, Nabil E, Badr A. Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design. Comput Methods Programs Biomed. 2018 Jan;153:161-170.
– Dhanik A, Kirshner JR, MacDonald D, Thurston G, Lin HC, Murphy AJ, et al. In-silico discovery of cancer-specific peptide-HLA complexes for targeted therapy. BMC Bioinformatics 2016;17:286.
– Dar H, Zaheer T, Rehman MT, Ali A, Javed A, Khan GA, et al. Prediction of promiscuous T-cell epitopes in the Zika virus polyprotein: An in silico approach. Asian Pac J Trop Med. 2016 Sep;9(9):844-50.
– Tapia D, Ross BN, Kalita A, Kalita M, Hatcher CL, Muruato LA, et al. From In silico Protein Epitope Density Prediction to Testing Escherichia coli O157:H7 Vaccine Candidates in a Murine Model of Colonization. Front Cell Infect Microbiol. 2016 Aug 30;6:94.
– de Freitas R, Gomes LF, Zaldini M, Felinto ME, Coutinho B, da Silva AA, et al. Combination of In Silico Methods in the Search for Potential CD4+ and CD8+ T Cell Epitopes in the Proteome of Leishmania braziliensis. Front. Immunol., 29 August 2016; http://dx.doi.org/10.3389/fimmu.2016.00327.
– Hackl H, Charoentong P, Finotello F, Trajanoski Z. Computational genomics tools for dissecting tumour–immune cell interactions. Nature Reviews Genetics 2016;17:441–458.
– Sheikh QM, Gatherer D, Reche PA, Flower DR. Towards the Knowledge-based Design of Universal Influenza Epitope Ensemble Vaccines. Bioinformatics 2016; doi: 10.1093/bioinformatics/btw399.
– Levy M, Thaiss CA, Elinav E. Metagenomic cross-talk: the regulatory interplay between immunogenomics and the microbiome. Genome Medicine 2015;7:120.
– Backert L, Kohlbacher O. Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Medicine 2015;7:119.
– Schultze JL. Teaching ‘big data’ analysis to young immunologists. Nature Immunology 2015;16:902–905.
– Vishnu Udayakumar S, Sankarasubramanian J, Gunasekaran P, Rajendhran J. Novel Vaccine Candidates against Brucella melitensis Identified through Reverse Vaccinology Approach. OMICS: A Journal of Integrative Biology November 2015;19(11):722-729.
– Molero-Abraham M, Glutting JP, Flower DR, Lafuente EM, Reche PA. EPIPOX: Immunoinformatic Characterization of the Shared T-Cell Epitome between Variola Virus and Related Pathogenic Orthopoxviruses. J Immunol Res. 2015;2015:738020.
– Simon C, Kudahl UJ, Sun J, Olsen LR, Zhang GL, Reinherz EL, et al. FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses. Journal of Immunology Research 2015;2015:380975.
– Tyagi N, Farnell EJ, Fitzsimmons CM, Ryan S, Tukahebwa E, Maizels RM, et al. Comparisons of Allergenic and Metazoan Parasite Proteins: Allergy the Price of Immunity. PLoS Comput Biol 2015;11(10):e1004546.
– Tsangemail JS. Utilizing population variation, vaccination, and systems biology to study human immunology. Trends in Immunology August 2015;36(8):479–493.
– Pellegrino P, Falvella FS, Perrone V, Carnovale C, Brusadelli T, Pozzi M, et al. The first steps towards the era of personalised vaccinology: predicting adverse reactions. The Pharmacogenomics Journal 2015;15:284–287.
– Calis JJA, Reinink P, Keller C, Kloetzel PM, Kesmir C. Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs. Immunogenetics February 2015;67(2):85-93.
– Schubert B, Brachvogel HP, Jürges CH, Kohlbacher O. EpiToolKit—a web-based workbench for vaccine design. Bioinformatics 2015; doi: 10.1093/bioinformatics/btv116.
– Oany AR, Ahmad SA, Hossain MU, Jyoti TP. Identification of highly conserved regions in L-segment of Crimean-Congo hemorrhagic fever virus and immunoinformatic prediction about potential novel vaccine. Adv Appl Bioinform Chem. 2015 Jan 8;8:1-10.
