Universities in Hunan Province
Universities in Hunan Province
Fu W.-W.,Universities in Hunan Province |
Fu W.-W.,Hengyang Normal University |
Zhang F.-X.,Universities in Hunan Province |
Zhang F.-X.,Hengyang Normal University |
And 2 more authors.
Journal of Structural Chemistry | Year: 2014
Two iron compounds [Fe( m-NO2phtpy)2](ClO4)2 1 and [Fe(m-Clphtpy)2](ClO4)2 2 are synthesized and their structures are determined by single crystal X-ray diffraction. Crystal data for 1 are: orthorhombic, space group Pcca, a = 25.471(4) A˚, b = 10.8182(18) A˚, c = 14.682(2) A˚, and Z = 4. Crystal data for 2 are: orthorhombic, space group Pnna, a = 14.521(2) A˚, b = 23.980(4) A˚, c = 23.142(4) A˚, and Z = 8. The iron atoms are coordinated by six N atoms from two terpyridines in both compounds. These two compounds are all linked by hydrogen bonds with C as donor and O as acceptor into a 3D network for 1 and a 2D network for 2. If viewed along the c direction in 1 and the a direction in 2, both compounds show the ordered figure with terpyridine overlapped as a square and the phenyl rings overlapped as a rectangle or a square. © 2014 by Pleiades Publishing, Ltd.
Fu W.-W.,Universities in Hunan Province
Acta Crystallographica Section E: Structure Reports Online | Year: 2012
In the title compound, C8H7ClO2, the hydroxyl and aldehyde groups are co-planar with the benzene ring [maximum deviation 0.018 (3) Å], and the Cl - C - C plane is almost perpendicular to the benzene ring [dihedral angle 83.7 (2)°]. An intramolecular O - H·O hydrogen bond occurs between the hydroxyl and aldehyde groups.
Cao B.,Hunan University |
Cao B.,Universities in Hunan Province |
Luo J.,Hunan University |
Luo J.,Universities in Hunan Province |
And 6 more authors.
Computational Biology and Chemistry | Year: 2015
The identification of protein complexes in protein-protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions. © 2015 Elsevier Ltd. All rights reserved.