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Brusic, Vladimir

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Brusic

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Vladimir

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Brusic, Vladimir
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    Large-Scale Analysis of B-Cell Epitopes on Influenza Virus Hemagglutinin – Implications for Cross-Reactivity of Neutralizing Antibodies
    (Frontiers Media S.A., 2014) Sun, Jing; Kudahl, Ulrich J.; Simon, Christian; Cao, Zhiwei; Reinherz, Ellis; Brusic, Vladimir
    Influenza viruses continue to cause substantial morbidity and mortality worldwide. Fast gene mutation on surface proteins of influenza virus result in increasing resistance to current vaccines and available antiviral drugs. Broadly neutralizing antibodies (bnAbs) represent targets for prophylactic and therapeutic treatments of influenza. We performed a systematic bioinformatics study of cross-reactivity of neutralizing antibodies (nAbs) against influenza virus surface glycoprotein hemagglutinin (HA). This study utilized the available crystal structures of HA complexed with the antibodies for the analysis of tens of thousands of HA sequences. The detailed description of B-cell epitopes, measurement of epitope area similarity among different strains, and estimation of antibody neutralizing coverage provide insights into cross-reactivity status of existing nAbs against influenza virus. We have developed a method to assess the likely cross-reactivity potential of bnAbs for influenza strains, either newly emerged or existing. Our method catalogs influenza strains by a new concept named discontinuous peptide, and then provide assessment of cross-reactivity. Potentially cross-reactive strains are those that share 100% identity with experimentally verified neutralized strains. By cataloging influenza strains and their B-cell epitopes for known bnAbs, our method provides guidance for selection of representative strains for further experimental design. The knowledge of sequences, their B-cell epitopes, and differences between historical influenza strains, we enhance our preparedness and the ability to respond to the emerging pandemic threats.
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    HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology
    (Oxford University Press, 2014) Zhang, Guang Lan; Riemer, Angelika B.; Keskin, Derin Benerci; Chitkushev, Lou; Reinherz, Ellis; Brusic, Vladimir
    High-risk human papillomaviruses (HPVs) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis and characterization of these cancers, it is necessary to make full use of the immunological data on HPV available through publications, technical reports and databases. These data vary in granularity, quality and complexity. The extraction of knowledge from the vast amount of immunological data using data mining techniques remains a challenging task. To support integration of data and knowledge in virology and vaccinology, we developed a framework called KB-builder to streamline the development and deployment of web-accessible immunological knowledge systems. The framework consists of seven major functional modules, each facilitating a specific aspect of the knowledgebase construction process. Using KB-builder, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2781 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. The HPVdb also catalogs 191 verified T cell epitopes and 45 verified human leukocyte antigen (HLA) ligands. Primary amino acid sequences of HPV antigens were collected and annotated from the UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature and databases. The data were subject to extensive quality control (redundancy elimination, error detection and vocabulary consolidation). A set of computational tools for an in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, classification of HPV types based on cancer risk, T cell epitope/HLA ligand visualization, T cell epitope/HLA ligand conservation analysis and sequence variability analysis, has been integrated within the HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a knowledge-based system that integrates curated data and information with tailored analysis tools to facilitate data mining for HPV vaccinology and immunology. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of HPV antigens and peptides. Database URL: http://cvc.dfci.harvard.edu/hpv/
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    Ultra-high resolution HLA genotyping and allele discovery by highly multiplexed cDNA amplicon pyrosequencing
    (BioMed Central, 2012) Lank, Simon M; Golbach, Brittney A; Creager, Hannah M; Wiseman, Roger W; Keskin, Derin Benerci; Reinherz, Ellis; Brusic, Vladimir; O’Connor, David H