– Jameson-Lee M, Koparde V, Griffith P, Scalora AF, Sampson JK, Khalid H, et al. In silico derivation of HLA-specific alloreactivity potential from whole exome sequencing of stem-cell transplant donors and recipients: understanding the quantitative immunobiology of allogeneic transplantation. Front. Immunol., 06 November 2014; doi: 10.3389/fimmu.2014.00529.
– Pappalardo F, Brusic V, Castiglione F, Schönbach C. Computational and bioinformatics techniques for immunology. Biomed Res Int. 2014;2014:263189.
– Moorhouse MJ, van Zessen D, IJspeert H, Hiltemann S, Horsman S, van der Spek PJ, et al. ImmunoGlobulin galaxy (IGGalaxy) for simple determination and quantitation of immunoglobulin heavy chain rearrangements from NGS. BMC Immunology 2014;15:59.
– McGarvey PB, Suzek BE, Baraniuk JN, Rao S, Conkright B, Lababidi S, et al. In silico analysis of autoimmune diseases and genetic relationships to vaccination against infectious diseases. BMC Immunology 2014;15:61.
– Curigliano G. From precision medicine to cancer care through the immunome: highlights from the European Society of Medical Oncology Congress, Madrid, 26-30th September 2014. Ecancermedicalscience. 2014 Oct 16;8:472.
– Teh-Poot C, Tzec-Arjona E, Martinez-Vega P, Ramirez-Sierra MJ, Rosado-Vallado M, Dumonteil E. From genome to a vaccine against Trypanosoma cruzi by immunoinformatics. J Infect Dis. 2014 Jul 28; doi: 10.1093/infdis/jiu418.
– Snijder B, Kandasamy RK, Superti-Furga G. Toward effective sharing of high-dimensional immunology data. Nature Biotechnology 2014;32:755–759.
– Zhang GL, Sun J, Chitkushev L, Brusic V. Big data analytics in immunology: a knowledge-based approach. Biomed Res Int. 2014:437987. doi: 10.1155/2014/437987.
– Zhang S, Desrosiers J, Aponte-Pieras JR, DaSilva K, Fast LD, et al. Human Immune Responses to H. pylori HLA Class II Epitopes Identified by Immunoinformatic Methods. PLoS ONE 2014;9(4):e94974.
– Gaze S, Driguez P, Pearson MS, Mendes T, Doolan DL, et al. An Immunomics Approach to Schistosome Antigen Discovery: Antibody Signatures of Naturally Resistant and Chronically Infected Individuals from Endemic Areas. PLoS Pathog 2014;10(3):e1004033.
– Zhang GL, Riemer AB, Keskin DB, Chitkushev L, Reinherz EL, Brusic V. HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology. Database 2014 Apr 4;2014:bau031. doi: 10.1093/database/bau031.
– Zhong J, Sharma J, Raju R, Palapetta SM, Prasad TSK, Huang TC, et al. TSLP signaling pathway map: a platform for analysis of TSLP-mediated signaling. Database 2014:bau007; doi: 10.1093/database/bau007.
– Dimitrakopoulou K, Dimitrakopoulos GN, Wilk E, Tsimpouris C, Sgarbas KN, Schughart K, Bezerianos A. Influenza A Immunomics and Public Health Omics: The Dynamic Pathway Interplay in Host Response to H1N1 Infection. OMICS: A Journal of Integrative Biology. February 10, 2014; doi:10.1089/omi.2013.0062.
– Whelan FJ, Yap NVL, Surette MG, Golding GB, Bowdish DME. A guide to bioinformatics for immunologists. Front. Immunol. 04 December 2013; doi: 10.3389/fimmu.2013.00416.
– Grifoni A, Montesano C, Patronov A, Colizzi V, Amicosante M. Immunoinformatic Docking Approach for the Analysis of KIR3DL1/HLA-B Interaction. BioMed Research International 2013;283805.
– Addine BC, Marrón R, Calero R, Mirabal M, Ramírez JC, Sarmiento ME, et al. In silico identification of common epitopes from pathogenic mycobacteria. BMC Immunology 2013;14(Suppl 1):S6.
– Patronov A, Doytchinova I. T-cell epitope vaccine design by immunoinformatics. Open Biol. 2013;3:120-139.
