Sequential1H-NMR assignments of neurotoxin III from the sea anemoneHeteractis macrodactylus and structural comparison with related toxins

The complete sequence-specific assignment of resonances in the¹H-NMR spectrum of the polypeptide neurotoxin III (Hm III) from the sea anemoneHeteractis macrodactylus is described. Comparison of the chemical shifts and pattern of NOEs for Hm III with those for the related toxin Hp III fromHeteractis paumotensis, which differs only in the substitution of Asn for Tyr at position 11, shows that the overall secondary and tertiary structures are conserved. The largest differences in chemical shift caused by the substitution at position 11 are observed for the NH resonances of Arg-13, Thr-14, Ala-15, Leu-17, and Cys-26. The CH resonances influenced most are those of ASP-6, Gly-9, Leu-17, and Glu-42, while the most affected CH resonances are from Leu-17, Glu-28, and Lys-32. The absence of long-range NOEs to the aromatic ring of Tyr-11 as well as the lack of significant chemical shift effects on residues outside the loop comprising residues 7–16 confirm that this part of the loop makes no long-lived contacts with the rest of the molecule. The deviations from random coil shifts of Hm III are compared with those of the related anemone toxins Hp II, Hp III, and toxin I fromStichodactyla helianthus (Sh I). The similarity in deviations in chemical shift as a function of sequence position for these four toxins emphasizes the overall structural homology among these polypeptides.     

The dynamics of sparse random networks

Recurrent neural networks with full symmetric connectivity have been extensively studied as associative memories and pattern recognition devices. However, there is considerable evidence that sparse, asymmetrically connected, mainly excitatory networks with broadly directed inhibition are more consistent with biological reality. In this paper, we use the technique of return maps to study the dynamics of random networks with sparse, asymmetric connectivity and nonspecific inhibition. These networks show three qualitatively different kinds of behavior: fixed points, cycles of low period, and extremely long cycles verging on aperiodicity. Using statistical arguments, we relate these behaviors to network parameters and present empirical evidence for the accuracy of this statistical model. The model, in turn, leads to methods for controlling the level of activity in networks. Studying random, untrained networks provides an understanding of the intrinsic dynamics of these systems. Such dynamics could provide a substrate for the much more complex behavior shown when synaptic modification is allowed.     

Identification of Products from Oxidation of Uric Acid Induced by Hydroxyl Radicals

The aim of the present study was to separate and characterise products formed by oxidation of uric acid by hydroxyl radicals with a view to probing for these products in vivo in clinical contexts. Aerated solutions of 200 μM uric acid, or its oxidation products, allantoin or parabanic acid, were exposed to gamma radiolysis, (52.0 Gy/min), as a source of HO- radicals, at pH 3.4 and 7.4. Aliquots were taken every 5 minutes for 20 minutes and oxidation products were separated by HPLC and analysed with a diode array detector. Identities of oxidation products were confirmed on the basis of similarity of retention times and absorbance spectra and peak purity parameters of known standards. Hydroperoxides were measured by tri-iodide formation in the 20 minute sample. Exposure of uric acid to such HO fluxes produced a net loss of the parent compound with formation of a complex mixture of products with allantoin and parabanic acid being the predominant products at pH 3.4. The rate of uric acid degradation at physiological pH was slower and the distribution of oxidation products was different. A small but significant amount of uric acid hydroperoxide was detected at both pHs. A mechanism for uric acid oxidation under these conditions is presented.     

Mouse chromosome 1

Chair of Committee for Mouse Chromosome 1     

SYSTEMATIC ANALYSIS OF SPHAEROPLEA (CHLOROPHYCEAE)1

A systematic investigation of the genus Sphaeroplea was conducted using cladistic analyses of both structural and isozyme characters for the same set of taxa. The structural data were not able to fully resolve some of the taxa while the isozyme data did produce a tree in which all nodes were supported by data. The structural characters were relatively consistent with one another, whereas the isozyme characters were much less internally consistent. Results from independent, cladistic analyses of both data sets support the concept that among those Sphaeroplea species investigated, S. fragilis Buchheim et Hoffman had an early divergence. The two data sets differed primarily in that the structural data support monophyly of the genus Sphaeroplea and the isozyme data do not. The greater relative consistency of the structural data suggests better support for trees inferred from its analysis. Furthermore, searches for character congruence between the two data sets revealed isozyme data which support monophyly of the genus Sphaeroplea, but had been overwhelmed by conflicting isozyme characters.     

Fast optimal genome tiling with applications to microarray design and homology search.

In this paper, we consider several variations of the following basic tiling problem: given a sequence of real numbers with two size-bound parameters, we want to find a set of tiles of maximum total weight such that each tiles satisfies the size bounds. A solution to this problem is important to a number of computational biology applications such as selecting genomic DNA fragments for PCR-based amplicon microarrays and performing homology searches with long sequence queries. Our goal is to design efficient algorithms with linear or near-linear time and space in the normal range of parameter values for these problems. For this purpose, we first discuss the solution to a basic online interval maximum problem via a sliding-window approach and show how to use this solution in a nontrivial manner for many of the tiling problems introduced. We also discuss NP-hardness results and approximation algorithms for generalizing our basic tiling problem to higher dimensions. Finally, computational results from applying our tiling algorithms to genomic sequences of five model eukaryotes are reported.