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At least 217 records · Page 12Linked to original sources

Bioinformatics and receptor mechanisms of psychotropic drugs.

One important aspect in biotechnology is gene discovery and target validation for drug discovery. Information from the human genome (HUGO) project may be used to deduce the amino acid sequence of all proteins produced in the human body. However, knowing the amino acid sequence of a protein is not the same as knowing its function. Identification of novel molecular targets for discovery of new, safer and more efficient therapeutic drugs from the human genome sequences requires multidisciplinary research efforts, including proteomics, structural biology and bioinformatics. In addition to possible effects on gene expression, most of the currently used therapeutic drugs either have enzymes or membrane proteins as their molecular targets of action. These membrane proteins include transporters of small molecules across cell membranes, ion channels, or receptors that convey signals from one side of a membrane to the other. Our research group as well as others have used computational techniques, along with biotechnology, molecular biology and other experimental techniques, to construct detailed 3-dimensional models of transporter proteins and G-protein coupled receptors (GPCRs), which are the molecular targets of action of psychotropic drugs. The models have been used to simulate the molecular dynamics and study the ligand binding and signal transduction mechanisms of these receptors. The use of bioinformatics, as exemplified in our modelling of GPCRs, is only one of the key factors for success in post-genomic research for new targets for therapeutic drugs.

Animals↗

Protein sequence analysis in silico: application of structure-based bioinformatics to genomic initiatives.

The current pace of high-throughput genome sequencing programs coupled with high-throughput functional genomic screens has provided researchers with a bewildering array of sequence and biological data to contend with. Identification of proteins of interest from a particular biological study requires the application of bioinformatic tools to process and prioritise the data. From a protein function standpoint, transfer of annotation from known proteins to a novel target is currently the only practical way to convert vast quantities of raw sequence data into meaningful information. New bioinformatics tools now provide more sophisticated methods to transfer functional annotation, integrating sequence, family profile and structural search methodology. The importance of these approaches to medical research is increasing as we move to annotate the proteome through functional and structural genomic efforts.

Animals↗

Bioinformatics, functional genomics, and proteomics study of Bacillus sp.

The ability of bioinformatics to characterize genomic and proteomic sequences from bacteria Bacillus sp. for prediction of genes and proteins has been evaluated. Genomics coupling with proteomics, which is relied on integration of the significant advances recently achieved in two-dimensional (2-D) electrophoretic separation of proteins and mass spectrometry (MS), are now important and high throughput techniques for qualifying and analyzing gene and protein expression, discovering new gene or protein products, and understanding of gene and protein functions including post-genomic study. In addition, the bioinformatics of Bacillus sp. is embraced into many databases that will facilitate to rapidly search the information of Bacillus sp. in both genomics and proteomics. It is also possible to highlight sites for post-translational modifications based on the specific protein sequence motifs that play important roles in the structure, activity and compartmentalization of proteins. Moreover, the secreted proteins from Bacillus sp. are interesting and widely used in many applications especially biomedical applications that are the highly advantages for their potential therapeutic values.

Bacillus↗

Bioinformatics data distribution and integration via Web Services and XML.

It is widely recognized that exchange, distribution, and integration of biological data are the keys to improve bioinformatics and genome biology in post-genomic era. However, the problem of exchanging and integrating biology data is not solved satisfactorily. The eXtensible Markup Language (XML) is rapidly spreading as an emerging standard for structuring documents to exchange and integrate data on the World Wide Web (WWW). Web service is the next generation of WWW and is founded upon the open standards of W3C (World Wide Web Consortium) and IETF (Internet Engineering Task Force). This paper presents XML and Web Services technologies and their use for an appropriate solution to the problem of bioinformatics data exchange and integration.

Computational Biology↗

Predicting the nuclear localization signals of 107 types of HPV L1 proteins by bioinformatic analysis.

