Cutoff variation induces different topological properties: a new discovery of amino acid network within protein.
ABSTRACT An increasing attention has been dedicated to the characterization of complex networks within the protein world. Before now most investigations about protein structures were only considered where the interactive cutoff distance R(c)=5 or 7A. It is noteworthy that the length of peptide bond is about 1.5A, the length of hydrogen bond is about 3A, the range of London-van der Waals force is about 5A and the range of hydrophobic effect can reach to 12A in protein molecule. Present work reports a study on the topological properties of the amino acid network constructed by different interactions above. The results indicate that the small-world property of amino acid network constructed by the peptide and hydrogen bond, London-van der Waals force and the hydrophobic effect is strong, very strong and relatively weak, respectively. Besides, there exists a precise exponential relation C is proportional to k(-0.5) at R(c)=12A. It means that the amino acid network constructed by the hydrophobic effect tend to be hierarchical. Functional modules could be the cause for hierarchical modularity architecture in protein structures. This study on amino acid interactive network for different interactions facilitates the identification of binding sites which is strongly linked with protein function, and furthermore provides reasonable understanding of the underlying laws of evolution in genomics and proteomics.
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ABSTRACT: Topological properties of native folds are obtained from statistical analysis of 160 low homology proteins covering the four structural classes. This is done analyzing one, two and three-vertex joint distribution of quantities related to the corresponding network of amino acid residues. Emphasis on the amino acid residue hydrophobicity leads to the definition of their center of mass as vertices in this contact network model with interactions represented by edges. The network analysis helps us to interpret experimental results such as hydrophobic scales and fraction of buried accessible surface area in terms of the network connectivity. Moreover, those networks show assortative mixing by degree. To explore the vertex-type dependent correlations, we build a network of hydrophobic and polar vertices. This procedure presents the wiring diagram of the topological structure of globular proteins leading to the following attachment probabilities between hydrophobic–hydrophobic 0.424(5), hydrophobic-polar 0.419(2) and polar–polar 0.157(3) residues.Physica A: Statistical Mechanics and its Applications 02/2006; · 1.68 Impact Factor
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ABSTRACT: In a cell or microorganism, the processes that generate mass, energy, information transfer and cell-fate specification are seamlessly integrated through a complex network of cellular constituents and reactions. However, despite the key role of these networks in sustaining cellular functions, their large-scale structure is essentially unknown. Here we present a systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variation in their individual constituents and pathways, these metabolic networks have the same topological scaling properties and show striking similarities to the inherent organization of complex non-biological systems. This may indicate that metabolic organization is not only identical for all living organisms, but also complies with the design principles of robust and error-tolerant scale-free networks, and may represent a common blueprint for the large-scale organization of interactions among all cellular constituents.Nature 11/2000; 407(6804):651-4. · 38.60 Impact Factor
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ABSTRACT: To reduce redundancy in the Protein Data Bank of 3D protein structures, which is caused by many homologous proteins in the data bank, we have selected a representative set of structures. The selection algorithm was designed to (1) select as many nonhomologous structures as possible, and (2) to select structures of good quality. The representative set may reduce time and effort in statistical analyses.Protein Science 04/1994; 3(3):522-4. · 2.74 Impact Factor