Identification of vaccine candidate antigens from a genomic analysis of Porphyomonas gingivalis

Porphyromonas gingivalis is a key periodontal pathogen which has been implicated in the etiology of chronic adult periodontitis. Our aim was to develop a protein based vaccine for the prevention and or treatment of this disease. We used a whole genome sequencing approach to identify potential vaccine candidates. From a genomic sequence, we selected 120 genes using a series of bioinformatics methods. The selected genes were cloned for expression in Escherichia coli and screened with P. gingivalis antisera before purification and testing in an animal model. Two of these recombinant proteins (PG32 and PG33) demonstrated significant protection in the animal model, while a number were reactive with various antisera. This process allows the rapid identification of vaccine candidates from genomic data. (C) 2001 Elsevier Science Ltd. All rights reserved.

Assessment of the ability to model proteins with leucine-rich repeats in light of the latest structural information

The three-dimensional structures of leucine-rich repeat (LRR) -containing proteins from five different families were previously predicted based on the crystal structure of the ribonuclease inhibitor. using an approach that combined homology-based modeling, structure-based sequence alignment of LRRs, and several rational assumptions. The structural models have been produced based on very limited sequence similarity, which, in general. cannot yield trustworthy predictions. Recently, the protein structures from three of these five families have been determined. In this report we estimate the quality of the modeling approach by comparing the models with the experimentally determined structures. The comparison suggests that the general architecture, curvature, interior/exterior orientations of side chains. and backbone conformation of the LRR structures can be predicted correctly. On the other hand. the analysis revealed that, in some cases. it is difficult to predict correctly the twist of the overall super-helical structure. Taking into consideration the conclusions from these comparisons, we identified a new family of bacterial LRR proteins and present its structural model. The reliability of the LRR protein modeling suggests that it would be informative to apply similar modeling approaches to other classes of solenoid proteins.

Ex vivo profiling of CD8(+)-T-cell responses to human cytomegalovirus reveals broad and multispecific reactivities in healthy virus carriers

Human cytomegalovirus (HCMV) can establish both nonproductive (latent) and productive (lytic) infections. Many of the proteins expressed during these phases of infection could be expected to be targets of the immune response; however, much of our understanding of the CD8(+)-T-cell response to HCMV is mainly based on the pp65 antigen. Very little is known about T-cell control over other antigens expressed during the different stages of virus infection; this imbalance in our understanding undermines the importance of these antigens in several aspects of HCMV disease pathogenesis. In the present study, an efficient and rapid strategy based on predictive bioinformatics and ex vivo functional T-cell assays was adopted to profile CD8(+)-T-cell responses to a large panel of HCMV antigens expressed during different phases of replication. These studies revealed that CD8(+)-T-cell responses to HCMV often contained multiple antigen-specific reactivities, which were not just constrained to the previously identified pp65 or IE-1 antigens. Unexpectedly, a number of viral proteins including structural, early/late antigens and HCMV-encoded immunomodulators (pp28, pp50, gH, gB, US2, US3, US6, and UL18) were also identified as potential targets for HCMV-specific CD8(+)-T-cell immunity. Based on this extensive analysis, numerous novel HCMV peptide epitopes and their HLA-restricting determinants recognized by these T cells have been defined. These observations contrast with previous findings that viral interference with the antigen-processing pathway during lytic infection would render immediate-early and early/late proteins less immunogenic. This work strongly suggests that successful HCMV-specific immune control in healthy virus carriers is dependent on a strong T-cell response towards a broad repertoire of antigens.

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

We consider the problem of assessing the number of clusters in a limited number of tissue samples containing gene expressions for possibly several thousands of genes. It is proposed to use a normal mixture model-based approach to the clustering of the tissue samples. One advantage of this approach is that the question on the number of clusters in the data can be formulated in terms of a test on the smallest number of components in the mixture model compatible with the data. This test can be carried out on the basis of the likelihood ratio test statistic, using resampling to assess its null distribution. The effectiveness of this approach is demonstrated on simulated data and on some microarray datasets, as considered previously in the bioinformatics literature. (C) 2004 Elsevier Inc. All rights reserved.

Searching oligo sets of human chromosome 12 using evolutionary strategies

DNA Microarray is a powerful tool to measure the level of a mixed population of nucleic acids at one time, which has great impact in many aspects of life sciences research. In order to distinguish nucleic acids with very similar composition by hybridization, it is necessary to design microarray probes with high specificities and sensitivities. Highly specific probes correspond to probes having unique DNA sequences; whereas highly sensitive probes correspond to those with melting temperature within a desired range and having no secondary structure. The selection of these probes from a set of functional DNA sequences (exons) constitutes a computationally expensive discrete non-linear search problem. We delegate the search task to a simple yet effective Evolution Strategy algorithm. The computational efficiency is also greatly improved by making use of an available bioinformatics tool.

