Biomedical subjects

Deendayal Dinakarpandian

Publications and source records attributed to Deendayal Dinakarpandian.

4 recordsLinked to original sources

PubMed2005

MachineProse: an ontological framework for scientific assertions.

OBJECTIVE: The idea of testing a hypothesis is central to the practice of biomedical research. However, the results of testing a hypothesis are published mainly in the form of prose articles. Encoding the results as scientific assertions that are both human and machine readable would greatly enhance the synergistic growth and dissemination of knowledge. DESIGN: We have developed MachineProse (MP), an ontological framework for the concise specification of scientific assertions. MP is based on the idea of an assertion constituting a fundamental unit of knowledge. This is in contrast to current approaches that use discrete concept terms from domain ontologies for annotation and assertions are only inferred heuristically. MEASUREMENTS: We use illustrative examples to highlight the advantages of MP over the use of the Medical Subject Headings (MeSH) system and keywords in indexing scientific articles. RESULTS: We show how MP makes it possible to carry out semantic annotation of publications that is machine readable and allows for precise search capabilities. In addition, when used by itself, MP serves as a knowledge repository for emerging discoveries. A prototype for proof of concept has been developed that demonstrates the feasibility and novel benefits of MP. As part of the MP framework, we have created an ontology of relationship types with about 100 terms optimized for the representation of scientific assertions. CONCLUSION: MachineProse is a novel semantic framework that we believe may be used to summarize research findings, annotate biomedical publications, and support sophisticated searches.

Artificial Intelligence↗

PubMed2005

Tandem machine learning for the identification of genes regulated by transcription factors.

BACKGROUND: The identification of promoter regions that are regulated by a given transcription factor has traditionally relied upon the identification and distributions of binding sites recognized by the factor. In this study, we have developed a tandem machine learning approach for the identification of regulatory target genes based on these parameters and on the corresponding binding site information contents that measure the affinities of the factor for these cognate elements. RESULTS: This method has been validated using models of DNA binding sites recognized by the xenobiotic-sensitive nuclear receptor, PXR/RXRalpha, for target genes within the human genome. An information theory-based weight matrix was first derived and refined from known PXR/RXRalpha binding sites. The promoter region of candidate genes was scanned with the weight matrix. A novel information density-based clustering algorithm was then used to identify clusters of information rich sites. Finally, transformed data representing metrics of location, strength and clustering of binding sites were used for classification of promoter regions using an ensemble approach involving neural networks, decision trees and Naïve Bayesian classification. The method was evaluated on a set of 24 known target genes and 288 genes known not to be regulated by PXR/RXRalpha. We report an average accuracy (proportion of correctly classified promoter regions) of 71%, sensitivity of 73%, and specificity of 70%, based on multiple cross-validation and the leave-one-out strategy. The performance on a test set of 13 genes showed that 10 were correctly classified. CONCLUSION: We have developed a machine learning approach for the successful detection of gene targets for transcription factors with high accuracy. The method has been validated for the transcription factor PXR/RXRalpha and has the potential to be extended to other transcription factors.

Algorithms↗

PubMed2004

An informatics search for the low-molecular weight chromium-binding peptide.

BACKGROUND: The amino acid composition of a low molecular weight chromium binding peptide (LMWCr), isolated from bovine liver, is reportedly E:G:C:D::4:2:2:2, though its sequence has not been discovered. There is some controversy surrounding the exact biochemical forms and the action of Cr(III) in biological systems; the topic has been the subject of many experimental reports and continues to be investigated. Clarification of Cr-protein interactions will further understanding Cr(III) biochemistry and provide a basis for novel therapies based on metallocomplexes or small molecules. RESULTS: A genomic search of the non-redundant database for all possible decapeptides of the reported composition yields three exact matches, EDGEECDCGE, DGEECDCGEE and CEGGCEEDDE. The first two sequences are found in ADAM 19 (A Disintegrin and Metalloproteinase domain 19) proteins in man and mouse; the last is found in a protein kinase in rice (Oryza sativa). A broader search for pentameric sequences (and assuming a disulfide dimer) corresponding to the stoichiometric ratio E:D:G:C::2:1:1:1, within the set of human proteins and the set of proteins in, or related to, the insulin signaling pathway, yields a match at an acidic region in the alpha-subunit of the insulin receptor (-EECGD-, residues 175-184). A synthetic peptide derived from this sequence binds chromium(III) and forms a metal-peptide complex that has properties matching those reported for isolated LMWCr and Cr(III)-containing peptide fractions. CONCLUSION: The search for an acidic decameric sequence indicates that LMWCr may not be a contiguous sequence. The identification of a distinct pentameric sequence in a significant insulin-signaling pathway protein suggests a possible identity for the LMWCr peptide. This identification clarifies directions for further investigation of LMWCr peptide fractions, chromium bio-coordination chemistry and a possible role in the insulin signaling pathway. Implications for models of chromium action in the insulin-signaling pathway are discussed.

Journal Article↗

PubMed2004

Collagenase unwinds triple-helical collagen prior to peptide bond hydrolysis.

Breakdown of triple-helical interstitial collagens is essential in embryonic development, organ morphogenesis and tissue remodelling and repair. Aberrant collagenolysis may result in diseases such as arthritis, cancer, atherosclerosis, aneurysm and fibrosis. In vertebrates, it is initiated by collagenases belonging to the matrix metalloproteinase (MMP) family. The three-dimensional structure of a prototypic collagenase, MMP-1, indicates that the substrate-binding site of the enzyme is too narrow to accommodate triple-helical collagen. Here we report that collagenases bind and locally unwind the triple-helical structure before hydrolyzing the peptide bonds. Mutation of the catalytically essential residue Glu200 of MMP-1 to Ala resulted in a catalytically inactive enzyme, but in its presence noncollagenolytic proteinases digested collagen into typical 3/4 and 1/4 fragments, indicating that the MMP-1(E200A) mutant unwinds the triple-helical collagen. The study also shows that MMP-1 preferentially interacts with the alpha2(I) chain of type I collagen and cleaves the three alpha chains in succession. Our results throw light on the basic mechanisms that control a wide range of biological and pathological processes associated with tissue remodelling.

Amino Acid Sequence↗