Biomedical subjects

C P Elkan

Publications and source records attributed to C P Elkan.

6 recordsLinked to original sources

PubMed1999

A common ancestor for a subunit in the mitochondrial proton-translocating NADH:ubiquinone oxidoreductase (complex I) and short-chain dehydrogenases/reductases.

The proton-translocating NADH:ubiquinone oxidoreductase or complex I is located in the inner membranes of mitochondria, where it catalyzes the transfer of electrons from NADH to ubiquinone. Here we report that one of the subunits in complex I is homologous to short-chain dehydrogenases and reductases, a family of enzymes with diverse activities that include metabolizing steroids, prostaglandins and nucleotide sugars. We discovered that a subunit of complex I in human, cow, Neurospora crassa and Aquifex aeolius is homologous to nucleotide-sugar epimerases and hydroxysteroid dehydrogenases while seeking distant homologs of these enzymes with a hidden Markov model-based search of Genpept. This homology allows us to use information from the solved three-dimensional structures of nucleotide-sugar epimerases and hydroxysteroid dehydrogenases and our motif analysis of these enzymes to predict functional domains on their homologs in complex I.

Amino Acid Sequence↗

PubMed1998

Spinach CSP41, an mRNA-binding protein and ribonuclease, is homologous to nucleotide-sugar epimerases and hydroxysteroid dehydrogenases.

Spinach CSP41 is part of a protein complex that binds to the 3' untranslated region (UTR) of petD precursor-mRNA, a chloroplast gene encoding subunit IV of the cytochrome b6/f complex. CSP41 cleaves the 3'-UTR of petD mRNA within the stem-loop structure, suggesting a key role in the control of chloroplast mRNA stability. We discovered that CSP41 is homologous to nucleotide-sugar epimerases and hydroxysteroid dehydrogenases while seeking distant homologs of these enzymes with a hidden Markov model-based search of Genpept. This analysis identified Synechocystis ORF, Accession 1652543 as a homolog. Subsequent analyses show that spinach CSP41 and Arabidopsis thaliana 2765081 are homologous to the Synechocystis ORF. Information from the solved 3D structures of epimerases and dehydrogenases and our motif analysis of these enzymes is used to predict domains on CSP41 that are important in binding and metabolism of mRNA. Cyanobacteria are among the earliest life forms, indicating that the divergence from a common ancestor of nucleotide-sugar epimerases and an mRNA binding protein with ribonuclease activity was ancient.

Amino Acid Sequence↗

PubMed1997

Hidden Markov model analysis of motifs in steroid dehydrogenases and their homologs.

The increasing size of protein sequence databases is straining methods of sequence analysis, even as the increased information offers opportunities for sophisticated analyses of protein structure, function, and evolution. Here we describe a method that uses artificial intelligence-based algorithms to build models of families of protein sequences. These models can be used to search protein sequence databases for remote homologs. The MEME (Multiple Expectation-maximization for Motif Elicitation) software package identifies motif patterns in a protein family, and these motifs are combined into a hidden Markvov model (HMM) for use as a database searching tool. Meta-MEME is sensitive and accurate, as well as automated and unbiased, making it suitable for the analysis of large datasets. We demonstrate Meta-MEME on a family of dehydrogenases that includes mammalian 11 beta-hydroxysteroid and 17 beta-hydroxysteroid dehydrogenase and their homologs in the short chain alcohol dehydrogenase family. We chose this dataset because it is large and phylogenetically diverse, providing a good test of the sensitivity and selectivity of Meta-MEME on a protein family of biological interest. Indeed, Meta-MEME identifies at least 350 members of this family in Genpept96 and clearly separates these sequences from non-homologous proteins. We also show how the MEME motif output can be used for phylogenetic analysis.

Algorithms↗

PubMed1997

An artificial intelligence approach to motif discovery in protein sequences: application to steriod dehydrogenases.

MEME (Multiple Expectation-maximization for Motif Elicitation) is a unique new software tool that uses artificial intelligence techniques to discover motifs shared by a set of protein sequences in a fully automated manner. This paper is the first detailed study of the use of MEME to analyse a large, biologically relevant set of sequences, and to evaluate the sensitivity and accuracy of MEME in identifying structurally important motifs. For this purpose, we chose the short-chain alcohol dehydrogenase superfamily because it is large and phylogenetically diverse, providing a test of how well MEME can work on sequences with low amino acid similarity. Moreover, this dataset contains enzymes of biological importance, and because several enzymes have known X-ray crystallographic structures, we can test the usefulness of MEME for structural analysis. The first six motifs from MEME map onto structurally important alpha-helices and beta-strands on Streptomyces hydrogenans 20beta-hydroxysteroid dehydrogenase. We also describe MAST (Motif Alignment Search Tool), which conveniently uses output from MEME for searching databases such as SWISS-PROT and Genpept. MAST provides statistical measures that permit a rigorous evaluation of the significance of database searches with individual motifs or groups of motifs. A database search of Genpept90 by MAST with the log-odds matrix of the first six motifs obtained from MEME yields a bimodal output, demonstrating the selectivity of MAST. We show for the first time, using primary sequence analysis, that bacterial sugar epimerases are homologs of short-chain dehydrogenases. MEME and MAST will be increasingly useful as genome sequencing provides large datasets of phylogenetically divergent sequences of biomedical interest.

Alcohol Dehydrogenase↗

PubMed1997

Meta-MEME: motif-based hidden Markov models of protein families.

MOTIVATION: Modeling families of related biological sequences using Hidden Markov models (HMMs), although increasingly widespread, faces at least one major problem: because of the complexity of these mathematical models, they require a relatively large training set in order to accurately recognize a given family. For families in which there are few known sequences, a standard linear HMM contains too many parameters to be trained adequately. RESULTS: This work attempts to solve that problem by generating smaller HMMs which precisely model only the conserved regions of the family. These HMMs are constructed from motif models generated by the EM algorithm using the MEME software. Because motif-based HMMs have relatively few parameters, they can be trained using smaller data sets. Studies of short chain alcohol dehydrogenases and 4Fe-4S ferredoxins support the claim that motif-based HMMs exhibit increased sensitivity and selectivity in database searches, especially when training sets contain few sequences.

Alcohol Dehydrogenase↗

PubMed1996

ParaMEME: a parallel implementation and a web interface for a DNA and protein motif discovery tool.

Many advanced software tools fail to reach a wide audience because they require specialized hardware, installation expertise, or an abundance of CPU cycles. The worldwide web offers a new opportunity for distributing such systems. One such program, MEME, discovers repeated patterns, called motifs, in sets of DNA or protein sequences. This tool is now available to biologists over the worldwide web, using an asynchronous, single-program multiple-data version of the program called ParaMEME that runs on an Intel Paragon XP/S parallel computer at the San Diego Super-computer Center. ParaMEME scales gracefully to 64 nodes on the Paragon with efficiencies > 72% for large data sets. The worldwide web interface to ParaMEME accepts a set of sequences interactively from a user, submits the sequences to the Paragon for analysis, and e-mails the results back to the user. ParaMEME is available for free public use at http://@www.sdsc.edu/CompSci/Biomed/ MEME.

Algorithms↗