Biomedical subjects

Narmada Thanki

Publications and source records attributed to Narmada Thanki.

9 recordsLinked to original sources

PubMed2006

CDD: a conserved domain database for interactive domain family analysis.

The conserved domain database (CDD) is part of NCBI's Entrez database system and serves as a primary resource for the annotation of conserved domain footprints on protein sequences in Entrez. Entrez's global query interface can be accessed at http://www.ncbi.nlm.nih.gov/Entrez and will search CDD and many other databases. Domain annotation for proteins in Entrez has been pre-computed and is readily available in the form of 'Conserved Domain' links. Novel protein sequences can be scanned against CDD using the CD-Search service; this service searches databases of CDD-derived profile models with protein sequence queries using BLAST heuristics, at http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi. Protein query sequences submitted to NCBI's protein BLAST search service are scanned for conserved domain signatures by default. The CDD collection contains models imported from Pfam, SMART and COG, as well as domain models curated at NCBI. NCBI curated models are organized into hierarchies of domains related by common descent. Here we report on the status of the curation effort and present a novel helper application, CDTree, which enables users of the CDD resource to examine curated hierarchies. More importantly, CDD and CDTree used in concert, serve as a powerful tool in protein classification, as they allow users to analyze protein sequences in the context of domain family hierarchies.

Amino Acid Sequence↗

PubMed2005

Linking tumor cell cytotoxicity to mechanism of drug action: an integrated analysis of gene expression, small-molecule screening and structural databases.

An integrated, bioinformatic analysis of three databases comprising tumor-cell-based small molecule screening data, gene expression measurements, and PDB (Protein Data Bank) ligand-target structures has been developed for probing mechanism of drug action (MOA). Clustering analysis of GI50 profiles for the NCI's database of compounds screened across a panel of tumor cells (NCI60) was used to select a subset of unique cytotoxic responses for about 4000 small molecules. Drug-gene-PDB relationships for this test set were examined by correlative analysis of cytotoxic response and differential gene expression profiles within the NCI60 and structural comparisons with known ligand-target crystallographic complexes. A survey of molecular features within these compounds finds thirteen conserved Compound Classes, each class exhibiting chemical features important for interactions with a variety of biological targets. Protein targets for an additional twelve Compound Classes could be directly assigned using drug-protein interactions observed in the crystallographic database. Results from the analysis of constitutive gene expressions established a clear connection between chemo-resistance and overexpression of gene families associated with the extracellular matrix, cytoskeletal organization, and xenobiotic metabolism. Conversely, chemo-sensitivity implicated overexpression of gene families involved in homeostatic functions of nucleic acid repair, aryl hydrocarbon metabolism, heat shock response, proteasome degradation and apoptosis. Correlations between chemo-responsiveness and differential gene expressions identified chemotypes with nonselective (i.e., many) molecular targets from those likely to have selective (i.e., few) molecular targets. Applications of data mining strategies that jointly utilize tumor cell screening, genomic, and structural data are presented for hypotheses generation and identifying novel anticancer candidates.

Antineoplastic Agents↗

PubMed2003

Molecular classification of cancer: unsupervised self-organizing map analysis of gene expression microarray data.

An unsupervised self-organizing map-based clustering strategy has been developed to classify tissue samples from an oligonucleotide microarray patient database. Our method is based on the likelihood that a test data vector may have a gene expression fingerprint that is shared by more than one tumor class and as such can identify datasets that cannot be unequivocally assigned to a single tumor class. Our self-organizing map analysis completely separated the tumor from the normal expression datasets. Within the 14 different tumor types, classification accuracies on the order of approximately 80% correct were achieved. Nearly perfect classifications were found for leukemia, central nervous system, melanoma, uterine, and lymphoma tumor types, with very poor classifications found for colorectal, ovarian, breast, and lung tumors. Classification results were further analyzed to identify sets of differentially expressed genes between tumor and normal gene expressions and among each tumor class. Within the total pool of 1139 genes most differentially expressed in this dataset, subsets were found that could be vetted according to previously published literature sources to be specific tumor markers. Attempts to classify gene expression datasets from other sources found a wide range of classification accuracies. Discussions about the utility of this method and the quality of data needed for accurate tumor classifications are provided.

Algorithms↗

PubMed2003

Mining the NCI screening database: explorations of agents involved in cell cycle regulation.

