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 Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1271
Author(s):  
Hoyeon Jeong ◽  
Yoonbee Kim ◽  
Yi-Sue Jung ◽  
Dae Ryong Kang ◽  
Young-Rae Cho
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Graph Clustering ◽  
Functional Modules ◽  
Protein Protein Interactions ◽  
Overlapping Clusters ◽  
Novel Proteins ◽  
Ppi Networks ◽  
Function Modules

Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

scholarly journals Network Pharmacology study on the mechanism of MKA Polyherbal Formulation in combating Respiratory Diseases

2020 ◽  
Vol 9 (6) ◽  
pp. 385-391
Author(s):  
T Poongodi ◽  
◽  
TH Nazeema ◽  
Keyword(s):  
Mechanism Of Action ◽  
Protein Interactions ◽  
Respiratory Diseases ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Egfr Inhibition ◽  
Protein Protein Interaction ◽  
Polyherbal Formulation

The Multi-targeted action of Polyherbal formulation is responsible for enhanced therapeutic efficacy in combating various diseases. But, understanding the mode of action of herbal medicine remains a challenge because of its complex metabolomics. Network pharmacology-based approach enables to explore the mechanism of action of polyherbal formulation in biological system. In present investigation, we have explored the molecular mechanism of action of the Polyherbal formulation MKA comprising of three botanicals Mimusops elengi L., Kedrostis foetidissima (Jacq.) Cogn. and Artemisia vulgaris L. in treating respiratory diseases by network pharmacology-based approach. The protein targets were mined from Binding database for the bioactive present in MKA. The disease associated targets were identified using Open target Platform. Based on ligand-target interactions, it was interpreted that MKA could alleviate the symptoms of respiratory disease by multiple mechanisms like EGFR inhibition by Quercetin and Quercetin-3-O-rhamnoside, KDR inhibition by Quercetin, STAT-3 inhibition by β-sitosterol- β-Dglucoside, TRPV1 inhibition by phytol acetate, etc. The Protein-protein interaction (PPI) network was constructed using STRING database. KEGG pathway based functional enrichment was also predicted for the PPI network. It was found that multiple ligand-target interactions and protein-protein interactions is responsible for pharmacological activity of MKA in respiratory diseases.

scholarly journals From the static interactome to dynamic protein complexes: Three challenges

2015 ◽  
Vol 13 (02) ◽  
pp. 1571001 ◽  
Author(s):  
Chern Han Yong ◽  
Limsoon Wong
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Static Interaction ◽  
Ppi Networks ◽  
Interaction Screening ◽  
Discovery Algorithms ◽  
Insight Into

Protein interactions and complexes behave in a dynamic fashion, but this dynamism is not captured by interaction screening technologies, and not preserved in protein–protein interaction (PPI) networks. The analysis of static interaction data to derive dynamic protein complexes leads to several challenges, of which we identify three. First, many proteins participate in multiple complexes, leading to overlapping complexes embedded within highly-connected regions of the PPI network. This makes it difficult to accurately delimit the boundaries of such complexes. Second, many condition- and location-specific PPIs are not detected, leading to sparsely-connected complexes that cannot be picked out by clustering algorithms. Third, the majority of complexes are small complexes (made up of two or three proteins), which are extra sensitive to the effects of extraneous edges and missing co-complex edges. We show that many existing complex-discovery algorithms have trouble predicting such complexes, and show that our insight into the disparity between the static interactome and dynamic protein complexes can be used to improve the performance of complex discovery.

scholarly journals Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Lei Yang ◽  
Xianglong Tang
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Protein Complexes ◽  
False Negative ◽  
Biological Database ◽  
Interacting Proteins ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Biological Meaning

Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

scholarly journals The Interactions of Small Proline-rich Proteins With Late Cornified Envelope Proteins Are Involved in the Pathogenesis of Psoriasis

2021 ◽  
Author(s):  
Siyu Tian ◽  
Shuming Chen ◽  
Yongyi Feng ◽  
Yong Li
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Protein Interactions ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Expression Levels ◽  
Cornified Envelope

Abstract Background: Psoriasis is a common cutaneous disease with many characteristics including inflammation and aberrant keratinocyte proliferation. However, the pathogenesis of psoriasis is not completely clear. Methods: We explore the differentially expressed genes (DEGs) in psoriasis by analyzing the gene expression profile obtained from the Gene Expression Omnibus (GEO) database. The DEGs were examined by gene ontology (GO) functional enrichment analysis and protein-protein interactions (PPI) network. Correlation analysis in R studio software analyzed the association of SPRR and LCE genes. The potential direct protein-protein interactions between SPRR proteins and LCE3D was further verified by co-localization observed in 293T cells and co-immunoprecipitation (CO-IP). The expression levels of SPRR and LCE genes were detected in the IMQ-induced psoriasiform dermatitis mice. Results: The small proline-rich (SPRR) and late cornified envelope (LCE) genes were identified as a module in constructed PPI network. The gene expression profile GSE63684 analysis showed that both SPRR family and LCE family genes were significantly upregulated in imiquimod (IMQ) induced psoriasiform dermatitis mice. Correlation analysis in R studio software recognized the association of SPRR and LCE genes, in which the potential direct protein-protein interactions between SPRR proteins and LCE3D was further verified by co-localization observed in 293T cells and co-immunoprecipitation (CO-IP) results that suggest direct interaction between SPRR2 and LCE3D. Notably, we found that the expression levels of SPRR and LCE genes were significantly increased in the IMQ-induced psoriasiform dermatitis mice while tazarotene cream treatment specifically decreased the mRNA expression of these genes, which indicated that the SPRR and LCEs were regulated simultaneously in psoriasis. Conclusion: Our studies found the interactions of SPRR proteins with LCE proteins, which may provide new insights into the pathogenesis of psoriasis.

scholarly journals USING INDIRECT PROTEIN–PROTEIN INTERACTIONS FOR PROTEIN COMPLEX PREDICTION

2008 ◽  
Vol 06 (03) ◽  
pp. 435-466 ◽  
Author(s):  
HON NIAN CHUA ◽  
KANG NING ◽  
WING-KIN SUNG ◽  
HON WAI LEONG ◽  
LIMSOON WONG
Keyword(s):  
Protein Interactions ◽  
Protein Complex ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Indirect Interactions ◽  
Protein Protein Interactions ◽  
Protein Complex Prediction ◽  
Ppi Networks ◽  
Level 2 ◽  
Novel Protein

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein–protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein–protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Modular Architecture ◽  
Post Translational Modifications ◽  
Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

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 Frequent assembly of chimeric complexes in the protein interaction network of an interspecies yeast hybrid

2020 ◽  
Author(s):  
Rohan Dandage ◽  
Caroline M Berger ◽  
Isabelle Gagnon-Arsenault ◽  
Kyung-Mee Moon ◽  
Richard Greg Stacey ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Molecular Level ◽  
Yeast Species ◽  
Protein Complexes ◽  
Mitochondrial Protein ◽  
Chimeric Protein ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Extreme Phenotypes ◽  
Yeast Hybrid

Abstract Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein-protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein-protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein-protein interactions in the hybrid, for instance in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteins.

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