Mutations and Protein Interaction Landscape Reveal Key Cellular Events Perturbed in Upper Motor Neurons with HSP and PLS

Hereditary spastic paraplegia (HSP) and primary lateral sclerosis (PLS) are rare motor neuron diseases, which affect mostly the upper motor neurons (UMNs) in patients. The UMNs display early vulnerability and progressive degeneration, while other cortical neurons mostly remain functional. Identification of numerous mutations either directly linked or associated with HSP and PLS begins to reveal the genetic component of UMN diseases. Since each of these mutations are identified on genes that code for a protein, and because cellular functions mostly depend on protein-protein interactions, we hypothesized that the mutations detected in patients and the alterations in protein interaction domains would hold the key to unravel the underlying causes of their vulnerability. In an effort to bring a mechanistic insight, we utilized computational analyses to identify interaction partners of proteins and developed the protein-protein interaction landscape with respect to HSP and PLS. Protein-protein interaction domains, upstream regulators and canonical pathways begin to highlight key cellular events. Here we report that proteins involved in maintaining lipid homeostasis and cytoarchitectural dynamics and their interactions are of great importance for UMN health and stability. Their perturbation may result in neuronal vulnerability, and thus maintaining their balance could offer therapeutic interventions.

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RING fingers and B-boxes: zinc-binding protein-protein interaction domains

Biochemistry and Cell Biology ◽

10.1139/o98-021 ◽

1998 ◽

Vol 76 (2-3) ◽

pp. 351-358 ◽

Cited By ~ 140

Author(s):

Katherine LB Borden

Keyword(s):

Conformational Changes ◽

Protein Interaction ◽

Protein Interactions ◽

Structural Features ◽

Zinc Binding ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Biological Studies ◽

Interaction Domains ◽

Nmr Methods

The cysteine-rich zinc-binding motifs known as the RING and B-box are found in several unrelated proteins. Structural, biochemical, and biological studies of these motifs reveal that they mediate protein-protein interactions. Several RING-containing proteins are oncoproteins and recent data indicate that proapoptotic activities can be mediated through the RING. 1H NMR methods were used to determine the structures of RINGs and a B-box domain and to monitor the conformational changes these motifs undergo upon zinc ligation. This review discusses in detail the structural features of the RING and B-box domains. Further, possible structure function relationships for these motifs particularly in their role as protein interaction domains are discussed.Key words: RING, B-box, PML, NMR.

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

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.80.3.17 ◽

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.

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

Current Medicinal Chemistry ◽

10.2174/0929867326666190620101637 ◽

2020 ◽

Vol 27 (37) ◽

pp. 6306-6355 ◽

Cited By ~ 2

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.

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Furan warheads for covalent trapping of weak protein-protein interactions: cross-linking of thymosin β4 to actin

Chemical Communications ◽

10.1039/d1cc01731d ◽

2021 ◽

Author(s):

Laia Miret Casals ◽

Willem Vannecke ◽

Kurt Hoogewijs ◽

Gianluca Arauz ◽

Marina Gay ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

3D Structure ◽

Cross Linking ◽

Thymosin Β4 ◽

Protein Protein Interactions ◽

Site Specific ◽

Protein Protein Interaction ◽

Covalent Trapping

We describe furan as a triggerable ‘warhead’ for site-specific cross-linking using the actin and thymosin β4 (Tβ4)-complex as model of a weak and dynamic protein-protein interaction with known 3D structure...

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Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2015100103 ◽

2015 ◽

Vol 4 (4) ◽

pp. 35-51 ◽

Cited By ~ 1

Author(s):

Bandana Barman ◽

Anirban Mukhopadhyay

Keyword(s):

Differentially Expressed Genes ◽

Protein Interaction ◽

Protein Interactions ◽

Protein Interaction Network ◽

Interaction Network ◽

Differentially Expressed ◽

Wild Type ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

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Discovering Interaction Motifs from Protein Interaction Networks

Biological Data Mining in Protein Interaction Networks ◽

10.4018/978-1-60566-398-2.ch007 ◽

2009 ◽

pp. 99-116 ◽

Cited By ~ 1

Author(s):

Hugo Willy

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Interaction Data ◽

Interaction Data ◽

Protein Patterns ◽

Protein Protein Interaction ◽

Family Based ◽

High Throughput Experiments

Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction data. However, most of the experiments could only answer the question of whether two proteins interact but not the question on the mechanisms by which proteins interact. Such understanding is crucial for understanding the protein interaction of an organism as a whole (the interactome) and even predicting novel protein interactions. Protein interaction usually occurs at some specific sites on the proteins and, given their importance, they are usually well conserved throughout the evolution of the proteins of the same family. Based on this observation, a number of works on finding protein patterns/motifs conserved in interacting proteins have emerged in the last few years. Such motifs are collectively termed as the interaction motifs. This chapter provides a review on the different approaches on finding interaction motifs with a discussion on their implications, potentials and possible areas of improvements in the future.

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Proximal Protein Interaction Landscape of RAS Paralogs

Cancers ◽

10.3390/cancers12113326 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3326 ◽

Cited By ~ 1

Author(s):

Benoît Béganton ◽

Etienne Coyaud ◽

Estelle M. N. Laurent ◽

Alain Mangé ◽

Julien Jacquemetton ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Biological Activities ◽

Mammalian Species ◽

Variable Region ◽

Specific Protein ◽

Protein Networks ◽

Ras Proteins ◽

Wild Type ◽

Protein Protein Interaction

RAS proteins (KRAS, NRAS and HRAS) are frequently activated in different cancer types (e.g., non-small cell lung cancer, colorectal cancer, melanoma and bladder cancer). For many years, their activities were considered redundant due to their high degree of sequence homology (80% identity) and their shared upstream and downstream protein partners. However, the high conservation of the Hyper-Variable-Region across mammalian species, the preferential activation of different RAS proteins in specific tumor types and the specific post-translational modifications and plasma membrane-localization of each paralog suggest they could ensure discrete functions. To gain insights into RAS proteins specificities, we explored their proximal protein–protein interaction landscapes using the proximity-dependent biotin identification technology (BioID) in Flp-In T-REx 293 cell lines stably transfected and inducibly expressing wild type KRAS4B, NRAS or HRAS. We identified more than 800 high-confidence proximal interactors, allowing us to propose an unprecedented comparative analysis of wild type RAS paralogs protein networks. These data bring novel information on poorly characterized RAS functions, e.g., its putative involvement in metabolic pathways, and on shared as well as paralog-specific protein networks that could partially explain the complexity of RAS functions. These networks of protein interactions open numerous avenues to better understand RAS paralogs biological activities.

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HMNPPID—human malignant neoplasm protein–protein interaction database

Human Genomics ◽

10.1186/s40246-019-0223-5 ◽

2019 ◽

Vol 13 (S1) ◽

Author(s):

Qingqing Li ◽

Zhihao Yang ◽

Zhehuan Zhao ◽

Ling Luo ◽

Zhiheng Li ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Malignant Neoplasm ◽

Biomedical Literature ◽

Malignant Neoplasms ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Interaction Database ◽

Protein Interaction Database

Abstract Background Protein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. Results In this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. Conclusions HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

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