FunPred 3.0: improved protein function prediction using protein interaction network

Saha, Sovan; Chatterjee, Piyali; Basu, Subhadip; Nasipuri, Mita; Plewczynski, Dariusz

doi:10.7717/peerj.6830

Cited by 14 publications

(15 citation statements)

References 47 publications

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“…Mathematically, PPIN can be highlighted as a graph G nv , which consists of a set of vertices v(nodes) connected by edges e (links). Thus, G nv = (v,e) (Saha et al, 2014;Saha et al, 2019a).…”

Section: Protein-protein Interaction Network (Ppin)mentioning

confidence: 99%

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Detection of spreader nodes in human-SARS-CoV protein-protein interaction network

Saha

Chatterjee²,

Nasipuri

et al. 2021

PeerJ

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The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein–protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein–protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art.

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Section: Protein-protein Interaction Network (Ppin)mentioning

confidence: 99%

“…In a PPIN, level-1 proteins of a node are those proteins that are in direct connection with that node, i.e., its immediate neighbors, whereas level-2 proteins are those proteins that are indirectly connected with level-1 proteins of that node, i.e., its indirect neighbors (Saha et al, 2014;Saha et al, 2019a).…”

Section: Level-1 and Level-2 Proteinsmentioning

confidence: 99%

Detection of spreader nodes in human-SARS-CoV protein-protein interaction network

Saha

Chatterjee²,

Nasipuri

et al. 2021

PeerJ

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“…Various research areas like protein structure prediction [ 1 , 2 ]; protein function prediction using protein sequences [ 3 , 4 ], protein domains [ 5 , 6 ], and protein–protein interaction networks (PPIN) [ 7 , 8 , 9 , 10 , 11 ]; protein subcellular localization identification [ 12 , 13 ]; and detection of essential proteins [ 14 , 15 , 16 ] have significantly been exploited due to the increase in the availability of a large number of proteins/protein sequences in the post-genomic era. In general, essential proteins are the highly connected modules in a PPIN [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN

Banik¹,

Podder²,

Saha

et al. 2022

Cells

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Proteins are vital for the significant cellular activities of living organisms. However, not all of them are essential. Identifying essential proteins through different biological experiments is relatively more laborious and time-consuming than the computational approaches used in recent times. However, practical implementation of conventional scientific methods sometimes becomes challenging due to poor performance impact in specific scenarios. Thus, more developed and efficient computational prediction models are required for essential protein identification. An effective methodology is proposed in this research, capable of predicting essential proteins in a refined yeast protein–protein interaction network (PPIN). The rule-based refinement is done using protein complex and local interaction density information derived from the neighborhood properties of proteins in the network. Identification and pruning of non-essential proteins are equally crucial here. In the initial phase, careful assessment is performed by applying node and edge weights to identify and discard the non-essential proteins from the interaction network. Three cut-off levels are considered for each node and edge weight for pruning the non-essential proteins. Once the PPIN has been filtered out, the second phase starts with two centralities-based approaches: (1) local interaction density (LID) and (2) local interaction density with protein complex (LIDC), which are successively implemented to identify the essential proteins in the yeast PPIN. Our proposed methodology achieves better performance in comparison to the existing state-of-the-art techniques.

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“…In the proposed methodology, Protein-protein interaction network (PPIN) has been used as the central component in identification of spreader nodes in SARS-CoV. PPIN has been found to be very effective module for protein function determination [16]- [18] as well as in the identification of central/essential or key spreader nodes in the network [19]- [23]. Compactness of the network and its transmission capability is estimated using centrality analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Due to high morbidity and mortality of SARS-CoV2, it has been felt that there is a pressing need to properly understand the way of disease transmission from SARS-CoV-2 PPIN to human PPIN [16]- [18]. But since SARS-CoV-2 PPIN is still not available, SARS-CoV PPIN is considered for this research study due to its high genetic similarity with SARS-CoV-2.…”

Section: Introductionmentioning

confidence: 99%

Detection of spreader nodes and ranking of interacting edges in Human-SARS-CoV protein interaction network

Saha¹,

Chatterjee

Basu

et al. 2020

Preprint

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The entire world has recently witnessed the commencement of coronavirus disease 19 pandemic. It is caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). It has exploited human vulnerabilities to coronavirus outbreak. SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure which ultimately puts an end to human life. No proven vaccine for n-CoV is available till date in spite of significant research efforts worldwide. International Committee on Taxonomy of Viruses (ICTV) has reached to a consensus that the virus SARS-CoV-2 is highly genetically similar to Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) outbreak of 2003. It has been reported that SARS-CoV has ~89% genetic similarities with n-CoV. With this hypothesis, the current work focuses on the identification of spreader nodes in SARS-CoV protein interaction network. Various network characteristics like edge ratio, neighborhood density and node weight have been explored for defining a new feature spreadability index by virtue of which spreader nodes and edges are identified. The selected top spreader nodes having high spreadability index have been also validated by Susceptible-Infected-Susceptible (SIS) disease model. Initially, the proposed method is applied on a synthetic protein interaction network followed by SARS-CoV-human protein interaction network. Hence, key spreader nodes and edges (ranked edges) are unmasked in SARS-CoV proteins and its connected level 1 and level 2 human proteins. The new network attribute spreadability index along with generated SIS values of selected top spreader nodes when compared with the other network centrality based methodologies like Degree centrality (DC), Closeness centrality (CC), Local average centrality (LAC) and Betweeness centrality (BC) is found to perform relatively better than the existing-state-of-art.

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FunPred 3.0: improved protein function prediction using protein interaction network

Cited by 14 publications

References 47 publications

Detection of spreader nodes in human-SARS-CoV protein-protein interaction network

Detection of spreader nodes in human-SARS-CoV protein-protein interaction network

Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN

Detection of spreader nodes and ranking of interacting edges in Human-SARS-CoV protein interaction network

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