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PubMed2003

IMGT-ONTOLOGY for immunogenetics and immunoinformatics.

IMGT, the international ImMunoGeneTics information system(R) (http://imgt.cines.fr), is a high quality integrated knowledge resource specializing in immunoglobulins (IG), T cell receptors (TR), major histocompatibility complex (MHC) and related proteins of the immune system (RPI) of human and other vertebrates, created in 1989, by the Laboratoire d'ImmunoGenetique Moleculaire LIGM. IMGT provides a common access to standardized data which include nucleotide and protein sequences, oligonucleotide primers, gene maps, genetic polymorphisms, specificities, 2D and 3D structures. IMGT consists of several sequence databases (IMGT/LIGM-DB, IMGT/MHC-DB, IMGT/PRIMER-DB), one genome database (IMGT/GENE-DB) and one three-dimensional structure database (IMGT/3Dstructure-DB), interactive tools for sequence analysis (IMGT/V-QUEST, IMGT/JunctionAnalysis, IMGT/PhyloGene, IMGT/Allele-Align), for genome analysis (IMGT/GeneSearch, IMGT/GeneView, IMGT/LocusView) and for 3D structure analysis (IMGT/StructuralQuery), and Web resources ("IMGT Marie-Paule page") comprising 8000 HTML pages. IMGT other accesses include SRS, FTP, search by BLAST, etc. By its high quality and its easy data distribution, IMGT has important implications in medical research (repertoire in autoimmune diseases, AIDS, leukemias, lymphomas, myelomas), veterinary research, genome diversity and genome evolution studies of the adaptive immune responses, biotechnology related to antibody engineering (scFv, phage displays, combinatorial libraries) and therapeutical approaches (grafts, immunotherapy). IMGT is freely available at http://imgt.cines.fr.

Animals↗
PubMed2003

The future for computational modelling and prediction systems in clinical immunology.

Advances in computational science, despite their enormous potential, have been surprisingly slow to impact on clinical practice. This paper examines the potential of bioinformatics to advance clinical immunology across a number of key examples including the use of computational immunology to improve renal transplantation outcomes, identify novel genes involved in immunological disorders, decipher the relationship between antigen presentation pathways and human disease, and predict allergenicity. These examples demonstrate the enormous potential for immunoinformatics to advance clinical and experimental immunology. The acceptance of immunoinformatic techniques by clinical and research immunologists will need robust standards of data quality, system integrity and properly validated immunoinformatic systems. Such validation, at a minimum, will require appropriately designed clinical studies conducted according to Good Clinical Practice standards. This strategy will enable immunoinformatics to achieve its full potential to advance and shape clinical immunology in the future.

Allergens↗
PubMed2006

Bioinformatics for study of autoimmunity.

Recent years have witnessed an explosive growth in available biological data pertaining to autoimmunity research. This includes a tremendous quantity of sequence data (biological structures, genetic and physical maps, pathways, etc.) generated by genome and proteome projects plus extensive clinical and epidemiological data. Autoimmunity research stands to greatly benefit from this data so long as appropriate strategies are available to enable full access to and utilization of this data. The quantity and complexity of this biological data necessitates use of advanced bioinformatics strategies for its efficient retrieval, analysis and interpretation. Major progress has been made in development of specialized tools for storage, analysis and modeling of immunological data, and this has led to development of a whole new field know as immunoinformatics. With advances in novel high-throughput immunology technologies immunoinformatics is transforming understanding of how the immune system functions. This paper reviews advances in the field of immunoinformatics pertinent to autoimmunity research including databases, tools in genomics and proteomics, tools for study of B- and T-cell epitopes, integrative approaches, and web servers.

Allergy and Immunology↗
PubMed2003

Bioinformatic strategies for better understanding of immune function.

Novartis Foundation sponsored a Symposium which brought together a group of experimental immunologists, theoretical immunologists, and bioinformaticians to discuss the new field of immunoinformatics. The discussion focused on immunological databases, antigen processing and presentation, immunogenomics, host-pathogen interactions, and mathematical modelling of the immune system. A main conclusion of the meeting is the critical role played by immunoinformatics in current immunology research. In particular, immunoinformatics provides a foundation for the emerging fields of systems immunology and immunogenomics.