– Sun J, Kudahl UJ, Simon C, Cao Z, Reinherz EL, Brusic V. Large-scale analysis of B-cell epitopes on influenza virus hemagglutinin – implications for cross-reactivity of neutralizing antibodies. Front. Immunol., 07 February 2014;doi: 10.3389/fimmu.2014.00038.
– Olsen LR, Kudahl UJ, Simon C, Sun J, Schönbach C, Reinherz EL, et al. BlockLogo: visualization of peptide and sequence motif conservation. J Immunol Methods. 2013 Dec 31;400-401:37-44.
– Priya Doss CG, Nagasundaram N, Srajan J, Chiranji C. LSHGD: A database for human leprosy susceptible genes. Genomics September 2012;100(3):162–166.
– Benichou J, Ben-Hamo R, Louzoun Y, Efroni S. Rep-Seq: uncovering the immunological repertoire through next-generation sequencing. Immunology March 2012;135(3):183–191.
– Zhang L, Chen Y, Wong H-S, Zhou S, Mamitsuka H, et al. TEPITOPEpan: Extending TEPITOPE for Peptide Binding Prediction Covering over 700 HLA-DR Molecules. PLoS ONE 2012;7(2): e30483.
– Seib KL, Zhao X, Rappuoli R. Developing vaccines in the era of genomics: a decade of reverse vaccinology. Clinical Microbiology and Infection 2012;18:109-116.
– Levitz L, Koita OA, Sangare K, Ardito MT, Boyle CM, Rozehnal J et al. Conservation of HIV-1 T cell epitopes across time and clades: Validation of immunogenic HLA-A2 epitopes selected for the GAIA HIV vaccine. Retrovirology. 2012;9(Suppl 2):P294.
– Saethang T, Hirose O, Kimkong I, Tran VA, Dang XT, Nguyen LA et al. EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information. BMC Bioinformatics 2012, 13:313.
– Prabdial-Sing N, Puren AJ, Bowyer SM. Sequence-based in silico analysis of well studied Hepatitis C Virus epitopes and their variants in other genotypes (particularly genotype 5a) against South African human leukocyte antigen backgrounds. BMC Immunology 2012;13:67.
– Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J et al. Immune epitope database analysis resource. Nucl. Acids Res. 2012;40(W1):W525-W530.
– Chailyan A, Tramontano A, Marcatili P. A database of immunoglobulins with integrated tools: DIGIT. Nucl. Acids Res. 2012;40(D1):D1230-D1234.
– Moss SF, Moise L, Lee DS, Kim W, Zhang S, Lee J et al. HelicoVax: Epitope-based therapeutic H. pylori vaccination in a mouse model. Vaccine. 2011 March 3;29(11):2085–2091.
– Patronov A, Dimitrov I, Flower DR, Doytchinova I. Peptide binding prediction for the human class II MHC allele HLA-DP2: a molecular docking approach. BMC Structural Biology 2011;11:32.
– Megan M. O’Meara and Mary L. Disis. Therapeutic Cancer Vaccines and Translating Vaccinomics Science to the Global Health Clinic: Emerging Applications Toward Proof of Concept. OMICS: A Journal of Integrative Biology. September 2011;15(9):579-588.
– Bagnoli F, Baudner B, Mishra RPN, Bartolini E, Fiaschi L, Mariotti P, et al. Designing the Next Generation of Vaccines for Global Public Health. OMICS: A Journal of Integrative Biology. September 2011;15(9):545-566.
– Poland GA, Ovsyannikova IG, Kennedy RB, Haralambieva IH, Jacobson RM. Vaccinomics and a New Paradigm for the Development of Preventive Vaccines Against Viral Infections. OMICS: A Journal of Integrative Biology. September 2011;15(9):625-636.
– Bocanegra-García V, Valencia-Delgadillo J, Cruz-Pulido W, Cantú-Ramírez R, Rivera-Sánchez G, Palma-Nicolás JP. De la genética a la genómica en el diseño de nuevas vacunas contra la tuberculosis. Enferm Infecc Microbiol Clin. 2011;29(8):609-14.
– Loukas A, Gaze S, Mulvenna JP, Gasser RB, Brindley PJ, Doolan DL et al. Vaccinomics for the Major Blood Feeding Helminths of Humans. OMICS: A Journal of Integrative Biology. Online Ahead of Print: June 16, 2011.
– Bremel RD, Homan EJ. An integrated approach to epitope analysis II: A system for proteomic-scale prediction of immunological characteristics. Immunome Research 2010;6:8.
