scholarly journals Edgetic Perturbations Contribute to Phenotypic Variability in PEX26 Deficiency

2021 ◽  
Vol 12 ◽  
Author(s):  
Amelie S. Lotz-Havla ◽  
Mathias Woidy ◽  
Philipp Guder ◽  
Jessica Schmiesing ◽  
Ralf Erdmann ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Matrix Protein ◽  
Protein Import ◽  
Phenotypic Variability ◽  
Interaction Network ◽  
Underlying Disease ◽  
Protein Protein Interactions ◽  
Peroxisomal Disorders ◽  
Peroxisomal Biogenesis

Peroxisomes share metabolic pathways with other organelles and peroxisomes are embedded into key cellular processes. However, the specific function of many peroxisomal proteins remains unclear and restricted knowledge of the peroxisomal protein interaction network limits a precise mapping of this network into the cellular metabolism. Inborn peroxisomal disorders are autosomal or X-linked recessive diseases that affect peroxisomal biogenesis (PBD) and/or peroxisomal metabolism. Pathogenic variants in the PEX26 gene lead to peroxisomal disorders of the full Zellweger spectrum continuum. To investigate the phenotypic complexity of PEX26 deficiency, we performed a combined organelle protein interaction screen and network medicine approach and 1) analyzed whether PEX26 establishes interactions with other peroxisomal proteins, 2) deciphered the PEX26 interaction network, 3) determined how PEX26 is involved in further processes of peroxisomal biogenesis and metabolism, and 4) showed how variant-specific disruption of protein-protein interactions (edgetic perturbations) may contribute to phenotypic variability in PEX26 deficient patients. The discovery of 14 novel protein-protein interactions for PEX26 revealed a hub position of PEX26 inside the peroxisomal interactome. Analysis of edgetic perturbations of PEX26 variants revealed a strong correlation between the number of affected protein-protein interactions and the molecular phenotype of matrix protein import. The role of PEX26 in peroxisomal biogenesis was expanded encompassing matrix protein import, division and proliferation, and membrane assembly. Moreover, the PEX26 interaction network intersects with cellular lipid metabolism at different steps. The results of this study expand the knowledge about the function of PEX26 and refine genotype-phenotype correlations, which may contribute to our understanding of the underlying disease mechanism of PEX26 deficiency.

Protein interaction studies for understanding the tremor pathway in Parkinson’s disease.

2020 ◽  
Vol 19 ◽  
Author(s):  
Nitu Dogra ◽  
Ruchi Jakhmola Mani ◽  
Deepshikha Pande Katare
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Literature Mining ◽  
Protein Protein Interactions ◽  
Calcium Ion Channels ◽  
Progression Of Disease ◽  
Different Sources

Background: Tremor is one of the most noticeable features, which occurs during the early stages of Parkinson’s disease (PD). It is one of the major pathological hallmarks and does not have any interpreted mechanism. In this study we have framed a hypothesis and deciphered protein-protein interactions between the proteins involved in impairment in sodium and calcium ion channels and thus cause synaptic plasticity leading to a tremor. Methods: Literature mining for retrieval of proteins was done using Science Direct, PubMed Central, SciELO and JSTOR databases. A well thought approach was used and a list of differentially expressed proteins in PD was collected from different sources. A total of 71 proteins were retrieved and a protein interaction network was constructed between them by using Cytoscape.v.3.7. The network was further analysed using BiNGO plugin for retrieval of overrepresented biological processes in Tremor-PD datasets. Hub nodes were also generated in the network. Results: The Tremor-PD pathway was deciphered which demonstrates the cascade of protein interactions that might lead to tremors in PD. Major proteins involved were LRRK2, TUBA1A, TRAF6, HSPA5, ADORA2A, DRD1, DRD2, SNCA, ADCY5, TH etc. Conclusion: In the current study it is predicted that ADORA2A and DRD1/DRD2 are equally contributing to the progression of disease by inhibiting the activity of adenylyl cyclase and thereby increases the permeability of the blood brain barrier causing an influx of neurotransmitters and together they alter the level of dopamine in the brain which eventually leads to tremor.

scholarly journals Interactome Data and Databases: Different Types of Protein Interaction

2004 ◽  
Vol 5 (2) ◽  
pp. 173-178 ◽  
Author(s):  
Javier De Las Rivas ◽  
Alberto de Luis
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Short Review ◽  
Experimental Methods ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Bioinformatic Tool ◽  
Different Types