Allergy and Immunology↗
PubMed2006

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models.

BACKGROUND: The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. RESULTS: We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. CONCLUSION: As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Algorithms↗
PubMed2026

Comparative Genomics-Guided Epitope Prioritization and in Silico Design of a Multi-Epitope DNA Vaccine Candidate Against Megalocytivirus pagrus 1.

Megalocytivirus pagrus 1 infection is a World Organisation for Animal Health-listed aquatic animal disease caused by a virus species comprising the RSIV, ISKNV, and TRBIV genogroups. Here, we integrated comparative genomics and immunoinformatics to prioritize a multi-epitope protein construct, pMEV, and to design a DNA vaccine candidate encoding it, with emphasis on RSIV-type infection relevant to rock bream aquaculture. Analysis of 61 complete genomes identified 28 core gene clusters, from which myristoylated membrane protein (MMP) and major capsid protein (MCP) were prioritized as source antigens for epitope screening. Four cytotoxic T-cell, five helper T-cell, and five linear B-cell epitope candidates were selected based on sequence-based screening and exploratory peptide-MHC docking. The selected epitopes were assembled with rock bream beta-defensin-3, PADRE, and peptide linkers to generate the 283-aa pMEV construct. Sequence-based physicochemical analyses indicated properties relevant to subsequent structural and expression-based evaluation, while computationally refined structural modeling identified nine putative conformational B-cell epitope regions. TLR3 docking, normal mode analysis, and a 200-ns molecular dynamics simulation characterized the structural behavior of the selected computational complex without inferring receptor activation. C-ImmSim further generated model-dependent generic humoral and helper T-cell-associated response patterns within a mammalian-based simulation framework. Finally, the pMEV coding sequence was codon-optimized and incorporated into an in silico pcDNA3.1(+)-based DNA vaccine design. Collectively, this study provides a comparative genomics-guided framework for prioritizing an experimentally testable multi-epitope DNA vaccine candidate against M. pagrus 1, while construct expression, immunogenicity, and protective efficacy remain to be evaluated experimentally.

Animals↗
PubMed2026

Influence of Major Histocompatibility Complex (MHC) Diversity on Immune Modulation, Pathogenesis, and Control of Lumpy Skin Disease Virus.

INTRODUCTION: Lumpy Skin Disease Virus (LSDV), a member of the genus Capripoxvirus within the family Poxviridae, is an economically important transboundary viral pathogen affecting cattle and water buffalo. The disease causes severe production losses through decreased milk yield, infertility, hide damage, reduced growth performance, and occasional mortality. The rapid geographic spread of LSDV, together with its vectorborne transmission and emerging recombinant strains, has intensified the need for improved understanding of viral pathogenesis, host immune responses, and effective prevention strategies. In particular, the role of the bovine Major Histocompatibility Complex (BoLA/MHC) in regulating antiviral immunity, disease susceptibility, and vaccine responsiveness has gained increasing scientific attention. METHODS: This review summarises the published literature related to the epidemiology, transmission, structure, pathogenesis, diagnosis, prevention, and control of LSDV, with special emphasis on the immunological and molecular role of bovine MHC molecules. Relevant studies concerning BoLA-mediated antigen presentation, immunoinformaticsbased epitope prediction, vaccine development, antiviral drug repurposing, molecular docking, genomic surveillance, and diagnostic approaches, including PCR- and ELISAbased assays, were critically evaluated. Recent advances in computational biology, molecular virology, and host-pathogen interaction studies were also reviewed. RESULTS: The reviewed studies demonstrate that Lumpy Skin Disease Virus (LSDV) possesses a complex double-stranded DNA genome enabling immune modulation and efficient transmission through arthropod vectors such as mosquitoes, ticks, and biting flies. Disease progression involves systemic viral replication, vascular injury, dermal necrosis, and inflammatory skin lesions. Real-time PCR remains the most sensitive diagnostic method for early detection, while ELISA supports surveillance. Evidence highlights the central role of bovine Major Histocompatibility Complex (BoLA) molecules in antigen presentation and T-cell activation. Computational studies identified promising BoLA-binding epitopes and repurposed antiviral candidates, including ivermectin, theaflavin, canagliflozin, and tepotinib, for future therapeutic development. DISCUSSION: Current evidence indicates that effective LSDV control requires integration of molecular diagnostics, vector management, vaccination, and host immunogenetics. BoLAguided immunoinformatics provides promising opportunities for developing multi-epitope vaccines, although experimental validation remains essential. Similarly, repurposed antiviral candidates require comprehensive in vivo and pharmacological evaluation before clinical application. Future research should focus on elucidating viral immune-evasion mechanisms, validating predicted epitopes, and translating computational findings into practical vaccines and therapeutics for sustainable disease control. CONCLUSION: Lumpy Skin Disease continues to pose a major threat to global cattle health and livestock economies. Advances in molecular diagnostics, genomic surveillance, antiviral drug discovery, and BoLA-guided vaccine design provide promising opportunities for improved disease control. Understanding the interaction between LSDV and the bovine MHC system is essential for developing next-generation vaccines, immunotherapeutics, and precision disease-management strategies. Future research should prioritise experimental validation of predicted epitopes, large-scale vaccine trials, and mechanistic studies on host-virus immune interactions to establish effective and sustainable global control programs for LSDV.