    Background: High-resolution HLA genotyping is a critical diagnostic and research assay. Current methods rarely achieve unambiguous high-resolution typing without making population-specific frequency inferences due to a lack of locus coverage and difficulty in exon-phase matching. Achieving high-resolution typing is also becoming more challenging with traditional methods as the database of known HLA alleles increases. Results: We designed a cDNA amplicon-based pyrosequencing method to capture 94% of the HLA class I open-reading-frame with only two amplicons per sample, and an analogous method for class II HLA genes, with a primary focus on sequencing the DRB loci. We present a novel Galaxy server-based analysis workflow for determining genotype. During assay validation, we performed two GS Junior sequencing runs to determine the accuracy of the HLA class I amplicons and DRB amplicon at different levels of multiplexing. When 116 amplicons were multiplexed, we unambiguously resolved 99%of class I alleles to four- or six-digit resolution, as well as 100% unambiguous DRB calls. The second experiment, with 271 multiplexed amplicons, missed some alleles, but generated high-resolution, concordant typing for 93% of class I alleles, and 96% for DRB1 alleles. In a third, preliminary experiment we attempted to sequence novel amplicons for other class II loci with mixed success. Conclusions: The presented assay is higher-throughput and higher-resolution than existing HLA genotyping methods, and suitable for allele discovery or large cohort sampling. The validated class I and DRB primers successfully generated unambiguously high-resolution genotypes, while further work is needed to validate additional class II genotyping amplicons.
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    Conservation Analysis of Dengue Virus T-cell Epitope-Based Vaccine Candidates Using Peptide Block Entropy
    (Frontiers Research Foundation, 2011) Olsen, Lars Rønn; Zhang, Guang Lan; Keskin, Derin Benerci; Reinherz, Ellis; Brusic, Vladimir
    Broad coverage of the pathogen population is particularly important when designing CD8+ T-cell epitope vaccines against viral pathogens. Traditional approaches are based on combinations of highly conserved T-cell epitopes. Peptide block entropy analysis is a novel approach for assembling sets of broadly covering antigens. Since T-cell epitopes are recognized as peptides rather than individual residues, this method is based on calculating the information content of blocks of peptides from a multiple sequence alignment of homologous proteins rather than using the information content of individual residues. The block entropy analysis provides broad coverage of variant antigens. We applied the block entropy analysis method to the proteomes of the four serotypes of dengue virus (DENV) and found 1,551 blocks of 9-mer peptides, which cover 99% of available sequences with five or fewer unique peptides. In contrast, the benchmark study by Khan et al. (2008) resulted in 165 conserved 9-mer peptides. Many of the conserved blocks are located consecutively in the proteins. Connecting these blocks resulted in 78 conserved regions. Of the 1551 blocks of 9-mer peptides 110 comprised predicted HLA binder sets. In total, 457 subunit peptides that encompass the diversity of all sequenced DENV strains of which 333 are T-cell epitope candidates.
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    Direct Identification of an HPV-16 Tumor Antigen from Cervical Cancer Biopsy Specimens
    (Frontiers Research Foundation, 2011) Keskin, Derin Benerci; Reinhold, Bruce; Lee, Sun Young; Zhang, Guanglan; Lank, Simon; O’Connor, David H.; Berkowitz, Ross; Brusic, Vladimir; Kim, Seung Jo; Reinherz, Ellis
    Persistent infection with high-risk human papilloma viruses (HPV) is the worldwide cause of many cancers, including cervical, anal, vulval, vaginal, penile, and oropharyngeal. Since T cells naturally eliminate the majority of chronic HPV infections by recognizing epitopes displayed on virally altered epithelium, we exploited Poisson detection mass spectrometry (MS\(^3\)) to identify those epitopes and inform future T cell-based vaccine design. Nine cervical cancer biopsies from HPV-16 positive HLA-A*02 patients were obtained, histopathology determined, and E7 oncogene PCR-amplified from tumor DNA and sequenced. Conservation of E7 oncogene coding segments was found in all tumors. MS\(^3\) analysis of HLA-A*02 immunoprecipitates detected E7\(_{11–19}\) peptide (YMLDLQPET) in seven of the nine tumor biopsies. The remaining two samples were E7\(_{11–19}\) negative and lacked the HLA-A*02 binding GILT thioreductase peptide despite possessing binding-competent HLA-A*02 alleles. Thus, the conserved E7\(_{11–19}\) peptide is a dominant HLA-A*02 binding tumor antigen in HPV-16 transformed cervical squamous and adenocarcinomas. Findings that a minority of HLA-A*02:01 tumors lack expression of both E7\(_{11–19}\) and a peptide from a thioreductase important in processing of cysteine-rich proteins like E7 underscore the value of physical detection, define a potential additional tumor escape mechanism and have implications for therapeutic cancer vaccine development.