In this study, 107 types of human papillomavirus (HPV) L1 protein sequences were obtained from available databases, and the nuclear localization signals (NLSs) of these HPV L1 proteins were analyzed and predicted by bioinformatic analysis. Out of the 107 types, the NLSs of 39 types were predicted by PredictNLS software (35 types of bipartite NLSs and 4 types of monopartite NLSs). The NLSs of the remaining HPV types were predicted according to the characteristics and the homology of the already predicted NLSs as well as the general rule of NLSs. According to the result, the NLSs of 107 types of HPV L1 proteins were classified into 15 categories. The different types of HPV L1 proteins in the same NLS category could share the similar or the same nucleocytoplasmic transport pathway. They might be used as the same target to prevent and treat different types of HPV infection. The results also showed that bioinformatic technology could be used to analyze and predict NLSs of proteins.

Amino Acid Sequence↗

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity: in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique.

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide-MHC binding affinity. The ISC-PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide-MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms--q2, SEP, and NC--ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).

Animals↗

Gene expression profiling in osteoblast biology: bioinformatic tools.

This review focuses on using microarray data on a clonal osteoblast cell model to demonstrate how various current and future bioinformatic tools can be used to understand, at a more global or comprehensible level, how cells grow and differentiate. In this example, BMP2 was used to stimulate growth and differentiation of osteoblast to a mineralized matrix. A discussion is included on various methods for clustering gene expression data, statistical evaluation of data, and various new tools that can be used to derive deeper insight into a particular biological problem. How these tools can be obtained is also discussed. New tools for the biologists to compare their datasets with others, as well as examples of future bioinformatic tools that can be used for developing gene networks and pathways for a given set of data are included and discussed.

Animals↗

Putative lipoproteins of Streptococcus agalactiae identified by bioinformatic genome analysis.

Streptococcus agalactiae is a significant pathogen causing invasive disease in neonates and thus an understanding of the molecular basis of the pathogenicity of this organism is of importance. N-terminal lipidation is a major mechanism by which bacteria can tether proteins to membranes. Lipidation is directed by the presence of a cysteine-containing 'lipobox' within specific signal peptides and this feature has greatly facilitated the bioinformatic identification of putative lipoproteins. We have designed previously a taxon-specific pattern (G+LPP) for the identification of Gram-positive bacterial lipoproteins, based on the signal peptides of experimentally verified lipoproteins (Sutcliffe I.C. and Harrington D.J. Microbiology 148: 2065-2077). Patterns searches with this pattern and other bioinformatic methods have been used to identify putative lipoproteins in the recently published genomes of S. agalactiae strains 2603/V and NEM316. A core of 39 common putative lipoproteins was identified, along with 5 putative lipoproteins unique to strain 2603/V and 2 putative lipoproteins unique to strain NEM316. Thus putative lipoproteins represent ca. 2% of the S. agalactiae proteome. As in other Gram-positive bacteria, the largest functional category of S. agalactiae lipoproteins is that predicted to comprise of substrate binding proteins of ABC transport systems. Other roles include lipoproteins that appear to participate in adhesion (including the previously characterised Lmb protein), protein export and folding, enzymes and several species-specific proteins of unknown function. These data suggest lipoproteins may have significant roles that influence the virulence of this important pathogen.

Amino Acid Sequence↗

Bioinformatic analysis of autism positional candidate genes using biological databases and computational gene network prediction.

Common genetic disorders are believed to arise from the combined effects of multiple inherited genetic variants acting in concert with environmental factors, such that any given DNA sequence variant may have only a marginal effect on disease outcome. As a consequence, the correlation between disease status and any given DNA marker allele in a genomewide linkage study tends to be relatively weak and the implicated regions typically encompass hundreds of positional candidate genes. Therefore, new strategies are needed to parse relatively large sets of 'positional' candidate genes in search of actual disease-related gene variants. Here we use biological databases to identify 383 positional candidate genes predicted by genomewide genetic linkage analysis of a large set of families, each with two or more members diagnosed with autism, or autism spectrum disorder (ASD). Next, we seek to identify a subset of biologically meaningful, high priority candidates. The strategy is to select autism candidate genes based on prior genetic evidence from the allelic association literature to query the known transcripts within the 1-LOD (logarithm of the odds) support interval for each region. We use recently developed bioinformatic programs that automatically search the biological literature to predict pathways of interacting genes (PATHWAYASSIST and GENEWAYS). To identify gene regulatory networks, we search for coexpression between candidate genes and positional candidates. The studies are intended both to inform studies of autism, and to illustrate and explore the increasing potential of bioinformatic approaches as a compliment to linkage analysis.

