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Huynh-Hoa Bui

Publications and source records attributed to Huynh-Hoa Bui.

16 recordsLinked to original sources
PubMed2007

Ab and T cell epitopes of influenza A virus, knowledge and opportunities.

The Immune Epitope Database and Analysis Resources (IEDB) (www.immuneepitope.org) was recently developed to capture epitope related data. IEDB also hosts various bioinformatics tools that can be used to identify novel epitopes as well as to analyze and visualize existing epitope data. Herein, a comprehensive analysis was undertaken (i) to compile and inventory existing knowledge regarding influenza A epitopes and (ii) to determine possible cross-reactivities of identified epitopes among avian H5N1 and human influenza strains. At present, IEDB contains >600 different epitopes derived from 58 different strains and 10 influenza A proteins. By using the IEDB analysis resources, conservancy analyses were performed, and several conserved and possibly cross-reactive epitopes were identified. Significant gaps in the current knowledge were also revealed, including paucity of Ab epitopes in comparison with T cell epitopes, limited number of epitopes reported for avian influenza strains/subtypes, and limited number of epitopes reported from proteins other than hemagglutinin and nucleoprotein. This analysis provides a resource for researchers to access existing influenza epitope data. At the same time, the analysis illustrates gaps in our collective knowledge that should inspire directions for further study of immunity against the influenza A virus.

Animals↗
PubMed2006

HLA-A2-restricted protection against lethal lymphocytic choriomeningitis.

The consequences of human lymphocytic choriomeningitis virus (LCMV) infection can be severe, including aseptic meningitis in immunocompetent individuals, hydrocephalus or chorioretinitis in fetal infection, or a highly lethal outcome in immunosuppressed individuals. In murine models of LCMV infection, CD8(+) T cells play a primary role in providing protective immunity, and there is evidence that cellular immunity may also be important in related arenavirus infections in humans. For this reason, we sought to identify HLA-A2 supertype-restricted epitopes from the LCMV proteome and evaluate them as vaccine determinants in HLA transgenic mice. We identified four HLA-A*0201-restricted peptides-nucleoprotein NP(69-77), glycoprotein precursor GPC(10-18), GPC(447-455), and zinc-binding protein Z(49-58)-that displayed high-affinity binding (< or =275 nM) to HLA-A*0201, induced CD8(+) T-cell responses of high functional avidity in HLA-A*0201 transgenic mice, and were naturally processed from native LCMV antigens in HLA-restricted human antigen presenting cells. One of the epitopes (GPC(447-455)), after peptide immunization of HLA-A*0201 mice, induced CD8(+) T cells capable of killing peptide-pulsed HLA-A*0201-restricted target cells in vivo and protected mice against lethal intracranial challenge with LCMV.

Amino Acid Sequence↗
PubMed2006

Curation of complex, context-dependent immunological data.

BACKGROUND: The Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data http://www.immuneepitope.org. DESCRIPTION: To identify and extract relevant data from the scientific literature in an efficient and accurate manner, novel processes were developed for manual and semi-automated annotation. CONCLUSION: Formalized curation strategies enable the processing of a large volume of context-dependent data, which are now available to the scientific community in an accessible and transparent format. The experiences described herein are applicable to other databases housing complex biological data and requiring a high level of curation expertise.

Allergy and Immunology↗
PubMed2006

A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus.

The value of predictive algorithms for identifying CD8+ T (T(CD8+))-cell epitopes has not been adequately tested experimentally. Here we demonstrate that conventional bioinformatic methods predict the vast majority of T(CD8+)-cell epitopes derived from vaccinia virus WR strain (VACV-WR) in the H-2(b) mouse model. This approach reveals the breadth of T-cell responses to vaccinia, a widely studied murine viral infection model, and may provide a tool for developing comprehensive antigenic maps of any complex pathogen.

Algorithms↗
PubMed2006

A community resource benchmarking predictions of peptide binding to MHC-I molecules.

Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.

Animals↗
PubMed2006

Structural prediction of peptides binding to MHC class I molecules.

Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling of peptide backbone conformations and flexible movement of MHC side chains and is unique among other prediction algorithms in its incorporation of explicit water molecules at the peptide-MHC interface. In an initial test of the algorithm, PePSSI was used to predict the conformation of eight peptides bound to HLA-A2, for which X-ray data are available. Comparison of the predicted and X-ray conformations of these peptides gave RMSD values between 1.301 and 2.475 A. Binding conformations of 266 peptides with known binding affinities for HLA-A2 were then predicted using PePSSI. Structural analyses of these peptide-HLA-A2 conformations showed that peptide binding affinity is positively correlated with the number of peptide-MHC contacts and negatively correlated with the number of interfacial water molecules. These results are consistent with the relatively hydrophobic binding nature of the HLA-A2 peptide binding interface. In summary, PePSSI is capable of rapid and accurate prediction of peptide-MHC binding conformations, which may in turn allow estimation of MHCI-peptide binding affinity.