BoLA↗
PubMed2001

Controlling the immune system: diffuse feedback via a diffuse informational network.

Diffuse feedback is defined to be a process by which a system in some sense improves its performance with respect to a variety of conflicting and even contradictory goals. In the immune system, such feedback is mediated by scores of extracellular chemicals (cytokines), each of which participates in achieving several goals. Progress toward any given goal is mediated by several cytokines. The 'immunoinformatics' of this diffuse informational network will be discussed. It will be shown how diffuse feedbacks, based on this network, can improve the performance of a given type of immune effector cell, and can cause the preferential amplification of more potent effectors. It will be argued that diffuse feedback also acts in other biological systems ranging from the metabolic system to ant colonies.

Animals↗
PubMed2005

Production of a monoclonal antibody against SARS-CoV spike protein with single intrasplenic immunization of plasmid DNA.

Severe acute respiratory syndrome (SARS) is a highly infectious disease caused by a novel coronavirus (SARS-CoV). Specific monoclonal antibodies (mAbs) against the SARS-CoV are vital for early diagnosis and pathological studies of SARS. Direct intrasplenic inoculation of plasmid DNA encoding antigen is an effective and fast approach to generate specific mAb when the protein antigen is difficult to prepare or dangerous in use. In this study, we selected one fragment of SARS-CoV spike protein (S1-(3)) as antigenic determinant by immunoinformatics. Single intrasplenic immunization of plasmid DNA encoding S1-(3) induced anti-spike protein antibodies. We established one hybridoma cell line secreting specific mAb and evaluated this mAb with murine leukemia virus pseudotyped with SARS-CoV spike protein (MLV/SARS-CoV). The mAb could recognize the spike protein on the MLV/SARS-CoV-infected Vero E6 cells albeit with no neutralizing effect on the infectivity of the pseudotype virus. Our results show that a single-shot intrasplenic DNA immunization is efficient for the production of specific mAb against SARS spike protein, and a linear epitope of the spike protein is recognized in this study.

Animals↗
PubMed2005

T cells recognize PD(N/T)R motif common in a variable number of tandem repeat and degenerate repeat sequences of MUC1.