Autistic Disorder↗

The origins of bioinformatics.

Bioinformatics is often described as being in its infancy, but computers emerged as important tools in molecular biology during the early 1960s. A decade before DNA sequencing became feasible, computational biologists focused on the rapidly accumulating data from protein biochemistry. Without the benefits of super computers or computer networks, these scientists laid important conceptual and technical foundations for bioinformatics today.

Computational Biology↗

A rapid bioinformatic method identifies novel genes with direct clinical relevance to colon cancer.

Identifying genes whose differential expression affect the survival of patients after primary tumor surgery is a major aim of clinical cancer research. To address this issue we combined rapid bioinformatic search algorithms with quantitative RT-PCR in a panel of clearly defined cases of colorectal carcinomas with detailed patient histories. Search algorithms were written that identified Expressed Sequence Tags (ESTs) from the Unigene EST collection of putative open reading frames (ORFs). Expression ratios of healthy to cancerous tissue of each Unigene ORF were calculated. The first 35 candidates arising from bioinformatic searches were examined for mRNA expression in a panel of 20 well documented cases of colon cancer. Four of these 35 genes showed significant correlations with histopathological parameters. Therefore, their expression was further analysed by quantitative RT-PCR in a larger patient cohort. Kaplan-Meier/log rank statistical tests of up to 49 patients in three of the four genes demonstrated significant association of gene expression with poor survival. All four genes demonstrated a strong association with metastatic tumor progression. Expression of the genes was localized to epithelial cells by in-situ hybridization.

Algorithms↗

Bioinformatic and experimental tools for identification of single-nucleotide polymorphisms in genes with a potential role for the development of the insulin resistance syndrome.

OBJECTIVES: Genes with a possible role for the development of the insulin resistance syndrome (IRS) were scanned for novel single-nucleotide polymorphisms (SNPs) using bioinformatics. METHODS: GenBank mRNA sequences were compared to the human EST database using gapped BLAST, software that is available on the internet. Mismatches between the search and the EST sequences indicated potential SNPs. Thirty-two SNPs in 13 genes were randomly chosen for experimental verification. PCR and direct sequencing were used to determine the 'true' SNPs. A random sample of 30 Swedish men with slightly elevated diastolic blood pressure (85-94 mmHg) obtained from a population-based study was selected for the sequencing. After completion of these stages, the potential SNPs were checked against the large and rapidly expanding SNP databases HGBASE and NCBI. RESULTS: EST searches of 146 genes revealed 106 potential SNPs in 44 genes. Experimental analysis of 32 of these potential SNPs verified two SNPs; endothelin receptor A 1471 G/C (3' UTR) and PAI-1 Trp514Arg from a T/C exchange. These two SNPs were also identified in the NCBI and HGBASE databases together with two polymorphisms that were not experimentally identified in our homogeneous Swedish population. Overall, the HGBASE and NCBI databases contained entries of 22% (23 out of 106) of the SNPs identified through our EST searches. CONCLUSIONS: In the search for genetic variations causing complex diseases like IRS in homogeneous populations (such as the Swedish one used here), important information can be obtained through bioinformatic searches of human genome databases and experimental verification.

Adult↗

Integrating structure, bioinformatics, and enzymology to discover function: BioH, a new carboxylesterase from Escherichia coli.