Algorithms↗
PubMed2006

Predicting population coverage of T-cell epitope-based diagnostics and vaccines.

BACKGROUND: T cells recognize a complex between a specific major histocompatibility complex (MHC) molecule and a particular pathogen-derived epitope. A given epitope will elicit a response only in individuals that express an MHC molecule capable of binding that particular epitope. MHC molecules are extremely polymorphic and over a thousand different human MHC (HLA) alleles are known. A disproportionate amount of MHC polymorphism occurs in positions constituting the peptide-binding region, and as a result, MHC molecules exhibit a widely varying binding specificity. In the design of peptide-based vaccines and diagnostics, the issue of population coverage in relation to MHC polymorphism is further complicated by the fact that different HLA types are expressed at dramatically different frequencies in different ethnicities. Thus, without careful consideration, a vaccine or diagnostic with ethnically biased population coverage could result. RESULTS: To address this issue, an algorithm was developed to calculate, on the basis of HLA genotypic frequencies, the fraction of individuals expected to respond to a given epitope set, diagnostic or vaccine. The population coverage estimates are based on MHC binding and/or T cell restriction data, although the tool can be utilized in a more general fashion. The algorithm was implemented as a web-application available at http://epitope.liai.org:8080/tools/population. CONCLUSION: We have developed a web-based tool to predict population coverage of T-cell epitope-based diagnostics and vaccines based on MHC binding and/or T cell restriction data. Accordingly, epitope-based vaccines or diagnostics can be designed to maximize population coverage, while minimizing complexity (that is, the number of different epitopes included in the diagnostic or vaccine), and also minimizing the variability of coverage obtained or projected in different ethnic groups.

Algorithms↗
PubMed2005

HLA-A*0201, HLA-A*1101, and HLA-B*0702 transgenic mice recognize numerous poxvirus determinants from a wide variety of viral gene products.

In virus models explored in detail in mice, CTL typically focus on a few immunodominant determinants. In this study we use a multipronged approach to understand the diversity of CTL responses to vaccinia virus, a prototypic poxvirus with a genome approximately 20-fold larger than that of the model RNA viruses typically studied in mice. Based on predictive computational algorithms for peptide binding to HLA supertypes, we synthesized a panel of 2889 peptides to begin to create an immunomic map of human CTL responses to poxviruses. Using this panel in conjunction with CTLs from vaccinia virus-infected HLA transgenic mice, we identified 14 HLA-A*0201-, 4 HLA-A*1101-, and 3 HLA-B*0702-restricted CD8(+) T cell determinants distributed over 20 distinct proteins. These peptides were capable of binding one or multiple A2, A3, and B7 supertype molecules with affinities typical of viral determinants. Surprisingly, many of the viral proteins recognized are predicted to be late gene products, in addition to the early intermediate gene products expected. Nearly all of the determinants identified have identical counterparts encoded by modified vaccinia virus Ankara as well as variola virus, the agent of smallpox. These findings have implications for the design of new smallpox vaccines and the understanding of immune responses to large DNA viruses in general.

Animals↗
PubMed2005

An ontology for immune epitopes: application to the design of a broad scope database of immune reactivities.

BACKGROUND: Epitopes can be defined as the molecular structures bound by specific receptors, which are recognized during immune responses. The Immune Epitope Database and Analysis Resource (IEDB) project will catalog and organize information regarding antibody and T cell epitopes from infectious pathogens, experimental antigens and self-antigens, with a priority on NIAID Category A-C pathogens (http://www2.niaid.nih.gov/Biodefense/bandc_priority.htm) and emerging/re-emerging infectious diseases. Both intrinsic structural and phylogenetic features, as well as information relating to the interactions of the epitopes with the host's immune system will be catalogued. DESCRIPTION: To effectively represent and communicate the information related to immune epitopes, a formal ontology was developed. The semantics of the epitope domain and related concepts were captured as a hierarchy of classes, which represent the general and specialized relationships between the various concepts. A complete listing of classes and their properties can be found at http://www.immuneepitope.org/ontology/index.html. CONCLUSION: The IEDB's ontology is the first ontology specifically designed to capture both intrinsic chemical and biochemical information relating to immune epitopes with information relating to the interaction of these structures with molecules derived from the host immune system. We anticipate that the development of this type of ontology and associated databases will facilitate rigorous description of data related to immune epitopes, and might ultimately lead to completely new methods for describing and modeling immune responses.

Journal Article↗
PubMed2005

HLA class I-restricted responses to vaccinia recognize a broad array of proteins mainly involved in virulence and viral gene regulation.

