Identifying the Hub Proteins from Complicated Membrane Protein Network Systems

157

Wenxiang diagram is a new two-dimensional representation that characterizes the disposition of hydrophobic and hydrophilic residues in α-helices. In this research, the hydrophobic and hydrophilic residues of two leucine zipper coiled-coil (LZCC) structural proteins, cGKIα(1-59) and MBS(CT35) are dispositioned on the wenxiang diagrams according to heptad repeat pattern (abcdefg)(n), respectively. Their wenxiang diagrams clearly demonstrate that the residues with same repeat letters are laid on same side of the spiral diagrams, where most hydrophobic residues are positioned at a and d, and most hydrophilic residues are localized on b, c, e, f and g polar position regions. The wenxiang diagrams of a dimetric LZCC can be represented by the combination of two monomeric wenxiang diagrams, and the wenxiang diagrams of the two LZCC (tetramer) complex structures can also be assembled by using two pairs of their wenxiang diagrams. Furthermore, by comparing the wenxiang diagrams of cGKIα(1-59) and MBS(CT35), the interaction between cGKIα(1-59) and MBS(CT35) is suggested to be weaker. By analyzing the wenxiang diagram of the cGKIα(1-59.)·MBS(CT42) complex structure, most affected residues of cGKIα(1-59) by the interaction with MBS(CT42) are proposed at positions d, a, e and g of the LZCC structure. These findings are consistent with our previous NMR results. Incorporating NMR spectroscopy, the wenxiang diagrams of LZCC structures may provide novel insights into the interaction mechanisms between dimeric, trimeric, tetrameric coiled-coil structures.

Section: Introductionmentioning

confidence: 99%

The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein–protein interaction mechanism

Zhou

2011

157

“…Using graphical approaches to study biological problems can provide an intuitive picture or useful insights for helping analyzing complicated mechanisms in these systems, as demonstrated by many previous studies on a series of important biological topics, such as enzyme-catalyzed reactions (Chou, 1981(Chou, , 1989Chou and Forsen, 1980;Zhou and Deng, 1984), protein folding kinetics (Chou, 1990), inhibition of HIV-1 reverse transcriptase (Althaus et al, 1993a(Althaus et al, , 1993b(Althaus et al, , 1993c, inhibition kinetics of processive nucleic acid polymerases and nucleases (Chou et al, 1994), drug metabolism systems (Chou, 2010), analysis of DNA sequence (Xie and Mo, 2011;Yu et al, 2009) and protein sequence evolution (Wu et al, 2010). Meanwhile, the 'wenxiang diagram' (Chou et al, 1997) has been used to investigate proteinprotein interactions (Chou and Cai, 2006;Smith and Nilar, 2010), and the graphical representation utilized to identify the hub proteins from complicated network systems (Gonzalez-Diaz et al, 2009;Hu et al, 2011;Shen et al, 2010). Recently, the 'cellular automaton image' (Wolfram, 1984) has also been applied to study hepatitis B viral infections (Xiao et al, 2006), HBV virus gene missense mutation (Xiao et al, 2005b), and visual analysis of SARS-CoV (Wang et al, 2005), as well as representing complicated biological sequences (Xiao et al, 2005a), and helping to identify various protein attributes (Xiao et al, 2008(Xiao et al, , 2009.…”

Section: Introductionmentioning

confidence: 99%

Disease embryo development network reveals the relationship between disease genes and embryo development genes

Gong

Liu

Zhang

et al. 2011

A basic problem for contemporary biology and medicine is exploring the correlation between human disease and underlying cellular mechanisms. For a long time, several efforts were made to reveal the similarity between embryo development and disease process, but few from the system level. In this article, we used the human protein-protein interactions (PPIs), disease genes with their classifications and embryo development genes and reconstructed a human disease-embryo development network to investigate the relationship between disease genes and embryo development genes. We found that disease genes and embryo development genes are prone to connect with each other. Furthermore, diseases can be categorized into three groups according to the closeness with embryo development in gene overlapping, interacting pattern in PPI network and co-regulated by microRNAs or transcription factors. Embryo development high-related disease genes show their closeness with embryo development at least in three biological levels. But it is not for embryo development medium-related disease genes and embryo development low-related disease genes. We also found that embryo development high-related disease genes are more central than other disease genes in the human PPI network. In addition, the results show that embryo development high-related disease genes tend to be essential genes compared with other diseases' genes. This network-based approach could provide evidence for the intricate correlation between disease process and embryo development, and help to uncover potential mechanisms of human complex diseases.

“…Enright and Ouzounis, 1999), phylogenetic profile (Pellegrini et al, 1999), patterns of gene expression (Zhou et al, 2002), or phenotype data (Clare and King, 2002) to predict protein functions. With the rapid development of high-throughput techniques for the detection of protein-protein interactions (PPIs), large-scale protein interactions' data have been generated recently, and become another abundant resource to study various problems in biological systems (Hu et al, 2 2012;Li et al, 2012a;Ren et al, 2011;Shen et al, 2010;Zheng et al, 2012;Hu et al, 2011b), especially including the prediction of unknown functions of proteins (Hu et al, 2011a).…”

Section: Introductionmentioning

confidence: 99%

k-Partite cliques of protein interactions: A novel subgraph topology for functional coherence analysis on PPI networks

Liu

Chen

2014

Many studies are aimed at identifying dense clusters/subgraphs from protein-protein interaction (PPI) networks for protein function prediction. However, the prediction performance based on the dense clusters is actually worse than a simple guilt-by-association method using neighbor counting ideas. This indicates that the local topological structures and properties of PPI networks are still open to new theoretical investigation and empirical exploration. We introduce a novel topological structure called k-partite cliques of protein interactions-a functionally coherent but not-necessarily dense subgraph topology in PPI networks-to study PPI networks. A k-partite protein clique is a maximal k-partite clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI's maximal k-partite cliques, we propose to transform PPI networks into induced K-partite graphs where edges exist only between the partites. Then, we present a maximal k-partite clique mining (MaCMik) algorithm to enumerate maximal k-partite cliques from K-partite graphs. Our MaCMik algorithm is then applied to a yeast PPI network. We observed interesting and unusually high functional coherence in k-partite protein cliques-the majority of the proteins in k-partite protein cliques, especially those in the same partites, share the same functions, although k-partite protein cliques are not restricted to be dense compared with dense subgraph patterns or (quasi-)cliques. The idea of k-partite protein cliques provides a novel approach of characterizing PPI networks, and so it will help function prediction for unknown proteins.