scholarly journals Internetwork connectivity of molecular networks across species of life

2020 ◽  
Author(s):  
Tarun Mahajan ◽  
Roy D Dar
Keyword(s):  
Molecular Interactions ◽  
Protein Interactions ◽  
Transcriptional Regulatory Network ◽  
Building Blocks ◽  
Design Principles ◽  
Hierarchical Organization ◽  
Molecular Networks ◽  
Interacting Protein ◽  
Ppi Networks ◽  
Eukaryotic Species

AbstractBackgroundMolecular interactions have been studied as independent complex networks in systems biology. However, molecular networks dont exist independently of each other. In a network of networks approach (called multiplex), we uncover the design principles for the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network.ResultsWe find that TRN and PPI networks are non-randomly coupled in the TRN-PPI multiplex across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene interactions in TRN and protein-protein interactions in PPI also non-randomly overlap in the multiplex. These design principles are conserved across the five eukaryotic species. We show that the robustness of the TRN-PPI multiplex is dependent on these design principles. Further, functionally important genes and proteins, such as essential, disease-related and those involved in host-pathogen PPI networks, are preferentially situated in essential parts of the human multiplex with highly overlapping interactions.ConclusionWe unveil the multiplex architecture of TRN and PPI networks across different species. Multiplex architecture may thus define a general framework for studying molecular networks across the different species of life. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

scholarly journals Internetwork connectivity of molecular networks across species of life

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tarun Mahajan ◽  
Roy D. Dar
Keyword(s):  
Molecular Interactions ◽  
Protein Interactions ◽  
Transcriptional Regulatory Network ◽  
Building Blocks ◽  
Hierarchical Organization ◽  
Molecular Networks ◽  
Protein Protein Interactions ◽  
Interacting Protein ◽  
Protein Protein Interaction ◽  
Eukaryotic Species

AbstractMolecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein–protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene–gene and protein–protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN–PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

Neurofilament triplet protein interactions: evidence for the preferred formation of NF-L-containing dimers and a putative function for the end domains

1996 ◽  
Vol 109 (10) ◽  
pp. 2493-2498 ◽  
Author(s):  
D.A. Carpenter ◽  
W. Ip
Keyword(s):  
Intermediate Filaments ◽  
Hybrid System ◽  
Weak Interaction ◽  
Molecular Interactions ◽  
Protein Interactions ◽  
Building Blocks ◽  
Full Length ◽  
Yeast Two Hybrid ◽  
Yeast Two Hybrid System ◽  
Two Hybrid

In this report we examine the molecular interactions that lead to formation of neurofilaments, the intermediate filaments in neurons. Using the yeast two-hybrid system, we found that the rod domains of all three NF triplet proteins interacted strongly with one another and with rod domains of the Type III IF proteins, vimentin and desmin. A slight preference toward NF-L-containing dimers was observed over ones not containing NF-L. Interactions among the full length NF triplet proteins exhibited more specificity. Full length NF-L had only a relatively weak interaction with another full length NF-L molecule, but reacted more robustly with full length NF-M or NF-H lacking only part of the head domain. No homologous or heterologous dimerization of NF-M and NF-H was detectable. These results support the hypothesis that neurofilaments are obligate heteropolymers and that heterodimeric subunits are the preferred building blocks. They further suggest that the mechanism that specifies heterodimeric interaction among the NF triplet proteins resides in the end domains.

scholarly journals Identifying Hierarchical and Overlapping Protein Complexes Based on Essential Protein-Protein Interactions and “Seed-Expanding” Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Jun Ren ◽  
Wei Zhou ◽  
Jianxin Wang
Keyword(s):  
Protein Interactions ◽  
Time Complexity ◽  
Protein Complexes ◽  
Hierarchical Organization ◽  
Experimental Results ◽  
Functional Enrichment ◽  
Detection Methods ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Ppi Networks

Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based onλ-module and “seed-expanding.” First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to aλ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameterλ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value ofλ_th. Experimental results ofS. cerevisiaeshow that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes.

scholarly journals Integration Strategy Is a Key Step in Network-Based Analysis and Dramatically Affects Network Topological Properties and Inferring Outcomes

