scholarly journals The Dawn of Mitophagy: What Do We Know by Now?

2020 ◽  
Vol 19 (2) ◽  
pp. 170-192
Author(s):  
Dmitrii M. Belousov ◽  
Elizaveta V. Mikhaylenko ◽  
Siva G. Somasundaram ◽  
Cecil E. Kirkland ◽  
Gjumrakch Aliev
Keyword(s):  
Protein Interactions ◽  
Mammalian Target Of Rapamycin ◽  
Mitochondrial Damage ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Mitochondrial Depolarization ◽  
Cell Protection ◽  
Antioxidant Genes ◽  
Protein Protein Interaction ◽  
Research Findings

Mitochondria are essential organelles for healthy eukaryotic cells. They produce energyrich phosphate bond molecules (ATP) through oxidative phosphorylation using ionic gradients. The presence of mitophagy pathways in healthy cells enhances cell protection during mitochondrial damage. The PTEN-induced putative kinase 1 (PINK1)/Parkin-dependent pathway is the most studied for mitophage. In addition, there are other mechanisms leading to mitophagy (FKBP8, NIX, BNIP3, FUNDC1, BCL2L13). Each of these provides tethering of a mitochondrion to an autophagy apparatus via the interaction between receptor proteins (Optineurin, p62, NDP52, NBR1) or the proteins of the outer mitochondrial membrane with ATG9-like proteins (LC3A, LC3B, GABARAP, GABARAPL1, GATE16). Another pathogenesis of mitochondrial damage is mitochondrial depolarization. Reactive oxygen species (ROS) antioxidant responsive elements (AREs) along with antioxidant genes, including pro-autophagic genes, are all involved in mitochondrial depolarization. On the other hand, mammalian Target of Rapamycin Complex 1 (mTORC1) and AMP-dependent kinase (AMPK) are the major regulatory factors modulating mitophagy at the post-translational level. Protein-protein interactions are involved in controlling other mitophagy processes. The objective of the present review is to analyze research findings regarding the main pathways of mitophagy induction, recruitment of the autophagy machinery, and their regulations at the levels of transcription, post-translational modification and protein-protein interaction that appeared to be the main target during the development and maturation of neurodegenerative disorders.

scholarly journals In Silico Analysis Predicting Effects of Deleterious SNPs of Human RASSF5 Gene on its Structure and Functions

2020 ◽  
Author(s):  
Md. Shahadat Hossain ◽  
Arpita Singha Roy ◽  
Md. Sajedul Islam
Keyword(s):  
Protein Interactions ◽  
In Silico ◽  
Structural Changes ◽  
Interaction Analysis ◽  
In Silico Analysis ◽  
Therapeutic Interventions ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Protein Protein Interaction ◽  
The Stability

AbstractRas association domain-containing protein 5 (RASSF5), one of the prospective biomarkers for tumors, generally plays a crucial role as a tumor suppressor. As deleterious effects can result from functional differences through SNPs, we sought to analyze the most deleterious SNPs of RASSF5 as well as predict the structural changes associated with the mutants that hamper the normal protein-protein interactions. We adopted both sequence and structure based approaches to analyze the SNPs of RASSF5 protein. We also analyzed the putative post translational modification sites as well as the altered protein-protein interactions that encompass various cascades of signals. Out of all the SNPs obtained from the NCBI database, only 25 were considered as highly deleterious by six in silico SNP prediction tools. Among them, upon analyzing the effect of these nsSNPs on the stability of the protein, we found 17 SNPs that decrease the stability. Significant deviation in the energy minimization score was observed in P350R, F321L, and R277W. Besides this, docking analysis confirmed that P350R, A319V, F321L, and R277W reduce the binding affinity of the protein with H-Ras, where P350R shows the most remarkable deviation. Protein-protein interaction analysis revealed that RASSF5 acts as a hub connecting two clusters consisting of 18 proteins and alteration in the RASSF5 may lead to disassociation of several signal cascades. Thus, based on these analyses, our study suggests that the reported functional SNPs may serve as potential targets for different proteomic studies, diagnosis and therapeutic interventions.

scholarly journals Sulfotyrosine, an interaction specificity determinant for extracellular protein-protein interactions

2021 ◽  
Author(s):  
Valley Stewart ◽  
Pamela C. Ronald
Keyword(s):  
Protein Interactions ◽  
Chemokine Receptors ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Protein Protein Interaction ◽  
Coreceptor Binding ◽  
Receptor Interactions ◽  
Technical Advances ◽  
Affinity Peptide ◽  
Hiv 1