We have analyzed by ex vivo ELISPOT the anti-vaccinia cytotoxic T lymphocyte responses of peripheral blood mononuclear cells from humans vaccinated with Dryvax vaccine. More than 6,000 peptides from 258 putative vaccinia ORFs predicted to bind the common molecules of the HLA A1, A2, A3, A24, B7, and B44 supertypes were screened with peripheral blood mononuclear cells of 31 vaccinees. A total of 48 epitopes derived from 35 different vaccinia antigens were identified, some of which (B8R, D1R, D5R, C10L, C19L, C7L, F12, and O1L) were recognized by multiple donors and contain multiple epitopes recognized in the context of different HLA types. The antigens recognized tend to be >100 residues in length and are expressed predominantly in the early phases of infection, although some late antigens were also recognized. Viral genome regulation and virulence factor were recognized most frequently, whereas few structural proteins were immunogenic. Finally, most epitopes were highly conserved among vaccinia virus Western Reserve, variola major and modified vaccinia Ankara, supporting their potential use in vaccine and diagnostic applications.

Amino Acid Sequence↗
PubMed2005

The design and implementation of the immune epitope database and analysis resource.

Epitopes are defined as parts of antigens interacting with receptors of the immune system. Knowledge about their intrinsic structure and how they affect the immune response is required to continue development of techniques that detect, monitor, and fight diseases. Their scientific importance is reflected in the vast amount of epitope-related information gathered, ranging from interactions between epitopes and major histocompatibility complex molecules determined by X-ray crystallography to clinical studies analyzing correlates of protection for epitope based vaccines. Our goal is to provide a central resource capable of capturing this information, allowing users to access and connect realms of knowledge that are currently separated and difficult to access. Here, we portray a new initiative, "The Immune Epitope Database and Analysis Resource." We describe how we plan to capture, structure, and store this information, what query interfaces we will make available to the public, and what additional predictive and analytical tools we will provide.

Animals↗
PubMed2005

Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications.

Prediction of which peptides can bind major histocompatibility complex (MHC) molecules is commonly used to assist in the identification of T cell epitopes. However, because of the large numbers of different MHC molecules of interest, each associated with different predictive tools, tool generation and evaluation can be a very resource intensive task. A methodology commonly used to predict MHC binding affinity is the matrix or linear coefficients method. Herein, we described Average Relative Binding (ARB) matrix methods that directly predict IC(50) values allowing combination of searches involving different peptide sizes and alleles into a single global prediction. A computer program was developed to automate the generation and evaluation of ARB predictive tools. Using an in-house MHC binding database, we generated a total of 85 and 13 MHC class I and class II matrices, respectively. Results from the automated evaluation of tool efficiency are presented. We anticipate that this automation framework will be generally applicable to the generation and evaluation of large numbers of MHC predictive methods and tools, and will be of value to centralize and rationalize the process of evaluation of MHC predictions. MHC binding predictions based on ARB matrices were made available at http://epitope.liai.org:8080/matrix web server.

Animals↗
PubMed2005

Characterization of the peptide-binding specificity of Mamu-A*11 results in the identification of SIV-derived epitopes and interspecies cross-reactivity.

The SIV-infected Indian rhesus macaque is the most established model of HIV infection, providing insight into pathogenesis and a system for testing novel vaccines. However, only a limited amount of information is available regarding the peptide-binding motifs and epitopes bound by their class I and class II MHC molecules. In this study, we utilized a library of over 1,000 different peptides and a high throughput MHC-peptide binding assay to detail the binding specificity of the rhesus macaque class I molecule Mamu-A*11. These studies defined the fine specificity of primary anchor positions, and dissected the role of secondary anchors, for peptides of 8-11 residues in length. This detailed information was utilized to develop size-specific polynomial algorithms to predict Mamu-A*11 binding capacity. Testing SIVmac239-derived Mamu-A*11 binding peptides for recognition by peripheral blood mononuclear cells (PBMC) from Mamu-A*11-positive, SIV-infected macaques, identified five novel SIV-derived Mamu-A*11 epitopes. Finally, we detected extensive cross-reactivity at the binding level between Mamu-A*11 and the mouse H-2 class I molecule Kk. Further experiments revealed that three out of four Mamu-A*11 binding peptides which bound Kk and were immunogenic in Kk mice were also recognized in Mamu-A*11-infected macaques. This is the first detailed description of mouse-macaque interspecies cross-reactivity, potentially useful in testing novel vaccines in mice and macaques.

Amino Acid Sequence↗
PubMed2005

A roadmap for the immunomics of category A-C pathogens.

The National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health (NIH), recently awarded 14 contracts to fund the Large-Scale Antibody and T Cell Epitope Discovery Program. This initiative is designed to identify immune epitopes from selected infectious agents utilizing complementary methods for epitope discovery. NIAID will make information on each newly identified epitope freely available to scientists worldwide through the Immune Epitope Database and Analysis Resource (IEDB), currently under development. On October 12-14, 2004, representatives of NIAID met in San Diego, California, with a group of investigators from various research institutions to discuss progress and plans for the large-scale epitope discovery projects and for the establishment of the IEDB. It is anticipated that these initiatives will establish detailed maps of immune reactions toward several important complex pathogens, which in turn will foster development of new diagnostic, immune-based therapeutic, and vaccine programs. Herein is an account of the meeting and its results.

Animals↗
PubMedDate not supplied

Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools.

A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.

Animals↗