Challenges to environmental toxicology and epidemiology: where do we stand and which way do we go?

Modern toxicology investigates a wide array of both old and new health hazards. Priority setting is needed to select agents for research from the plethora of exposure circumstances. The changing societies and a growing fraction of the aged have to be taken into consideration. A precise exposure assessment is of importance for risk estimation and regulation. Toxicology contributes to the exploration of pathomechanisms to specify the exposure metrics for risk estimation. Combined effects of co-existing agents are not yet sufficiently understood. Animal experiments allow a separate administration of agents which can not be disentangled by epidemiological means, but their value is limited for low exposure levels in many of today's settings. As an experimental science, toxicology has to keep pace with the rapidly growing knowledge about the language of the genome and the changing paradigms in cancer development. During the pioneer era of assembling a working draft of the human genome, toxicogenomics has been developed. Gene and pathway complexity have to be considered when investigating gene-environment interactions. For a best conduct of studies, modem toxicology needs a close liaison with many other disciplines like epidemiology and bioinformatics. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

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. (c) 2005 Elsevier Inc. All rights reserved.

The chemistry and biology of human relaxin-3

A novel member of the human relaxin subclass of the insulin superfamily was recently discovered during a genomics database search and named relaxin-3. Like human relaxin-1 and relaxin-2, relaxin-3 is predicted to consist of a two-chain structure and three disulfide bonds in a disposition identical to that of insulin. To undertake detailed biophysical and biological characterization of the peptide, its chemical synthesis was undertaken. In contrast to human relaxin-1 and relaxin-2, however, relaxin-3 could not be successfully prepared by simple combination of the individual chains, thus necessitating recourse to the use of a regioselective disulfide bond formation strategy. Solid phase synthesis of the separate, selectively S-protected A and B chains followed by their purification and the subsequent stepwise formation of each of the three disulfides led to the successful acquisition of human relaxin-3. Comprehensive chemical characterization confirmed both the correct chain orientation and the integrity of the synthetic product. Relaxin-3 was found to bind to and activate native relaxin receptors in vitro and stimulate water drinking through central relaxin receptors in vivo. Recent studies have demonstrated that relaxin-3 will bind to and activate human LGR7, but not LGR8, in vitro. Secondary structural analysis showed it to adopt a less ordered confirmation than either relaxin-1 or relaxin-2, reflecting the presence in the former of a greater percentage of nonhelical forming amino acids. NMR spectroscopy and simulated annealing calculations were used to determine the three-dimensional structure of relaxin-3 and to identify key structural differences between the human relaxins.

A Novel Mammalian Retromer Component, Vps26B

The mammalian retromer protein complex, which consists of three proteins - Vps26, Vps29, and Vps35 - in association with members of the sorting nexin family of proteins, has been implicated in the trafficking of receptors and their ligands within the endosomal/lysosomal system of mammalian cells. A bioinformatic analysis of the mouse genome identified an additional transcribed paralog of the Vps26 retromer protein, which we termed Vps26B. No paralogs were identified for Vps29 and Vps35. Phylogenetic studies indicate that the two paralogs of Vps26 become evident after the evolution of the chordates. We propose that the chordate Vps26-like gene published previously be renamed Vps26A to differentiate it from Vps26B. As for Vps26A, biochemical characterization of Vps26B established that this novel 336 amino acid residue protein is a peripheral membrane protein. Vps26B co-precipitated with Vps35 from transfected cells and the direct interaction between these two proteins was confirmed by yeast 2-hybrid analysis, thereby establishing Vps26B as a subunit of the retromer complex. Within HeLa cells, Vps26B was found in the cytoplasm with low levels at the plasma membrane, while Vps26A was predominantly associated with endosomal membranes. Within A549 cells, both Vps26A and Vps26B co-localized with actin-rich lamellipodia at the cell surface. These structures also co-localized with Vps35. Total internal reflection fluorescence microscopy confirmed the association of Vps26B with the plasma membrane in a stable HEK293 cell line expressing cyan fluorescent protein (CFP)-Vps26B. Based on these observations, we propose that the mammalian retromer complex is located at both endosomes and the plasma membrane in some cell types.

