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The early history and emergence of molecular functions and modular scale-free network behavior.

ABSTRACT: The formation of protein structural domains requires that biochemical functions, defined by conserved amino acid sequence motifs, be embedded into a structural scaffold. Here we trace domain history onto a bipartite network of elementary functional loop sequences and domain structures defined at the fold superfamily level of SCOP classification. The resulting 'elementary functionome' network and its loop motif and structural domain graph projections create evolutionary 'waterfalls' describing the emergence of primordial functions. Waterfalls reveal how ancient loops are shared by domain structures in two initial waves of functional innovation that involve founder 'p-loop' and 'winged helix' domain structures. They also uncover a dynamics of modular motif embedding in domain structures that is ongoing, which transfers 'preferential' cooption properties of ancient loops to emerging domains. Remarkably, we find that the emergence of molecular functions induces hierarchical modularity and power law behavior in network evolution as the network of motifs and structures expand metabolic pathways and translation.

SUBMITTER: Aziz MF

PROVIDER: S-EPMC4848518 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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The early history and emergence of molecular functions and modular scale-free network behavior.

Aziz M Fayez MF Caetano-Anollés Kelsey K Caetano-Anollés Gustavo G

Scientific reports 20160428

The formation of protein structural domains requires that biochemical functions, defined by conserved amino acid sequence motifs, be embedded into a structural scaffold. Here we trace domain history onto a bipartite network of elementary functional loop sequences and domain structures defined at the fold superfamily level of SCOP classification. The resulting 'elementary functionome' network and its loop motif and structural domain graph projections create evolutionary 'waterfalls' describing th ...[more]

PMID: 27121452

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Project description:Although the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter, the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree. Structural properties of numerical simulated networks are analyzed and compared with each of the four real networks. This work helps understand the interplay between structures on different scales in online social networks.

Dataset Information

The early history and emergence of molecular functions and modular scale-free network behavior.

Publications

The early history and emergence of molecular functions and modular scale-free network behavior.

Similar Datasets

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