Evolutionary analysis of phycobiliproteins: Implications for their structural and functional relationships

Phycobiliproteins, together with linker polypeptides and various chromophores, are basic building blocks of phycobilisomes, a supramolecular complex with a light-harvesting function in cyanobacteria and red algae. Previous studies suggest that the different types of phycobiliproteins and the linker polypeptides originated from the same ancestor. Here we retrieve the phycobilisome-related genes from the well-annotated and even unfinished cyanobacteria genomes and find that many sites with elevated d(N)/d(S) ratios in different phycobiliprotein lineages are located in the chromophore-binding domain and the helical hairpin domains (X and Y). Covariation analyses also reveal that these sites are significantly correlated, showing strong evidence of the functional-structural importance of interactions among these residues. The potential selective pressure driving the diversification of phycobiliproteins may be related to the phycobiliprotein-chromophore microenvironment formation and the subunits interaction. Sites and genes identified here would provide targets for further research on the structural-functional role of these residues and energy transfer through the chromophores.

Zooplankton functional groups on the continental shelf of the yellow sea

Zooplankton plays a vital role in marine ecosystems. Variations in the zooplankton species composition, biomass, and secondary production will change the structure and function of the ecosystem. How to describe this process and make it easier to be modeled in the Yellow Sea ecosystem is the main purpose of this paper. The zooplankton functional groups approach, which is considered a good method of linking the structure of food webs and the energy flow in the ecosystems, is used to describe the main contributors of secondary produciton of the Yellow Sea ecosystem. The zooplankton can be classified into six functional groups: giant crustaceans, large copepods, small copepods, chaetognaths, medusae, and salps. The giant crustaceans, large copepods, and small copepods groups, which are the main food resources for fish, are defined depending on the size spectrum. Medusae and chaetognaths are the two gelatinous carnivorous groups, which compete with fish for food. The salps group, acting as passive filter-feeders, competes with other species feeding on phytoplankton, but their energy could not be efficiently transferred to higher trophic levels. From the viewpoint of biomass, which is the basis of the food web, and feeding activities, the contributions of each functional group to the ecosystem were evaluated; the seasonal variations, geographical distribution patterns, and species composition of each functional group were analyzed. The average zooplankton biomass was 2.1 g dry wt m(-2) in spring, to which the giant crustaceans, large copepods, and small copepods contributed 19, 44, and 26%, respectively. High biomasses of the large copepods and small copepods were distributed at the coastal waters, while the giant crustaceans were mainly located at offshore area. In summer, the mean biomass was 3.1 g dry wt m(-2), which was mostly contributed by the giant crustaceans (73%), and high biomasses of the giant crustaceans, large copepods, and small copepods were all distributed in the central part of the Yellow Sea. During autumn, the mean biomass was 1.8 g dry wt m(-2), which was similarly constituted by the giant crustaceans, large copepods, and small copepods (36, 33, and 23%, respectively), and high biomasses of the giant crustaceans and large copepods occurred in the central part of the Yellow Sea, while the small copepods were mainly located at offshore stations. The giant crustaceans and large copepods dominated the zooplankton biomass (2.9 g dry wt m(-2)) in winter, contributing respectively 57 and 27%, and they, as well as the small copepods, were all mainly located in the central part of the Yellow Sea. The chaetognaths group was mainly located in the northern part of the Yellow Sea during all seasons, but contributed less to the biomass compared with the other groups. The medusae and salps groups were distributed unevenly, with sporadic dynamics, mainly along the coastline and at the northern part of the Yellow Sea. No more than 10 species belonging to the respective functional groups dominated the zooplankton biomass and controlled the dynamics of the zooplankton community. The clear picture of the seasonal and spatial variations of each zooplankton functional group makes the complicated Yellow Sea ecosystem easier to be understood and modeled. (C) 2010 Elsevier Ltd. All rights reserved.

High performance cocktail functional monomer for making molecule imprinting polymer

In order to make a molecule imprinting polymer (MIP) with highly chiral selectivity against N-t-Boc-L-Trp, a new kind of "cocktail" functional monomer: acrylamide+2-vinylpyridine was investigated. The MIP showed impressive chiral selectivity (alpha=3.23). With the increasing of water content in the mobile phase, ionic and hydrophobic interaction were found to be responsible for the chiral recognition process instead of the hydrogen bond. Tailing and peak asymmetry problems were overcome by using linear gradient elution. Physical properties such as thermal stability and pore structure for the MIP were also investigated.

New cocktail functional monomers in making molecule imprinting polymer with high chiral selectivity

in order td produce molecule imprinting polymer (MIP) with high chiral selectivity against N-c-protected amino acid, new cocktail functional monomers acrylamide (AM) + 2-vinylpyridine (2-VP) and AM + methacrylic acid (MAA) were investigated. AM + 2-VP was found to be more efficient in improving the selectivity and resolution of the molecule imprinting polymer.

Using Structural and Functional Information in Diagnostic Design

We wish to design a diagnostic for a device from knowledge of its structure and function. the diagnostic should achieve both coverage of the faults that can occur in the device, and should strive to achieve specificity in its diagnosis when it detects a fault. A system is described that uses a simple model of hardware structure and function, representing the device in terms of its internal primitive functions and connections. The system designs a diagnostic in three steps. First, an extension of path sensitization is used to design a test for each of the connections in teh device. Next, the resulting tests are improved by increasing their specificity. Finally the tests are ordered so that each relies on the fewest possible connections. We describe an implementation of this system and show examples of the results for some simple devices.

Combining Functional and Structural Reasoning for Safety Analysis of Electrical Designs

C.J.Price, D.R.Pugh, N.A.Snooke, J.E.Hunt, M.S.Wilson, Combining Functional and Structural Reasoning for Safety Analysis of Electrical Designs, Knowledge Engineering Review, vol 12:3, pp.271-287, 1997.

Automating Mechanical FMEA Using Functional Models

Hughes, N., Chou E., Price, C. J. Lee M. H.(1999). Automating Mechanical FMEA Using Functional Models, Proceedings 12th Int. Florida AI Research Soc. Conf. (FLAIRS-99), AAAI Press, May 1999, pp. 394-398.

Confirmation of Data Mining Based Predictions of Protein Function

King, R. D. and Wise, P. H. and Clare, A. (2004) Confirmation of Data Mining Based Predictions of Protein Function. Bioinformatics 20(7), 1110-1118