The tumor-associated antigen MUC1 is a transmembrane glycoprotein, which is overexpressed in human carcinomas. Peptide epitopes, containing the PDTR fragment from the variable number of tandem repeat (VNTR) domains of MUC1 have been found to be immunodominant in T-cell and B-cell responses. However, little is known about the immunogenicity and specificity of T-cell epitopes from other regions of MUC1 that may also participate in immune responses against tumors. In this study, the combination of immunoinformatics, molecular modeling and a vaccine adjuvant strategy were used to predict and describe a novel T-cell epitope, SAPDNRPAL, located within the degenerate tandem repeat of MUC1. This peptide possesses structural similarity to both VNTR-derived SAPDTRPAP and Sendai virus peptide FAPGNYPAL, which are known to induce cytotoxic T lymphocytes (CTL). We found that SAPDNRPAL had a higher affinity for mouse H-D(b), H-2K(b) and human HLA-A2 molecules than SAPDTRPAP. A chimeric peptide (CP) containing SAPDNRPAL and an adjuvant C5a-derived decapeptide induced epitope-specific type 1 T cells in human MUC1 transgenic mice (ELISPOT). Mice that received dendritic cells (DC) pulsed with the CP or a 25-mer peptide containing the SAPDNRPAL sequence showed increased frequencies of SAPDNRPAL- and SAPDTRPAP-specific interferon-gamma producing T cells. PDTR-specific antibody 214D4 reacted with both SAPDNRPAL and SAPDTRPAP (ELISA). Altogether, our data suggest that the degenerate MUC1 repeat sequence contains the immunogenic T-cell epitope SAPDNRPAL, which is cross-reactive with the VNTR-derived peptide SAPDTRPAP. We suggest that the use of immunogenic PDNR-containing epitope(s) in vaccine strategies could be beneficial for developing increased, PD(N/T)R motif-specific T-cell responses against tumors expressing MUC1.

Animals↗
PubMed2006

Prediction and molecular modeling of T-cell epitopes derived from placental alkaline phosphatase for use in cancer immunotherapy.

In our ongoing efforts to combat cancer, peptide-based tumor vaccines are promising as one of the several alternatives used for cancer immunotherapy and immunoprevention. We have attempted to identify T-cell epitopes suitable for the development of a peptide-based cancer vaccine directed towards placental isozyme of alkaline phosphatase (PLAP), an oncofetal antigen. After identifying amino acid residues specific to PLAP and distinct from other close PLAP homologs, we have used sequence-based immunoinformatics tools (BIMAS and SYFPEITHI) and conducted molecular modeling studies using InsightII to investigate the binding affinity of the epitopes containing the unique residues with respective MHC class I molecules. Promiscuous epitopes binding to different alleles of different class I HLA loci were analyzed to get a population coverage that is widespread. Binding affinity deduced from the modeling studies corroborated the status of most of the epitopes scoring high in BIMAS and SYFPEITHI. We have thus identified specific epitopes from PLAP that have a potential for binding to their respective MHC class I alleles with high affinity. These peptides would be analysed in experiments to demonstrate their involvement in the induction of primary cytotoxic T-cell responses in vitro, using respective HLA-restricted T-cells in our way towards the development of an effective anti-cancer vaccine in a background of diverse MHC haplotypes.

Alkaline Phosphatase↗
PubMed2005

Integrated modeling of the major events in the MHC class I antigen processing pathway.

Rational design of epitope-driven vaccines is a key goal of immunoinformatics. Typically, candidate selection relies on the prediction of MHC-peptide binding only, as this is known to be the most selective step in the MHC class I antigen processing pathway. However, proteasomal cleavage and transport by the transporter associated with antigen processing (TAP) are essential steps in antigen processing as well. While prediction methods exist for the individual steps, no method has yet offered an integrated prediction of all three major processing events. Here we present WAPP, a method combining prediction of proteasomal cleavage, TAP transport, and MHC binding into a single prediction system. The proteasomal cleavage site prediction employs a new matrix-based method that is based on experimentally verified proteasomal cleavage sites. Support vector regression is used for predicting peptides transported by TAP. MHC binding is the last step in the antigen processing pathway and was predicted using a support vector machine method, SVMHC. The individual methods are combined in a filtering approach mimicking the natural processing pathway. WAPP thus predicts peptides that are cleaved by the proteasome at the C terminus, transported by TAP, and show significant affinity to MHC class I molecules. This results in a decrease in false positive rates compared to MHC binding prediction alone. Compared to prediction of MHC binding only, we report an increased overall accuracy and a lower rate of false positive predictions for the HLA-A*0201, HLA-B*2705, HLA-A*01, and HLA-A*03 alleles using WAPP. The method is available online through our prediction server at http://www-bs.informatik.uni-tuebingen.de/WAPP

ATP-Binding Cassette Transporters↗
PubMed2026

Leptospira-host interactions: advancing next-generation vaccines and diagnostics.

