Biomedical subjects

Vladimir Brusic

Publications and source records attributed to Vladimir Brusic.

At least 19 recordsLinked to original sources

PubMed2006

PRED(TAP): a system for prediction of peptide binding to the human transporter associated with antigen processing.

BACKGROUND: The transporter associated with antigen processing (TAP) is a critical component of the major histocompatibility complex (MHC) class I antigen processing and presentation pathway. TAP transports antigenic peptides into the endoplasmic reticulum where it loads them into the binding groove of MHC class I molecules. Because peptides must first be transported by TAP in order to be presented on MHC class I, TAP binding preferences should impact significantly on T-cell epitope selection. DESCRIPTION: PRED(TAP) is a computational system that predicts peptide binding to human TAP. It uses artificial neural networks and hidden Markov models as predictive engines. Extensive testing was performed to valid the prediction models. The results showed that PRED(TAP) was both sensitive and specific and had good predictive ability (area under the receiver operating characteristic curve Aroc>0.85). CONCLUSION: PRED(TAP) can be integrated with prediction systems for MHC class I binding peptides for improved performance of in silico prediction of T-cell epitopes. PRED(TAP) is available for public use at [1].

Journal Article↗

PubMed2006

Prediction of HLA-DQ3.2beta ligands: evidence of multiple registers in class II binding peptides.

MOTIVATION: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. METHODS: We describe a structure-based prediction model for modeling peptide-DQ3.2beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2beta binding and non-binding peptides obtained from biochemical and functional studies. RESULTS: Our model predicts DQ3.2beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.

Antibodies↗

PubMed2006

SCORPION2: a database for structure-function analysis of scorpion toxins.

Scorpion toxins are important experimental tools for characterization of vast array of ion channels and serve as scaffolds for drug design. General public database entries contain limited annotation whereby rich structure-function information from mutation studies is typically not available. SCORPION2 contains more than 800 records of native and mutant toxin sequences enriched with binding affinity and toxicity information, 624 three-dimensional structures and some 500 references. SCORPION2 has a set of search and prediction tools that allow users to extract and perform specific queries: text searches of scorpion toxin records, sequence similarity search, extraction of sequences, visualization of scorpion toxin structures, analysis of toxic activity, and functional annotation of previously uncharacterized scorpion toxins. The SCORPION2 database is available at http://sdmc.i2r.a-star.edu.sg/scorpion/.

Animals↗

PubMed2006

SARS coronavirus nucleocapsid immunodominant T-cell epitope cluster is common to both exogenous recombinant and endogenous DNA-encoded immunogens.

Correspondence between the T-cell epitope responses of vaccine immunogens and those of pathogen antigens is critical to vaccine efficacy. In the present study, we analyzed the spectrum of immune responses of mice to three different forms of the SARS coronavirus nucleocapsid (N): (1) exogenous recombinant protein (N-GST) with Freund's adjuvant; (2) DNA encoding unmodified N as an endogenous cytoplasmic protein (pN); and (3) DNA encoding N as a LAMP-1 chimera targeted to the lysosomal MHC II compartment (p-LAMP-N). Lysosomal trafficking of the LAMP/N chimera in transfected cells was documented by both confocal and immunoelectron microscopy. The responses of the immunized mice differed markedly. The strongest T-cell IFN-gamma and CTL responses were to the LAMP-N chimera followed by the pN immunogen. In contrast, N-GST elicited strong T cell IL-4 but minimal IFN-gamma responses and a much greater antibody response. Despite these differences, however, the immunodominant T-cell ELISpot responses to each of the three immunogens were elicited by the same N peptides, with the greatest responses being generated by a cluster of five overlapping peptides, N76-114, each of which contained nonameric H2d binding domains with high binding scores for both class I and, except for N76-93, class II alleles. These results demonstrate that processing and presentation of N, whether exogenously or endogenously derived, resulted in common immunodominant epitopes, supporting the usefulness of modified antigen delivery and trafficking forms and, in particular, LAMP chimeras as vaccine candidates. Nevertheless, the profiles of T-cell responses were distinctly different. The pronounced Th-2 and humoral response to N protein plus adjuvant are in contrast to the balanced IFN-gamma and IL-4 responses and strong memory CTL responses to the LAMP-N chimera.

Amino Acid Sequence↗

PubMed2006

Deduction of functional peptide motifs in scorpion toxins.

