Inhibition of copper absorption by zinc

Copper and zinc interact at the intestinal mucosal level, affecting copper absorption. Amino acids, such as histidine, may affect the absorption of these two elements by chelating these cations. The two mechanisms could have additive potential. This possibility was investigated using a duodenal-jejunal single-pass perfusion procedure in anesthetized rats. Copper absorption and tissue retention from solutions containing 0.1 mM copper were determined in the presence of either no zinc or equimolar zinc, or at a zinc/copper ratio of 10/1, either without histidine or with histidine at a 10/1 or 20/1 ratio to copper. Copper removal from the intestinal lumen was decreased by zinc, and further reduced by increasing concentrations of histidine. There was a greater accumulation of copper in the small intestine, reaching a maximum with a 10-fold excess of histidine. With zinc at a 10/1 ratio to copper, the addition of a 10- or 20-fold molar excess of histidine further decreased the net uptake of copper from the perfusate while greater copper accumulation in the tissue occurred. Histidine thus enhances the inhibitory effects of zinc on copper absorp|tion, suggesting the application of convergent mechanisms for diminishing copper uptake. This could be relevant for the treatment of Wilson’s disease.     

Bayesian adaptive sequence alignment algorithms

The selection of a scoring matrix and gap penalty parameters continues to be an important problem in sequence alignment. We describe here an algorithm, the 'Bayes block aligner, which bypasses this requirement. Instead of requiring a fixed set of parameter settings, this algorithm returns the Bayesian posterior probability for the number of gaps and for the scoring matrices in any series of interest. Furthermore, instead of returning the single best alignment for the chosen parameter settings, this algorithm returns the posterior distribution of all alignments considering the full range of gapping and scoring matrices selected, weighing each in proportion to its probability based on the data. We compared the Bayes aligner with the popular Smith-Waterman algorithm with parameter settings from the literature which had been optimized for the identification of structural neighbors, and found that the Bayes aligner correctly identified more structural neighbors. In a detailed examination of the alignment of a pair of kinase and a pair of GTPase sequences, we illustrate the algorithm's potential to identify subsequences that are conserved to different degrees. In addition, this example shows that the Bayes aligner returns an alignment-free assessment of the distance between a pair of sequences.      

Isolation and analysis of candidate myeloid tumor suppressor genes from a commonly deleted segment of 7q22

Monosomy 7 and deletions of 7q are recurring leukemia-associated cytogenetic abnormalities that correlate with adverse outcomes in children and adults. We describe a 2.52-Mb genomic DNA contig that spans a commonly deleted segment of chromosome band 7q22 identified in myeloid malignancies. This interval currently includes 14 genes, 19 predicted genes, and 5 predicted pseudogenes. We have extensively characterized the FBXL13, NAPE-PLD, and SVH genes as candidate myeloid tumor suppressors. FBXL13 encodes a novel F-box protein, SVHis a member of a gene family that contains Armadillo-like repeats, and NAPE-PLD encodes a phospholipase D-type phosphodiesterase. Analysis of a panel of leukemia specimens with monosomy 7 did not reveal mutations in these or in the candidate genes LRRC17, PRO1598, and SRPK2. This fully sequenced and annotated contig provides a resource for candidate myeloid tumor suppressor gene discovery.     

FRASS: the web-server for RNA structural comparison

Background  

The impressive increase of novel RNA structures, during the past few years, demands automated methods for structure comparison. While many algorithms handle only small motifs, few techniques, developed in recent years, (ARTS, DIAL, SARA, SARSA, and LaJolla) are available for the structural comparison of large and intact RNA molecules.     

Improving the quality of protein structure models by selecting from alignment alternatives

Background  

In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish high quality protein structure models from inferior models. Here, we propose a new method to use an MQAP to improve the quality of models. With a given target sequence and template structure, we construct a number of different alignments and corresponding models for the sequence. The quality of these models is scored with an MQAP and used to choose the most promising model. An SVM-based selection scheme is suggested for combining MQAP partial potentials, in order to optimize for improved model selection.