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    Predicting Peptides Binding to MHC Class II Molecules Using Multi-objective Evolutionary Algorithms
    (BioMed Central, 2007) Rajapakse, Menaka; Schmidt, Bertil; Feng, Lin; Brusic, Vladimir
    Background: Peptides binding to Major Histocompatibility Complex (MHC) class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides. In this paper, we propose two approaches using multi-objective evolutionary algorithms (MOEA) for predicting peptides binding to MHC class II molecules. One uses the information from both binders and non-binders for self-discovery of motifs. The other, in addition, uses information from experimentally determined motifs for guided-discovery of motifs. Results: The proposed methods are intended for finding peptides binding to MHC class II I-A\(^{g7}\) molecule – a promiscuous binder to a large number of low affinity peptides. Cross-validation results across experiments on two motifs derived for I-A\(^{g7}\) datasets demonstrate better generalization abilities and accuracies of the present method over earlier approaches. Further, the proposed method was validated and compared on two publicly available benchmark datasets: (1) an ensemble of qualitative HLA-DRB1*0401 peptide data obtained from five different sources, and (2) quantitative peptide data obtained for sixteen different alleles comprising of three mouse alleles and thirteen HLA alleles. The proposed method outperformed earlier methods on most datasets, indicating that it is well suited for finding peptides binding to MHC class II molecules. Conclusion: We present two MOEA-based algorithms for finding motifs, one for self-discovery and the other for guided-discovery by experimentally determined motifs, and thereby predicting binding peptides to I-A\(^{g7}\) molecule. Our experiments show that the proposed MOEA-based algorithms are better than earlier methods in predicting binding sites not only on I-A\(^{g7}\) but also on most alleles of class II MHC benchmark datasets. This shows that our methods could be applicable to find binding motifs in a wide range of alleles.
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    Evaluation of MHC Class I Peptide Binding Prediction Servers: Applications for Vaccine Research
    (BioMed Central, 2008) Lin, Hong Huang; Ray, Surajit; Tongchusak, Songsak; Reinherz, Ellis; Brusic, Vladimir
    Background: Protein antigens and their specific epitopes are formulation targets for epitope-based vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of appropriate prediction servers difficult. This study reports a comparative evaluation of thirty prediction servers for seven human MHC-I molecules. Results: Of 147 individual predictors 39 have shown excellent, 47 good, 33 marginal, and 28 poor ability to classify binders from non-binders. The classifiers for HLA-A*0201, A*0301, A*1101, B*0702, B*0801, and B*1501 have excellent, and for A*2402 moderate classification accuracy. Sixteen prediction servers predict peptide binding affinity to MHC-I molecules with high accuracy; correlation coefficients ranging from r = 0.55 (B*0801) to r = 0.87 (A*0201). Conclusion: Non-linear predictors outperform matrix-based predictors. Most predictors can be improved by non-linear transformations of their raw prediction scores. The best predictors of peptide binding are also best in prediction of T-cell epitopes. We propose a new standard for MHC-I binding prediction – a common scale for normalization of prediction scores, applicable to both experimental and predicted data. The results of this study provide assistance to researchers in selection of most adequate prediction tools and selection criteria that suit the needs of their projects.