SUMMARYLeptospirosis, a widespread zoonotic disease caused by pathogenic Leptospira species, remains a major public health challenge, particularly in tropical and subtropical regions. Despite advances in understanding Leptospira biology and pathogenesis, effective disease control continues to be limited by the lack of rapid, early diagnostics, and broadly protective vaccines. This review comprehensively examines recent progress in deciphering Leptospira-host interactions, with emphasis on key virulence factors, immune-evasion mechanisms, and host immune responses that influence disease outcomes. Particular focus is placed on the molecular and cellular basis of adhesion, invasion, immune modulation, and persistent colonization. We further discuss the limitations of current vaccines and diagnostic approaches, and highlight how emerging technologies, including pan-genomics, proteomics, reverse vaccinology, immunoinformatics, and omics-based antigen discovery, are facilitating the development of next-generation vaccines and diagnostics. Finally, we outline major translational challenges and future perspectives for improving clinical management, surveillance, and prevention of leptospirosis. The concepts discussed in this review may also provide broader insights into vaccine and diagnostic development for other zoonotic bacterial infections.

Humans↗
PubMed2004

Immunome-derived vaccines.

Immune response to a subset of antigens and epitopes derived from an infectious pathogen may be sufficient for competent protection; immune recognition of every potential epitope derived from the pathogen's genome does not appear to be required. The pneumococcal and hepatitis vaccines, both of which are subunit vaccines, illustrate this premise. Similarly, 'immunome-derived vaccines' are based on the concept that response to the subset of antigens and epitopes that interface with the host immune system (the immunome) and not the whole organism (represented by the proteome or genome) can be sufficient for protection. Competent immune responses to cancer are also probably restricted to the neoplasm's 'immunome', although the set of antigens that drive successful immune response to cancer cells has proven more difficult to uncover. Researchers are now using bioinformatics sequence analysis tools, epitope mapping tools, microarrays and high-throughput immunology assays to discover the components of the immunome, which are then used to compose these new vaccines. At least one immunome-derived vaccine is in clinical trials and many others are in the vaccine pipeline. Due to the rapid improvement of immunoinformatics tools and immunological assays, the era of immunome-derived vaccines has begun.

Animals↗
PubMed2004

Genome-derived vaccines.

Vaccine research entered a new era when the complete genome of a pathogenic bacterium was published in 1995. Since then, more than 97 bacterial pathogens have been sequenced and at least 110 additional projects are now in progress. Genome sequencing has also dramatically accelerated: high-throughput facilities can draft the sequence of an entire microbe (two to four megabases) in 1 to 2 days. Vaccine developers are using microarrays, immunoinformatics, proteomics and high-throughput immunology assays to reduce the truly unmanageable volume of information available in genome databases to a manageable size. Vaccines composed by novel antigens discovered from genome mining are already in clinical trials. Within 5 years we can expect to see a novel class of vaccines composed by genome-predicted, assembled and engineered T- and Bcell epitopes. This article addresses the convergence of three forces--microbial genome sequencing, computational immunology and new vaccine technologies--that are shifting genome mining for vaccines onto the forefront of immunology research.

Animals↗
PubMed2005

Information technologies for vaccine research.

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

Animals↗
PubMed2003

Databases and data mining for computational vaccinology.

Drugs and vaccines are keys to the effective fight against disease. While the pharmaceutical industry has developed an awesome array of real and virtual approaches to rational drug discovery, the complexity of the immune system hampers attempts to design and develop vaccines in a rational manner. The goal of immunoinformatics (the application of informatics techniques to immunological macromolecules), an emergent sub-discipline of bioinformatics, is to develop computational vaccinology as a potent tool in the quest for new vaccines. Databases and data mining, the two principal weapons at the disposal of the in silico vaccinologist, will be presented in the light of current developments.

Computational Biology↗
PubMed2003

MHCPred: bringing a quantitative dimension to the online prediction of MHC binding.

The accurate prediction of T cell epitopes is one of the key aspirations of immunoinformatics. We have developed a partial least squares-based, robust multivariate statistical method for the quantitative prediction of peptide binding to major histocompatibility complexes (MHCs), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available as a World Wide Web server.

Algorithms↗