A comprehensive statistical study of metabolic and protein–protein interaction network properties

Gamermann, D.; Triana-Dopico, Julián; Jaime, R.

doi:10.1016/j.physa.2019.122204

Cited by 11 publications

(12 citation statements)

References 39 publications

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“…The STRING database, in particular, has been used frequently as a tool for inferring missing protein activities in genomes thereby extending or increasing the protein repertoire of organisms including pathogenic organisms [48,49]. There have been recent instances in the literature of fitting the power-law distribution to PPI networks generated using STRING data [50,51]. For a PPI network generated using a threshold of 0.9, only a fraction of the nodes (from the right tail of the distribution) fit the Power-law function [50].…”

Section: Discussionmentioning

confidence: 99%

“…There have been recent instances in the literature of fitting the power-law distribution to PPI networks generated using STRING data [50,51]. For a PPI network generated using a threshold of 0.9, only a fraction of the nodes (from the right tail of the distribution) fit the Power-law function [50]. In the other case, networks generated for 3 sets of differentially expressed genes (upregulated, downregulated, and total) using data from the STRING database version 10 with a confidence threshold of 0.4, all followed the Power-law with R2 values of 0.749, 0.859, and 0.836 respectively [51].…”

Section: Discussionmentioning

confidence: 99%

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Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

2020

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Background Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function. Results The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes. Conclusions We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

2020

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“…In the LFR benchmark, the network vertices are generated with a degree distribution that follows a power-law function and then assigned to communities whose sizes distribution is also a scale-free function, though the exponents of the two distributions may be different. The use of scale-free functions for the degree distribution of vertices in a network is inspired by many works in the field [27,28,29], though some other works dispute this claim [30,31,32]. The benchmark then connects the nodes among them such that a fraction µ of each vertex connections will be with nodes from the same community while the remaining fraction 1 − µ is with nodes from other communities.…”

Section: Benchmarksmentioning

confidence: 99%

An algorithm for network community structure determination by surprise

Gamermann,

Pellizaro

2020

Preprint

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Graphs representing real world systems may be studied from their underlying community structure. A community in a network is an intuitive idea for which there is no consensus on its objective mathematical definition. The most used metric in order to detect communities is the modularity, though many disadvantages of this parameter have already been noticed in the literature. In this work, we present a new approach based on a different metric: the surprise. Moreover, the biases of different community detection algorithms and benchmark networks are thoroughly studied, identified and commented about.

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“…Regardless of the layout, metabolic networks typically contain many poorly connected nodes interconnected by a few heavily connected nodes (the hubs), the latter being especially associated with cofactors such as ATP, NADH, glutamate and coenzyme A [29] . Consequently, the node connectivity, defined as the number of edges per node, shows a heavy-tailed probability distribution [30] , [31] , [32] , [33] , [34] , [35] , [36] . Furthermore, metabolic networks have a non-random topology and are likely organized in a hierarchical modular structure ( Fig.…”

Section: Introductionmentioning

confidence: 99%

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Desmet

Brouckaert

Boerjan

et al. 2021

Computational and Structural Biotechnology Journal

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A comprehensive statistical study of metabolic and protein–protein interaction network properties

Cited by 11 publications

References 39 publications

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

An algorithm for network community structure determination by surprise

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

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