The physical mapping of bacteriophage T5 transfer tRNAs.

Transfer RNAs, isolated from Escherichia coli F cells infected with T5 bacteriophage, were charged with radioactive amino acids and used in RNA-DNA hybridization studies to detect and locate T5 tRNA cistrons in the T5 DNA chromosome. Hybridization of 14 3H-aminoacyl-tRNA species, including purified T5 [35S]Met-tRNAm and [35S]Met-tRNAf, to the separated strands of T5+ DNA indicates that most, if not all, of the T5 tRNAs are transcribed from the continuous heavy strand of T5 DNA. Heteroduplex mapping of eight mutant T5 DNA deletions has enabled us to locate and determine the size of these deleted segments. By correlating this information with the presence and absence of specific tDNA sequences in these mutants, as determined by tRNA-DNA hybridization, we were able to define the physical limits of four tDNA-containing loci along the T5 DNA molecule. A physical map for 15 tRNA species examined indicates that the structural genes for these tRNAs are clustered within a segment length of T5 DNA that represents approximately 11.2% of the total wild type T5 DNA. The existence of the deletion mutants indicates that T5 tRNAs are dispensable for T5 replication under the growth conditions and for the host employed.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.