Biomedical subjects

Ling-Ling Chen

Publications and source records attributed to Ling-Ling Chen.

23 records · Page 2Linked to original sources

PubMed2003

Specificity for inhibitors of metal-substituted methionine aminopeptidase.

Methionine aminopeptidases (MetAPs) have been studied in vitro as Co(II) enzymes, but their in vivo metal remains to be defined. While activation of Escherichia coli MetAP (EcMetAP1) by Co(II), Mn(II), and Zn(II) was detectable by a colorimetric Met-S-Gly-Phe assay, significant activation by Ni(II) was shown in a fluorescence Met-AMC assay, in addition to Co(II) and Mn(II) activation. When tested on the metal-substituted EcMetAP1s, a few inhibitors that we obtained recently from a random screening on Co-EcMetAP1 either became much weak or lost activity on Mn- or Zn-EcMetAP1, although they kept inhibitory activity on Ni-EcMetAP1. A couple of peptidic inhibitors and the methionine mimetic (3R)-amino-(2S)-hydroxyheptanoic acid (AHHpA, 6) maintained moderate activities on Co-, Mn-, Zn-, and Ni-EcMetAP1s. Our results clearly demonstrate that the metal-substitution has changed the enzyme specificity for substrates and inhibitors. Therapeutic applications call for inhibitors specific for MetAP with a physiologically relevant metal at its active site.

Aminopeptidases↗

PubMed2003

Seven GC-rich microbial genomes adopt similar codon usage patterns regardless of their phylogenetic lineages.

Seven GC-rich (group I) and three AT-rich (group II) microbial genomes are analyzed in this paper. The seven microbes in group I belong to different phylogenetic lineages, even different domains of life. The common feature is that they are highly GC-rich organisms, with more than 60% genomic GC content. Group II includes three bacteria, which belong to the same subdivision as Pseudomonas aeruginosa in group I. The genomic GC content of the three bacteria is in the range of 26-50%. It is shown that although the phylogenetic lineages of the organisms in group I are remote, the common feature of highly genomic GC content forces them to adopt similar codon usage patterns, which constitutes the basis of an algorithm using a set of universal parameters to recognize known genes in the seven genomes. The common codon usage pattern of function known genes in the seven genomes is GGS type, where G, G, and S are the bases of G, non-G, and G/C, respectively. On the contrary, although the phylogenetic lineages of the three bacteria in group II are quite close, the codon usage patterns of function known genes in these genomes are obviously distinct. There are no universal parameters to identify known genes in the three genomes in group II. It can be deduced that the genomic GC content is more important than phylogenetic lineage in gene recognition programs. We hope that the work might be useful for understanding the common characteristics in the organization of microbial genomes.

Algorithms↗

PubMed2003

Discovery and structural modification of inhibitors of methionine aminopeptidases from Escherichia coli and Saccharomyces cerevisiae.

A series of pyridine-2-carboxylic acid derivatives were synthesized according to the leads from the screening, and potent inhibitors have been obtained by structural modification. They have shown submicromolar inhibition of the enzymes (for example, for 9n, IC(50) = 130 nM for EcMetAP1 and IC(50) = 380 nM for ScMetAP1). They represent small-molecule MetAP inhibitors with novel structures different from alkylating fumagillin derivatives and peptidic bestatin-based MetAP inhibitor.

Aminopeptidases↗

PubMed2003

Gene recognition from questionable ORFs in bacterial and archaeal genomes.

The ORFs of microbial genomes in annotation files are usually classified into two groups: the first corresponds to known genes; whereas the second includes 'putative', 'probable', 'conserved hypothetical', 'hypothetical', 'unknown' and 'predicted' ORFs etc. Since the annotation is not 100% accurate, it is essential to confirm which ORF of the latter group is coding and which is not. Starting from known genes in the former, this paper describes an improved Z curve method to recognize genes in the latter. Ten-fold cross-validation tests show that the average accuracy of the algorithm is greater than 99% for recognizing the known genes in 57 bacterial and archaeal genomes. The method is then applied to recognize genes of the latter group. The likely non-coding ORFs in each of the 57 bacterial or archaeal genomes studied here are recognized and listed at the website http://tubic.tju.edu.cn/ZCURVE_C_html/noncoding.html. The working mechanism of the algorithm has been discussed in details. A computer program, called ZCURVE_C, was written to calculate a coding score called Z-curve score for ORFs in the above 57 bacterial and archaeal genomes. Coding/non-coding is simply determined by the criterion of Z-curve score > 0/ Z-curve score < 0. A website has been set up to provide the service to calculate the Z-curve score. A user may submit the DNA sequence of an ORF to the server at http://tubic.tju.edu.cn/ZCURVE_C/Default.cgi, and the Z-curve score of the ORF is calculated and returned to the user immediately.

Algorithms↗

PubMed2003

Performance evaluation of the Abbott Cell-Dyn 1800 automated hematology analyzer.

The Cell-Dyn 1800 is a new automated hematology analyzer that provides an 18-parameter blood count and operates at a throughput of 60 specimens per hour. (Clinical significance has not been established for plateletcrit and platelet distribution width; therefore these parameters are not reportable in the United States.) The instrument is smaller than its predecessor and offers a variety of enhanced features, including increased patient data storage, availability of a bar code reader, and a cyanide-free reagent system. Our study comprehensively assessed the analytical characteristics of the instrument in a variety of areas. When applicable the requirements of appropriate NCCLS guideline documents were followed. Instrument background and carryover were shown to be consistent with the performance specification. Accuracy was demonstrated by linearity studies and by comparison of results from the automated blood count parameters of the Cell-Dyn 1700 and from the manual differential method. The instrument-generated results and morphology flags were found to be effective in screening for abnormalities demonstrated by manual morphological assessment. Precision of the Cell-Dyn 1800 instrument was studied over the short term (with fresh blood) as well as the long term with stabilized quality control material. In either case, the instrument exhibited satisfactory performance. The blood count parameters showed acceptable stability during storage experiments, although in the case of mean platelet volume, the Cell-Dyn 1800 results increased during the first 6 hours of storage, reflecting the morphological changes expected of platelets from EDTA-anticoagulated specimens.

Blood Cell Count↗