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Nana Jin ◽  
Deng Wu ◽  
Yonghui Gong ◽  
Xiaoman Bi ◽  
Hong Jiang ◽  
...  
Keyword(s):  
Molecular Interactions ◽  
Protein Interactions ◽  
Drug Targets ◽  
Molecular Complexes ◽  
Molecular Networks ◽  
Positive Interactions ◽  
Disease Genes ◽  
Topological Properties ◽  
Network Analyses ◽  
Integration Strategies

An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches.

scholarly journals AE-LGBM: Sequence-Based Novel Approach To Detect Interacting Protein Pairs via Ensemble of Autoencoder and LightGBM

2020 ◽  
Author(s):  
Abhibhav Sharma ◽  
Buddha Singh
Keyword(s):  
Protein Interactions ◽  
False Positive Rate ◽  
Experimental Studies ◽  
Core Network ◽  
Interacting Protein ◽  
Ppi Networks ◽  
Novel Approach ◽  
Protein Functions ◽  
Positive Rate ◽  
Process Prediction

1.AbstractProtein-protein interactions (PPIs) are a vital phenomenon for every biological process. Prediction of PPI can be very helpful in the probing of protein functions which can further help in the development of new and powerful therapy designs for disease prevention. A lot of experimental studies have been done previously to study PPIs. However, lab-based experimental studies of PPI prediction are resource-extensive and time-consuming. In recent years, several high throughput, computational approaches to predict PPI have been developed but they could be fallible in terms of accuracy and false-positive rate. To overcome these shortcomings, we propose a novel approach AE-LGBM to predict the PPI more accurately. This method is based on the LightGBM classifier and utilizes the Autoencoder, which is an artificial neural network, to efficiently produce lower-dimensional, discriminative, and noise-free features. We incorporate conjoint triad (CT) features along with Composition-Transition-Distribution (CTD) features into the model and obtained promising results. The ten-fold cross-validation results indicate that the prediction accuracies obtained for Human and Yeast datasets are 98.7% and 95.4% respectively. This method was further evaluated on other datasets and has achieved excellent accuracies of 100%, 100%, 99.9%, 99.2% on E.coli, M.musculus, C.elegans, and H.sapiens respectively. We also executed AE-LGBM over three important PPI networks namely, single-core network (CD9), the multiple-core network (The Ras/Raf/MEK/ERK pathway), and the cross-connection network (Wnt Network). The method was successful in predicting the pathway with an impressive accuracy of 100%, 100%, and 98.9% respectively. These figures are significantly higher than previous methods that are based on state-of-the-art models and models including LightGBM or Autoencoder, proving AE-LGBM to be highly versatile, efficient, and robust.

scholarly journals PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions

2021 ◽  
Vol 17 (8) ◽  
pp. e1008844
Author(s):  
Seyed Ziaeddin Alborzi ◽  
Amina Ahmed Nacer ◽  
Hiba Najjar ◽  
David W. Ritchie ◽  
Marie-Dominique Devignes
Keyword(s):  
Protein Interactions ◽  
Gold Standard ◽  
Statistical Significance ◽  
Building Blocks ◽  
P Value ◽  
Protein Protein Interactions ◽  
Multiple Sources ◽  
Ppi Networks ◽  
Domain Interactions ◽  
Computational Discovery

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing. We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84, 552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9, 175 DDIs), Silver (24, 934 DDIs) and Bronze (50, 443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10, 229 DDIs that are consistent with more than 13, 300 PPIs extracted from the IMEx database, and nearly 23, 300 DDIs (27.5%) that are consistent with more than 214, 000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided. Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.

scholarly journals PPIDomainMiner : Inferring domain-domain interactions from multiple sources of protein-protein interactions

2021 ◽  
Author(s):  
Alborzi Seyed Ziaeddine ◽  
Ahmed-Nacer Amina ◽  
Najjar Hiba ◽  
David W Ritchie ◽  
Devignes Marie-Dominique
Keyword(s):  
Protein Interactions ◽  
Gold Standard ◽  
Statistical Significance ◽  
Weighted Average ◽  
Building Blocks ◽  
Protein Protein Interactions ◽  
Multiple Sources ◽  
Tripartite Graph ◽  
Ppi Networks ◽  
Domain Interactions