Scorpion toxins are important physiological probes for characterizing ion channels. Molecular databases have limited functional annotation of scorpion toxins. Their function can be inferred by searching for conserved motifs in sequence signature databases that are derived statistically but are not necessarily biologically relevant. Mutation studies provide biological information on residues and positions important for structure-function relationship but are not normally used for extraction of binding motifs. 3D structure analyses also aid in the extraction of peptide motifs in which non-contiguous residues are clustered spatially. Here we present new, functionally relevant peptide motifs for ion channels, derived from the analyses of scorpion toxin native and mutant peptides.

Amino Acid Motifs↗

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↗

PubMed2006

PREDNOD, a prediction server for peptide binding to the H-2g7 haplotype of the non-obese diabetic mouse.

The non-obese diabetic (NOD) mouse is a widely used animal model for study of autoimmune diseases, in particular human type 1 diabetes mellitus (T1DM). Identification of the subset of peptides that bind MHC molecules comprising the H-2g7 haplotype of NOD mouse and thereby representing potential NOD T-cell epitopes is important for research into the pathogenesis and immunotherapy of T1DM. The H-2g7 haplotype comprises the MHC class-I molecules Kd and Db and a single class-II molecule I-Ag7. We have developed a prediction system, PREDNOD, for accurate identification of peptides that bind the MHC molecules constituting the H-2g7 haplotype. PREDNOD is accessible at http://antigen.i2r.a-star.edu.sg/Ag7.

Algorithms↗

PubMed2006

Information management for the study of allergies.

Microarrays and other large-scale screening technologies produce quantities of increasingly complex allergy data. These data link molecular and clinical measurements and observations and provide fertile ground for improving our understanding of the processes involved in allergic reactions. Information technology is employed in gathering, storage, retrieval and analysis of these data. The increasing proportion of allergy data are generated from genomics and proteomics approaches. The major activity focuses on characterization of allergens including IgE reactivity, structural properties, and mapping of IgE and T-cell epitopes. Because of the complexity of allergy data, their utilization requires bioinformatics approaches. Allergen data are stored in the general and specialist databases. At least a dozen of important allergen databases and data repositories have been developed to date. These data are analysed using general and specialist bioinformatics tools. The major applications of bioinformatics include support for allergen characterization, assessment of allergenicity, and identification of allergic cross-reactivity. These applications in turn support the development of vaccines and therapies for allergic disease. In this article we review allergen databases and tools for the analysis of allergens, and discuss the new directions in the field supported by large scale screening involving genomics, proteomics, and bioinformatics support.

Allergens↗

PubMed2005

MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides.

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability (area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets--termed T-cell epitope hotspots. MULTIPRED is available at http://antigen.i2r.a-star.edu.sg/multipred/.

Computational Biology↗

PubMed2005

PREDBALB/c: a system for the prediction of peptide binding to H2d molecules, a haplotype of the BALB/c mouse.

PRED(BALB/c) is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2(d)) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2(d) class I (H2-K(d), H2-L(d) and H2-D(d)) and class II (I-E(d) and I-A(d)) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PRED(BALB/c) is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2(d) haplotype). PRED(BALB/c) is available at http://antigen.i2r.a-star.edu.sg/predBalbc/.

Animals↗

PubMed2005

One NY-ESO-1-derived epitope that promiscuously binds to multiple HLA-DR and HLA-DP4 molecules and stimulates autologous CD4+ T cells from patients with NY-ESO-1-expressing melanoma.

NY-ESO-1 is expressed by a broad range of human tumors and is often recognized by Abs in the sera of cancer patients with NY-ESO-1-expressing tumors. The NY-ESO-1 gene also encodes several MHC class I- and class II-restricted tumor epitopes recognized by T lymphocytes. In this study we report one novel pan-MHC class II-restricted peptide sequence, NY-ESO-1 87-111, that is capable of binding to multiple HLA-DR and HLA-DP4 molecules, including HLA-DRB1*0101, 0401, 0701, and 1101 and HLA-DPB1*0401 and 0402 molecules. We also demonstrate that peptide NY-ESO-1 87-111 stimulates Th1-type and Th-2/Th0-type CD4(+) T cells and clones when presented in the context of these HLA-DR and HLA-DP4 molecules. Both bulk CD4(+) T cells and CD4(+) T cell clones were capable of recognizing not only peptide-pulsed APCs, but also autologous dendritic cells, either loaded with the NY-ESO-1 protein or transfected with NY-ESO-1 cDNAs. Using IFN-gamma and IL-5 ELISPOT assays and PBL from patients with NY-ESO-1-expressing tumors, we observed the existence of Th1-type circulating CD4(+) T cells recognizing peptide NY-ESO-1 87-111 in the context of HLA-DP4 molecules. Taken together, these data represent the first report of an HLA-DR- and HLA-DP-restricted epitope from a tumor Ag. They also support the relevance of cancer vaccine trials with peptides NY-ESO-1 87-111 in the large number of cancer patients with NY-ESO-1-expressing tumors.