We propose an integrated application of technologies, computation and statistical methods to design experiments for examination of cellular pathways that are necessary for cell survival and that are candidates for cancer therapy. Our design combines information derived from two very different data sets: tumor screening data from over 36,000 synthetic compounds screened against over 60 tumor cell lines, and replicate microarray gene expression measurements using one cell line and one compound. Data filtering, based on restricted cellular cytotoxicity profiles from chemically similar sets of compounds, has been used to select a class of benzothiazoles for subsequent microarray gene expression measurements in the most chemosensitive tumor cell line. The results confirmed observations that P450 metabolizing isoforms, CYP1A1 and CYP1B1, are overexpressed in MCF-7 tumor cells following treatment with benzothiazole. These results are consistent with the proposed inactivity of the CYP1A1-mediated metabolism of benzothiazole and the antitumor activity of the metabolically resistant halogenated forms.

Animals↗

PubMed2002

Crystal structure of the YjeE protein from Haemophilus influenzae: a putative Atpase involved in cell wall synthesis.

A hypothetical protein encoded by the gene YjeE of Haemophilus influenzae was selected as part of a structural genomics project for X-ray analysis to assist with the functional assignment. The protein is considered essential to bacteria because the gene is present in virtually all bacterial genomes but not in those of archaea or eukaryotes. The amino acid sequence shows no homology to other proteins except for the presence of the Walker A motif G-X-X-X-X-G-K-T that indicates the possibility of a nucleotide-binding protein. The YjeE protein was cloned, expressed, and the crystal structure determined by the MAD method at 1.7-A resolution. The protein has a nucleotide-binding fold with a four-stranded parallel beta-sheet flanked by antiparallel beta-strands on each side. The topology of the beta-sheet is unique among P-loop proteins and has features of different families of enzymes. Crystallization of YjeE in the presence of ATP and Mg2+ resulted in the structure with ADP bound in the P-loop. The ATPase activity of YjeE was confirmed by kinetic measurements. The distribution of conserved residues suggests that the protein may work as a "molecular switch" triggered by ATP hydrolysis. The phylogenetic pattern of YjeE suggests its involvement in cell wall biosynthesis.

Adenosine Triphosphatases↗

PubMed2002

The Protein Data Bank.

The Protein Data Bank [PDB; Berman, Westbrook et al. (2000), Nucleic Acids Res. 28, 235-242; http://www.pdb.org/] is the single worldwide archive of primary structural data of biological macromolecules. Many secondary sources of information are derived from PDB data. It is the starting point for studies in structural bioinformatics. This article describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource. The reader should come away with an understanding of the scope of the PDB and what is provided by the resource.

Databases, Protein↗

PubMed2002

The Protein Data Bank: unifying the archive.

The Protein Data Bank (PDB; http://www.pdb.org/) is the single worldwide archive of structural data of biological macromolecules. This paper describes the progress that has been made in validating all data in the PDB archive and in releasing a uniform archive for the community. We have now produced a collection of mmCIF data files for the PDB archive (ftp://beta.rcsb.org/pub/pdb/uniformity/data/mmCIF/). A utility application that converts the mmCIF data files to the PDB format (called CIFTr) has also been released to provide support for existing software.

Amino Acid Sequence↗

PubMed2002

Assisting functional assignment for hypothetical Heamophilus influenzae gene products through structural genomics.

The three-dimensional structures of Haemophilus influenzae proteins whose biological functions are unknown are being determined as part of a structural genomics project to ask whether structural information can assist in assigning the functions of proteins. The structures of the hypothetical proteins are being used to guide further studies and narrow the field of such studies for ultimately determining protein function. An outline of the structural genomics methodological approach is provided along with summaries of a number of completed and in progress crystallographic and NMR structure determinations. With more than twenty-five structures determined at this point and with many more in various stages of completion, the results are encouraging in that some level of functional understanding can be deduced from experimentally solved structures. In addition to aiding in functional assignment, this effort is identifying a number of possible new targets for drug development.

Genome, Viral↗

PubMedDate not supplied

Evaluating chemical structure similarity as an indicator of cellular growth inhibition.

Chemical variations of small compounds are commonly used to probe biological systems and potentially discover lead-like compounds with selective target activity. Molecular probes are either generated by synthesis or acquired through directed searches of commercially available compound libraries. The data generated when testing the probes in various biological systems constitutes a structure/activity analysis. The ability to detect variations and classify biological responses requires the analysis of a compound in multiple assays. While the concept of a structure/activity relationship is straightforward, its implementation can vary considerably depending on the biological system under study and the probe library selected for testing. The analysis presented here will focus on the accumulated compound library used to screen for growth inhibition across the National Cancer Institute's panel of 60 tumor cells. The considerable chemical and biological diversity inherent in these data offers an opportunity to establish a quantifiable connection between chemical structure and biological activity. We find that the connection between structure and biological response is not symmetric, with biological response better at predicting chemical structure than vice versa. Structurally and functionally similar compounds can have distinguishable biological responses reflecting different mechanisms of action.

Antineoplastic Agents↗