Inferring protein function by domain context similarities in protein-protein interaction networks

Zhang, Song; Hu, Chen; Liu, Ke; Sun, Zhirong

doi:10.1186/1471-2105-10-395

Cited by 29 publications

(51 citation statements)

References 28 publications

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“…26 Since protein dynamics and even protein function may differ per tissue, calculating the local networks and related functions from proteins identified within a tissue may yield information on the role of that protein specific to the tissue environment. 27 For this study, we focused on earmarking new ECM protein candidates by protein-protein network analysis for further investigation because of the importance of the ECM to valvular structure and cell-cell messaging. ECM proteins were sorted by percentile rank within the proteome and filtered to the top 20 th percentile of ECM proteins within the datasets.…”

Section: Resultsmentioning

confidence: 99%

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Networked-based Characterization of Extracellular Matrix Proteins from Adult Mouse Pulmonary and Aortic Valves

et al. 2010

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A precise mixture of extracellular matrix (ECM) secreted by valvular cells forms a scaffold that lends the heart valve the exact mechanical and tensile strength needed for accurate hemodynamic performance. ECM proteins are a key component of valvular endothelial cell (VEC) - valvular interstitial cell (VIC) communication essential for maintenance of the valve structure. This study reports the healthy adult pulmonary and aortic valve proteomes characterized by LC/MS/MS, resulting in 2710 proteins expressed by 1513 genes, including over 300 abundant ECM proteins. Surprisingly, this study defines a distinct proteome for each semilunar valve. Protein-protein networking (PPN) was used as a tool to direct selection of proteomic candidates for biological investigation. Local PPN for nidogen 1 (Nid1), biglycan (Bgn), elastin microfibril interface-located protein 1 (Emilin-1) and milk fat globule-EGF factor 8 protein (Mfge8) were enriched with proteins essential to valve function and produced biological functions highly relevant to valve biology. Immunofluorescent investigations demonstrated that these proteins are functionally distributed within the pulmonary and aortic valve structure, indicative of important contribution to valve function. This study yields new insight into protein expression contributing to valvular maintenance and health and provides a platform for unbiased assessment of protein alterations during disease processes.

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Section: Resultsmentioning

confidence: 99%

“…26,27 Protein-protein networks have been used to predict and confirm candidate disease genes, 28 to predict phenotypic effects of gene mutation, 29 and to infer protein function within specific tissue environments. 27,30 …”

Section: Introductionmentioning

confidence: 99%

Networked-based Characterization of Extracellular Matrix Proteins from Adult Mouse Pulmonary and Aortic Valves

et al. 2010

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“…The experiments are carried out on S. cerevisiae. The results show that our method outperforms other six existing methods (DCS [3], ZhangDC [33], Markov random field (MRF) model [34], WAC [29], RLC [22] and UBiRW [30]). …”

Section: Introductionmentioning

confidence: 73%

“…In order to assess the effectiveness of ThrRW, we compare it with other six methods (DCS [3], ZhangDC [33], Markov random field (MRF) model [34], WAC [29], RLC [22] and UBiRW [30]). DCS is a recent method, which combines the domain compositions of both proteins and their neighbors in PIN to predict protein functions.…”

Section: Resultsmentioning

confidence: 99%

Predicting Protein Functions by Using Unbalanced Random Walk Algorithm on Three Biological Networks

Peng

Chen

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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With the gap between the sequence data and their functional annotations becomes increasing wider, many computational methods have been proposed to annotate functions for unknown proteins. However, designing effective methods to make good use of various biological resources is still a big challenge for researchers due to function diversity of proteins. In this work, we propose a new method named ThrRW, which takes several steps of random walking on three different biological networks: protein interaction network (PIN), domain co-occurrence network (DCN), and functional interrelationship network (FIN), respectively, so as to infer functional information from neighbors in the corresponding networks. With respect to the topological and structural differences of the three networks, the number of walking steps in the three networks will be different. In the course of working, the functional information will be transferred from one network to another according to the associations between the nodes in different networks. The results of experiment on S. cerevisiae data show that our method achieves better prediction performance not only than the methods that consider both PIN data and GO term similarities, but also than the methods using both PIN data and protein domain information, which verifies the effectiveness of our method on integrating multiple biological data sources.

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“…Nevertheless, high throughput assays typically include false positives PPIs 5 which stipulate an enduring need for efficient computational methods to complement existing experimental approaches. In this context, combining the interolog method 6 with adding domain information 7 , gene ontology (GO) annotation 8 and cellular localization 9,10 yields a graphical representation of the interaction networks, a robust and well-established approach to provide an intuitive vision and useful insights to help and analyze complex relations therein, as indicated by several previous studies in the reconstruction and…”

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confidence: 99%

Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs

Gupta

Srivastava²,

Osmanoğlu³

et al. 2020

Sci Rep

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Apart from some model organisms, the interactome of most organisms is largely unidentified. Highthroughput experimental techniques to determine protein-protein interactions (ppis) are resource intensive and highly susceptible to noise. Computational methods of PPI determination can accelerate biological discovery by identifying the most promising interacting pairs of proteins and by assessing the reliability of identified PPIs. Here we present a first in-depth study describing a global view of the ant Camponotus floridanus interactome. Although several ant genomes have been sequenced in the last eight years, studies exploring and investigating PPIs in ants are lacking. Our study attempts to fill this gap and the presented interactome will also serve as a template for determining PPIs in other ants in future. our C. floridanus interactome covers 51,866 non-redundant PPIs among 6,274 proteins, including 20,544 interactions supported by domain-domain interactions (DDIs), 13,640 interactions supported by DDIs and subcellular localization, and 10,834 high confidence interactions mediated by 3,289 proteins. These interactions involve and cover 30.6% of the entire C. floridanus proteome.understanding of PPIs in various organisms [11][12][13][14] . Here we used domain information, subcellular localization and isoform information to filter the preliminary global PPI network of C. floridanus reconstructed on stringent interolog based criteria. We focus on interactions predicted with high confidence to reduce noise. This conservative approach rejects 79.1% of the preliminary predicted interactions. We then explored the topologically important and evolutionary conserved proteins by analyzing the reconstructed interactome regarding cellular functions. Scientific RepoRtS |(2020) 10:2334 | https://doi.Assigning the confidence score. In fact, the preliminary network is filtered successively as mentioned above to reconstruct the final network, in this way the final network is already of high-confidence as many network biologists working on PPI networks have used DDIs and subcellular localization either to increase confidence or validate the interacting pairs. Here additionally we used topology-based method CAPPIC (cluster-based assessment of protein-protein interaction confidence) to assign the interaction confidence score 31,69 in the filtered network. In brief, CAPPIC calculations are based on the assumption that the proteins existing in the same network module are expected to have a higher number of common neighbours (neighbourhood interconnectedness 70 ), and a short path length inbetween 71 . For scoring the confidence level, CAPPIC first performs the clustering of the network using a robust clustering algorithm, Markov Cluster (MCL) 72 and then scores the interactions according to their level of compliance with the basic assumptions of topology-based methods. For the clustering we used an MCL inflation value of 1.5. Scores were classified to three subsets; low confidence score between 0 to 0.3, medium confidence score betwe...

show abstract

Inferring protein function by domain context similarities in protein-protein interaction networks

Cited by 29 publications

References 28 publications

Networked-based Characterization of Extracellular Matrix Proteins from Adult Mouse Pulmonary and Aortic Valves

Networked-based Characterization of Extracellular Matrix Proteins from Adult Mouse Pulmonary and Aortic Valves

Predicting Protein Functions by Using Unbalanced Random Walk Algorithm on Three Biological Networks

Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs

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