In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of ‘protein–protein interactions’, i.e. the ‘interactome’. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of ‘what a protein–protein interaction is’ and ‘which types of protein–protein interactions are found in a living cell’. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.

scholarly journals Deciphering biological evolution exploiting the topology of Protein Locality Graph

2021 ◽  
Author(s):  
Barnali Das ◽  
Pralay Mitra
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Biological Networks ◽  
Biological Evolution ◽  
Interaction Network ◽  
Computational Time ◽  
Protein Protein Interactions ◽  
Power Graph ◽  
New Evidence ◽  
Protein Modules

The conventional sequence comparison-based evolutionary studies ignore other evolutionary constraints like interaction among proteins, functions of proteins and genes etc. A lot of speculations exist in literature regarding the presence of species divergence at the level of the Protein Interaction Networks. Additionally, it has been conjectured that the intra-module connections stay conserved whereas the inter-module connections change during evolution. The most important components of the biological networks are the functional modules which are more conserved among the evolutionary closer species. Here, we demonstrate an alternative method to decipher biological evolution by exploiting the topology of a spatially localized Protein Interaction Network called Protein Locality Graph (PLG). Our lossless graph compression from PLG to a power graph called Protein Cluster Interaction Network (PCIN) results in a 90% size reduction and aids in improving computational time. Further, we exploit the topology of PCIN and demonstrate our capability of deriving the correct species tree by focusing on the cross-talk between the protein modules exclusively. Our results provide new evidence that traces of evolution are not only present at the level of the Protein-Protein Interactions, but are also very much present at the level of the inter-module interactions.

scholarly journals Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Peng Liu ◽  
Lei Yang ◽  
Daming Shi ◽  
Xianglong Tang
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Statistical Tests ◽  
Interaction Network ◽  
Biological Data ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Small Parts

A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptivek-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

IDENTIFICATION OF ESSENTIAL PROTEINS FROM WEIGHTED PROTEIN–PROTEIN INTERACTION NETWORKS

2013 ◽  
Vol 11 (03) ◽  
pp. 1341002 ◽  
Author(s):  
MIN LI ◽  
JIAN-XIN WANG ◽  
HUAN WANG ◽  
YI PAN
Keyword(s):  
Protein Interaction ◽  
Network Topology ◽  
Protein Interactions ◽  
Interaction Network ◽  
False Positives ◽  
Centrality Measures ◽  
Protein Protein Interactions ◽  
Weighting Method ◽  
Essential Proteins ◽  
Protein Essentiality

Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein–protein interactions has produced unprecedented opportunities for detecting protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. Unfortunately, the protein–protein interactions produced by high-throughput experiments generally have high false positives. Moreover, most of centrality measures based on network topology are sensitive to false positives. We therefore propose a new method for evaluating the confidence of each interaction based on the combination of logistic regression-based model and function similarity. Nine standard centrality measures in weighted network were redefined in this paper. The experimental results on a yeast protein interaction network shows that the weighting method improved the performance of centrality measures considerably. More essential proteins were discovered by the weighted centrality measures than by the original centrality measures used in the unweighted network. Even about 20% improvements were obtained from closeness centrality and subgraph centrality.

scholarly journals Evolution of Protein-protein Interaction Networks in Duplication-Divergence Model

2012 ◽  
Vol 22 (1) ◽  
pp. 7-14
Author(s):  
Bui Phuong Thuy ◽  
Trinh Xuan Hoang
Keyword(s):  
Experimental Data ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Living Organisms

Protein interacts with one another resulting in complex functions in living organisms. Like many other real-world networks, the networks of protein-protein interactions possess a certain degree of ordering, such as the scale-free property. The latter means that the probability $P$ to find a protein that interacts with $k$ other proteins follows a power law, $P(k) \sim k^{-\gamma}$. Protein interaction networks (PINs) have been studied by using a stochastic model, the duplication-divergence model, which is based on mechanisms of gene duplication and divergence during evolution. In this work, we show that this model can be used to fit experimental data on the PIN of yeast Saccharomyces cerevisae at two different time instances simultaneously. Our study shows that the evolution of PIN given by model is consistent with growing experimental data over time, and that the scale-free property of protein interaction network is robust against random deletion of interactions.

An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Yu-Miao Zhang ◽  
Jun Wang ◽  
Tao Wu
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Epidermal Cells ◽  
Cyclin Dependent Kinase ◽  
Protein Subcellular Localization ◽  
Protein Protein Interactions ◽  
Efficient System ◽  
Protein Protein Interaction ◽  
Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

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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