Alleles↗

PubMed2005

Accurate prediction of scorpion toxin functional properties from primary structures.

Scorpion toxins are common experimental tools for studies of biochemical and pharmacological properties of ion channels. The number of functionally annotated scorpion toxins is steadily growing, but the number of identified toxin sequences is increasing at much faster pace. With an estimated 100,000 different variants, bioinformatic analysis of scorpion toxins is becoming a necessary tool for their systematic functional analysis. Here, we report a bioinformatics-driven system involving scorpion toxin structural classification, functional annotation, database technology, sequence comparison, nearest neighbour analysis, and decision rules which produces highly accurate predictions of scorpion toxin functional properties.

Algorithms↗

PubMed2005

Neural models for predicting viral vaccine targets.

We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve--AROC>0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC>0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) membrane protein were predicted by using MULTIPRED.

Algorithms↗

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↗

PubMed2005

An update on the functional molecular immunology (FIMM) database.

Data on the major histocompatibility complex, T-cell epitopes, B-cell epitopes, antigens and diseases are heterogeneous and scattered among different databases and the literature. Since it has become increasingly difficult to obtain an integrated view of functional immune response components, we have developed and updated over several years the Functional molecular IMMunology (FIMM) database (http:// research.i2r.a-star.edu.sg/fimm/). FIMM contains integrated expert-curated data on protein antigens, and on human immunological receptors that recognise and bind them in healthy or disease states. Interfaces with multiple, intuitive query options and query reports provide immunologists with prioritised information that aids data interpretation, vaccine target discovery and immune disease research.

Allergy and Immunology↗

PubMed2005

Supporting the curation of biological databases with reusable text mining.

Curators of biological databases transfer knowledge from scientific publications, a laborious and expensive manual process. Machine learning algorithms can reduce the workload of curators by filtering relevant biomedical literature, though their widespread adoption will depend on the availability of intuitive tools that can be configured for a variety of tasks. We propose a new method for supporting curators by means of document categorization, and describe the architecture of a curator-oriented tool implementing this method using techniques that require no computational linguistic or programming expertise. To demonstrate the feasibility of this approach, we prototyped an application of this method to support a real curation task: identifying PubMed abstracts that contain allergen cross-reactivity information. We tested the performance of two different classifier algorithms (CART and ANN), applied to both composite and single-word features, using several feature scoring functions. Both classifiers exceeded our performance targets, the ANN classifier yielding the best results. These results show that the method we propose can deliver the level of performance needed to assist database curation.

Allergens↗

PubMed2004

Systematic analysis of snake neurotoxins' functional classification using a data warehousing approach.

MOTIVATION: Sequence annotations, functional and structural data on snake venom neurotoxins (svNTXs) are scattered across multiple databases and literature sources. Sequence annotations and structural data are available in the public molecular databases, while functional data are almost exclusively available in the published articles. There is a need for a specialized svNTXs database that contains NTX entries, which are organized, well annotated and classified in a systematic manner. RESULTS: We have systematically analyzed svNTXs and classified them using structure-function groups based on their structural, functional and phylogenetic properties. Using conserved motifs in each phylogenetic group, we built an intelligent module for the prediction of structural and functional properties of unknown NTXs. We also developed an annotation tool to aid the functional prediction of newly identified NTXs as an additional resource for the venom research community. AVAILABILITY: We created a searchable online database of NTX proteins sequences (http://research.i2r.a-star.edu.sg/Templar/DB/snake_neurotoxin). This database can also be found under Swiss-Prot Toxin Annotation Project website (http://www.expasy.org/sprot/).

Animals↗

PubMed2004

CysView: protein classification based on cysteine pairing patterns.

CysView is a web-based application tool that identifies and classifies proteins according to their disulfide connectivity patterns. It accepts a dataset of annotated protein sequences in various formats and returns a graphical representation of cysteine pairing patterns. CysView displays cysteine patterns for those records in the data with disulfide annotations. It allows the viewing of records grouped by connectivity patterns. CysView's utility as an analysis tool was demonstrated by the rapid and correct classification of scorpion toxin entries from GenPept on the basis of their disulfide pairing patterns. It has proved useful for rapid detection of irrelevant and partial records, or those with incomplete annotations. CysView can be used to support distant homology between proteins. CysView is publicly available at http://research.i2r.a-star.edu.sg/CysView/.

Computer Graphics↗