Structural proteomics projects are generating three-dimensional structures of novel, uncharacterized proteins at an increasing rate. However, structure alone is often insufficient to deduce the specific biochemical function of a protein. Here we determined the function for a protein using a strategy that integrates structural and bioinformatics data with parallel experimental screening for enzymatic activity. BioH is involved in biotin biosynthesis in Escherichia coli and had no previously known biochemical function. The crystal structure of BioH was determined at 1.7 A resolution. An automated procedure was used to compare the structure of BioH with structural templates from a variety of different enzyme active sites. This screen identified a catalytic triad (Ser82, His235, and Asp207) with a configuration similar to that of the catalytic triad of hydrolases. Analysis of BioH with a panel of hydrolase assays revealed a carboxylesterase activity with a preference for short acyl chain substrates. The combined use of structural bioinformatics with experimental screens for detecting enzyme activity could greatly enhance the rate at which function is determined from structure.

Biotin↗

Structural e-bioinformatics and drug design.

Nowadays the in silico scenario for drug design is totally dependent on structural biology and structural bioinformatics. A myriad of free bioinformatics applications and services have been posted on the web. This mini-review mentions web sites that are useful in structure-based drug design. The information is given in a logical manner, following the drug design process i.e. characterization of a protein target, modelling the protein using sequence homology, optimization of the protein structure and finally docking of small ligands into the active site.

Amino Acid Sequence↗

Bioinformatics for venom and toxin sciences.

Venomous animals produce a myriad of important pharmacological components. The individual components, or venoms (toxins), are used in ion channel and receptor studies, drug discovery, and formulation of insecticides. The toxin data are scattered across public databases which provide sequence and structural descriptions, but very limited functional annotation. The exponential growth of newly identified toxin data has created a need for better data management. Venominformatics is a systematic bioinformatics approach in which classified, consolidated and cleaned venom data are stored into repositories and integrated with advanced bioinformatics tools for the analysis of structure and function of toxins. Venominformatics complements experimental studies and helps reduce the number of essential experiments.

Animals↗

Public services from the European Bioinformatics Institute.

The European Bioinformatics Institute (EBI) provides numerous free-of-charge, publicly available bioinformatics services that can be divided into the following categories: ftp downloads; data submissions processing and biological database production; access to query; analysis and retrieval systems and tools; user support; training and education and industry support through EBI's SME program. These services are all available at the website. It is imperative that EBI's data as well as the tools to analyse it efficiently are made available in a free and unambiguous way to the scientific community. An important part of the EBI's mission is to make this happen in a fast, reliable and efficient manner. This paper serves as a brief introduction to each of these services.

Computational Biology↗

Bioinformatics for glycomics: status, methods, requirements and perspectives.

The term 'glycomics' describes the scientific attempt to identify and study all the glycan molecules - the glycome - synthesised by an organism. The aim is to create a cell-by-cell catalogue of glycosyltransferase expression and detected glycan structures. The current status of databases and bioinformatics tools, which are still in their infancy, is reviewed. The structures of glycans as secondary gene products cannot be easily predicted from the DNA sequence. Glycan sequences cannot be described by a simple linear one-letter code as each pair of monosaccharides can be linked in several ways and branched structures can be formed. Few of the bioinformatics algorithms developed for genomics/proteomics can be directly adapted for glycomics. The development of algorithms, which allow a rapid, automatic interpretation of mass spectra to identify glycan structures is currently the most active field of research. The lack of generally accepted ways to normalise glycan structures and exchange glycan formats hampers an efficient cross-linking and the automatic exchange of distributed data. The upcoming glycomics should accept that unrestricted dissemination of scientific data accelerates scientific findings and initiates a number of new initiatives to explore the data.

Animals↗

Bioinformatics software resources.

This review looks at internet archives, repositories and lists for obtaining popular and useful biology and bioinformatics software. Resources include collections of free software, services for the collaborative development of new programs, software news media and catalogues of links to bioinformatics software and web tools. Problems with such resources arise from needs for continued curator effort to collect and update these, combined with less than optimal community support, funding and collaboration. Despite some problems, the available software repositories provide needed public access to many tools that are a foundation for analyses in bioscience research efforts.

Algorithms↗