AbstractMany biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing.We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84, 552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9, 175 DDIs), Silver (24, 934 DDIs) and Bronze (50, 443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains more than 3, 250 DDIs that are consistent with nearly 10, 600 PPIs extracted from the IMEx database, and more than 23, 000 DDIs (27.5%) that are consistent with more than 62, 000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than ten PPIs in the IMEx database are provided.Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.Author summaryWe revisit at a large scale the question of inferring DDIs from PPIs. Compared to previous studies, we take a unified approach accross multiple sources of PPIs. This approach is a method for inferring new edges in a tripartite graph setting and can be compared to link prediction approaches in knowledge graphs. Aggregation of several sources is performed using an optimized weighted average of the individual scores calculated in each source. A huge dataset of over 84K DDIs is produced which far exceeds the previous datasets. We show that a significant portion of the PPIDM dataset covers a large number of PPIs from curated (IMEx) or non curated (STRING) databases. Such a reservoir of DDIs deserves further exploration and can be combined with high-throughput methods such as cross-linking mass spectrometry to identify plausible protein partners of proteins of interest.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals The Protein Interactome of Glycolysis in Escherichia coli

Proteomes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 16
Author(s):  
Shomeek Chowdhury ◽  
Stephen Hepper ◽  
Mudassir K. Lodi ◽  
Milton H. Saier ◽  
Peter Uetz
Keyword(s):  
Escherichia Coli ◽  
Protein Interactions ◽  
Allosteric Regulation ◽  
Glycolytic Enzymes ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Interacting Protein ◽  
E Coli ◽  
Protein Interactome ◽  
The Impact

Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.

scholarly journals Plasma heat shock protein response to euglycemia in type 2 diabetes

2021 ◽  
Vol 9 (1) ◽  
pp. e002057
Author(s):  
Alexander S Atkin ◽  
Abu Saleh Md Moin ◽  
Ahmed Al-Qaissi ◽  
Thozhukat Sathyapalan ◽  
Stephen L Atkin ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Heat Shock ◽  
Protein Interactions ◽  
Glucose Variability ◽  
Interacting Protein ◽  
Ubiquitin Conjugating Enzyme ◽  
Shock Proteins ◽  
Protein Response ◽  
Design And Methods

IntroductionGlucose variability is associated with mortality and macrovascular diabetes complications. The mechanisms through which glucose variability mediates tissue damage are not well understood, although cellular oxidative stress is likely involved. As heat shock proteins (HSPs) play a role in the pathogenesis of type 2 diabetes (T2D) complications and are rapidly responsive, we hypothesized that HSP-related proteins (HSPRPs) would differ in diabetes and may respond to glucose normalization.Research design and methodsA prospective, parallel study in T2D (n=23) and controls (n=23) was undertaken. T2D subjects underwent insulin-induced blood glucose normalization from baseline 7.6±0.4 mmol/L (136.8±7.2 mg/dL) to 4.5±0.07 mmol/L (81±1.2 mg/dL) for 1 hour. Control subjects were maintained at 4.9±0.1 mmol/L (88.2±1.8 mg/dL). Slow Off-rate Modified Aptamer-scan plasma protein measurement determined a panel of HSPRPs.ResultsAt baseline, E3-ubiquitin-protein ligase (carboxyl-terminus of Hsc70 interacting protein (CHIP) or HSPABP2) was lower (p=0.03) and ubiquitin-conjugating enzyme E2G2 higher (p=0.003) in T2D versus controls. Following glucose normalization, DnaJ homolog subfamily B member 1 (DNAJB1 or HSP40) was reduced (p=0.02) in T2D, with HSP beta-1 (HSPB1) and HSP-70-1A (HSP70-1A) (p=0.07 and p=0.09, respectively) also approaching significance relative to T2D baseline levels.ConclusionsKey HSPRPs involved in critical protein interactions, CHIP and UBE2G2, were altered in diabetes at baseline. DNAJB1 fell in response to euglycemia, suggesting that HSPs are reacting to basal stress that could be mitigated by tight glucose control with reduction of glucose variability.

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