In vitro and in silico analysis of signal peptides from the human blood fluke, Schistosoma mansoni

Proteins secreted by and anchored on the surfaces of parasites are in intimate contact with host tissues. The transcriptome of infective cercariae of the blood fluke, Schistosoma mansoni, was screened using signal sequence trap to isolate cDNAs encoding predicted proteins with an N-terminal signal peptide. Twenty cDNA fragments were identified, most of which contained predicted signal peptides or transmembrane regions, including a novel putative seven-transmembrane receptor and a membrane-associated mitogen-activated protein kinase. The developmental expression pattern within different life-cycle stages ranged from ubiquitous to a transcript that was highly upregulated in the cercaria. A bioinformatics-based comparison of 100 signal peptides from each of schistosomes, humans, a parasitic nematode and Escherichia coli showed that differences in the sequence composition of signal peptides, notably the residues flanking the predicted cleavage site, might account for the negative bias exhibited in the processing of schistosome signal peptides in mammalian cells. (c) 2005 Federation of European Microbiological Societies. Published by Elsevier B.V. All rights reserved.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

A new method for identification of protein (sub)families in a set of proteins based on hydropathy distribution in proteins

Structural similarity among proteins is reflected in the distribution of hydropathicity along the amino acids in the protein sequence. Similarities in the hydropathy distributions are obvious for homologous proteins within a protein family. They also were observed for proteins with related structures, even when sequence similarities were undetectable. Here we present a novel method that employs the hydropathy distribution in proteins for identification of (sub)families in a set of (homologous) proteins. We represent proteins as points in a generalized hydropathy space, represented by vectors of specifically defined features. The features are derived from hydropathy of the individual amino acids. Projection of this space onto principal axes reveals groups of proteins with related hydropathy distributions. The groups identified correspond well to families of structurally and functionally related proteins. We found that this method accurately identifies protein families in a set of proteins, or subfamilies in a set of homologous proteins. Our results show that protein families can be identified by the analysis of hydropathy distribution, without the need for sequence alignment. (C) 2005 Wiley-Liss, Inc.

Substrate specifity of protein kinases and computational prediction of substrates

To ensure signalling fidelity, kinases must act only on a defined subset of cellular targets. Appreciating the basis for this substrate specificity is essential for understanding the role of an individual protein kinase in a particular cellular process. The specificity in the cell is determined by a combination of peptide specificity of the kinase (the molecular recognition of the sequence surrounding the phosphorylation site), substrate recruitment and phosphatase activity. Peptide specificity plays a crucial role and depends on the complementarity between the kinase and the substrate and therefore on their three-dimensional structures. Methods for experimental identification of kinase substrates and characterization of specificity are expensive and laborious, therefore, computational approaches are being developed to reduce the amount of experimental work required in substrate identification. We discuss the structural basis of substrate specificity of protein kinases and review the experimental and computational methods used to obtain specificity information. (c) 2005 Elsevier B.V. All rights reserved.

CandiVF - Candida albicans virulence factor database

Candida albicans is a pathogen commonly infecting patients who receive immunosuppressive drug therapy, long-term catheterization, or those who suffer from acquired immune deficiency syndrome (AIDS). The major factor accountable for pathogenicity of C. albicans is host immune status. Various virulence molecules, or factors, of are also responsible for the disease progression. Virulence proteins are published in public databases but they normally lack detailed functional annotations. We have developed CandiVF, a specialized database of C. albicans virulence factors (http://antigen.i2r.a-star.edu.sg/Templar/DB/CandiVF/) to facilitate efficient extraction and analysis of data aimed to assist research on immune responses, pathogenesis, prevention, and control of candidiasis. CandiVF contains a large number of annotated virulence proteins, including secretory, cell wall-associated, membrane, cytoplasmic, and nuclear proteins. This database has in-built bioinformatics tools including keyword and BLAST search, visualization of 3D-structures, HLA-DR epitope prediction, virulence descriptors, and virulence factors ontology.

Information integration in molecular bioscience

Integrating information in the molecular biosciences involves more than the cross-referencing of sequences or structures. Experimental protocols, results of computational analyses, annotations and links to relevant literature form integral parts of this information, and impart meaning to sequence or structure. In this review, we examine some existing approaches to integrating information in the molecular biosciences. We consider not only technical issues concerning the integration of heterogeneous data sources and the corresponding semantic implications, but also the integration of analytical results. Within the broad range of strategies for integration of data and information, we distinguish between platforms and developments. We discuss two current platforms and six current developments, and identify what we believe to be their strengths and limitations. We identify key unsolved problems in integrating information in the molecular biosciences, and discuss possible strategies for addressing them including semantic integration using ontologies, XML as a data model, and graphical user interfaces as integrative environments.