– Bremel RD, Homan EJ. An integrated approach to epitope analysis I: Dimensional reduction, visualization and prediction of MHC binding using amino acid principal components and regression approaches. Immunome Research 2010;6:7.
– Ansari HR, Raghava GPS. Identification of conformational B-cell Epitopes in an antigen from its primary sequence. Immunome Research 2010;6:6.
– Cong H, Mui EJ, Witola WH, Sidney J, Alexander J, Sette A et al. Human immunome, bioinformatic analyses using HLA supermotifs and the parasite genome, binding assays, studies of human T cell responses, and immunization of HLA-A*1101 transgenic mice including novel adjuvants provide a foundation for HLA-A03 restricted CD8+T cell epitope based, adjuvanted vaccine protective against Toxoplasma gondii. Immunome Research 2010;6:12.
– Nielsen M, Justesen S, Lund O, Lundegaard C, Buus S. NetMHCIIpan-2.0 – Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure. Immunome Research 2010;6:9.
– Wee LJK, Simarmata D, Kam YW, Ng LFP, Tong JC. SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction. BMC Genomics 2010, 11(Suppl 4):S21.
– Lane J, Duroux P, Lefranc MP. From IMGT-ONTOLOGY to IMGT/LIGMotif: the IMGT® standardized approach for immunoglobulin and T cell receptor gene identification and description in large genomic sequences. BMC Bioinformatics 2010;11:223.
– Flower DR, Macdonald IK, Ramakrishnan K, Davies MN, Doytchinova IA. Computer aided selection of candidate vaccine antigens. Immunome Res. 2010; 6(Suppl 2):S1.
– Dimitrov I, Garnev P, Flower DR, Doytchinova I. EpiTOP—a proteochemometric tool for MHC class II binding prediction. Bioinformatics 2010;26(16):2066-2068.
– Dimitrov I, Garnev P, Flower DR, Doytchinova I. MHC Class II Binding Prediction—A Little Help from a Friend. J Biomed Biotechnol. 2010;2010:705821.
– Zhao l, Li J. Mining for the antibody-antigen interacting associations that predict the B cell epitopes. BMC Structural Biology 2010;10(Suppl 1):S6.
– Ehrenmann F, Kaas Q, Lefranc MP. IMGT/3Dstructure-DB and IMGT/DomainGapAlign: a database and a tool for immunoglobulins or antibodies, T cell receptors, MHC, IgSF and MhcSF. Nucleic Acids Research 2010;38(Database issue):D301-D307.
– Ansari HR, Flower DR, Raghava GPS. AntigenDB: an immunoinformatics database of pathogen antigens. Nucleic Acids Research 2010;38(Database issue):D847-D853.
– Feldhahn M, Dönnes P, Thiel P., Kohlbacher O. FRED—a framework for T-cell epitope detection. Bioinformatics 2009 25(20):2758-2759.
– Lefranc MP, Giudicelli V, Ginestoux C, Jabado-Michaloud J, Folch G, Bellahcene F et al. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res. 2009 Jan;37(Database issue):D1006-12.
– Huang YX, Bao YL, Guo Sh, Wang Y, Zhou CG, Li YX. Pep-3D-Search: a method for B-cell epitope prediction based on mimotope analysis. BMC Bioinformatics 2008;9:538.
– De Groot AS, Rivera DS, McMurry JA, Buus S, Martina W. Identification of immunogenic HLA-B7 “Achilles’ heel” epitopes within highly conserved regions of HIV. Vaccine. 2008 June 6;26(24):3059–3071.
– Trost B, Bickis M, Kusalik A. Strength in numbers: achieving greater accuracy in MHC-I binding prediction by combining the results from multiple prediction tools. Immunome Research 2007;3:5.
– Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007;8:4.
– Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 2007;8:238.
– Korber B, LaBute M, Yusim K. Immunoinformatics Comes of Age. PLoS Comput Biol 2006;2(6):e71.
– Liu W, Meng X, Xu Q, Flower DR, Li T. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinformatics 2006;7:182.
– Petrovsky N, Schönbach C, Brusic V. Bioinformatic strategies for better understanding of immune function. In Silico Biology 2003;3:0034.
– Singh H, Raghava GPS. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics 2003;19(8):1009-1014.