Tyrosine sulfation, a post-translational modification, can enhance and often determine protein-protein interaction specificity. Sulfotyrosyl residues (sTyr) are formed by tyrosyl-protein sulfotransferases (TPSTs) during maturation of certain secreted proteins. Here we consider three contexts for sTyr function. First, a single sTyr residue is critical for high-affinity peptide-receptor interactions in plant peptide hormones and animal receptors for glycopeptide hormones. Second, structurally flexible anionic segments often contain a cluster of two or three sTyr residues within a six-residue span. These sTyr residues are essential for coreceptor binding of the HIV-1 envelope spike protein during virus entry and for chemokine interactions with many chemokine receptors. Third, several proteins that interact with thrombin, central to normal blood-clotting, require the presence of sTyr residues in the context of acidic sequences termed hirudin-like motifs. Consequently, many proven and potential therapeutic proteins derived from blood-consuming invertebrates depend on sTyr residues for their activity. Technical advances in generating and documenting site-specific sTyr substitutions facilitate discovery and analysis, and promise to enable engineering of defined interaction determinants.

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.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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 Androgen signaling uses a writer and a reader of ADP-ribosylation to regulate protein complex assembly

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chun-Song Yang ◽  
Kasey Jividen ◽  
Teddy Kamata ◽  
Natalia Dworak ◽  
Luke Oostdyk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Prostate Cancer Cells ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Complex Assembly ◽  
Adp Ribosylation ◽  
Signaling Axis ◽  
Regulate Protein ◽  
Protein Complex Assembly

AbstractAndrogen signaling through the androgen receptor (AR) directs gene expression in both normal and prostate cancer cells. Androgen regulates multiple aspects of the AR life cycle, including its localization and post-translational modification, but understanding how modifications are read and integrated with AR activity has been difficult. Here, we show that ADP-ribosylation regulates AR through a nuclear pathway mediated by Parp7. We show that Parp7 mono-ADP-ribosylates agonist-bound AR, and that ADP-ribosyl-cysteines within the N-terminal domain mediate recruitment of the E3 ligase Dtx3L/Parp9. Molecular recognition of ADP-ribosyl-cysteine is provided by tandem macrodomains in Parp9, and Dtx3L/Parp9 modulates expression of a subset of AR-regulated genes. Parp7, ADP-ribosylation of AR, and AR-Dtx3L/Parp9 complex assembly are inhibited by Olaparib, a compound used clinically to inhibit poly-ADP-ribosyltransferases Parp1/2. Our study reveals the components of an androgen signaling axis that uses a writer and reader of ADP-ribosylation to regulate protein-protein interactions and AR activity.

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 Universal Screening Methods and Applications of ThermoFluor®

2006 ◽  
Vol 11 (7) ◽  
pp. 854-863 ◽  
Author(s):  
Maxwell D. Cummings ◽  
Michael A. Farnum ◽  
Marina I. Nelen
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Unfolding ◽  
Direct Detection ◽  
Functional Characterization ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Bacterial Enzyme ◽  
Research Problems

The genomics revolution has unveiled a wealth of poorly characterized proteins. Scientists are often able to produce milligram quantities of proteins for which function is unknown or hypothetical, based only on very distant sequence homology. Broadly applicable tools for functional characterization are essential to the illumination of these orphan proteins. An additional challenge is the direct detection of inhibitors of protein-protein interactions (and allosteric effectors). Both of these research problems are relevant to, among other things, the challenge of finding and validating new protein targets for drug action. Screening collections of small molecules has long been used in the pharmaceutical industry as 1 method of discovering drug leads. Screening in this context typically involves a function-based assay. Given a sufficient quantity of a protein of interest, significant effort may still be required for functional characterization, assay development, and assay configuration for screening. Increasingly, techniques are being reported that facilitate screening for specific ligands for a protein of unknown function. Such techniques also allow for function-independent screening with better characterized proteins. ThermoFluor®, a screening instrument based on monitoring ligand effects on temperature-dependent protein unfolding, can be applied when protein function is unknown. This technology has proven useful in the decryption of an essential bacterial enzyme and in the discovery of a series of inhibitors of a cancer-related, protein-protein interaction. The authors review some of the tools relevant to these research problems in drug discovery, and describe our experiences with 2 different proteins.

scholarly journals Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dan Tan ◽  
Qiang Li ◽  
Mei-Jun Zhang ◽  
Chao Liu ◽  
Chengying Ma ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Structural Information ◽  
Affinity Purification ◽  
Protein Protein Interactions ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Lysine Residues ◽  
Spacer Arm ◽  
Cross Linker ◽  
Protein Nucleic Acid

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

2021 ◽  
Author(s):  
Qianmu Yuan ◽  
Jianwen Chen ◽  
Huiying Zhao ◽  
Yaoqi Zhou ◽  
Yuedong Yang
Keyword(s):  
Protein Interactions ◽  
Spatial Information ◽  
Screening Tools ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Convolutional Network ◽  
Source Codes ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

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