scholarly journals Network analysis reveals how lipids and other cofactors influence membrane protein allostery

2020 ◽  
Vol 153 (14) ◽  
pp. 141103
Author(s):  
Annie M. Westerlund ◽  
Oliver Fleetwood ◽  
Sergio Pérez-Conesa ◽  
Lucie Delemotte
Keyword(s):  
Network Analysis ◽  
Membrane Protein ◽  
Protein Allostery

scholarly journals Network analysis reveals how lipids and other cofactors influence membrane protein allostery

2020 ◽  
Author(s):  
Annie M. Westerlund ◽  
Oliver Fleetwood ◽  
Sergio Perez-Conesa ◽  
Lucie Delemotte
Keyword(s):  
Membrane Proteins ◽  
Membrane Protein ◽  
Ion Channel ◽  
Small Molecules ◽  
Interaction Networks ◽  
Fast Diffusion ◽  
Systematic Analysis ◽  
Protein Allostery ◽  
Residue Interaction ◽  
Transmembrane Voltage

AbstractMany membrane proteins are modulated by external stimuli, such as small molecule binding or change in pH, transmembrane voltage or temperature. This modulation typically occurs at sites that are structurally distant from the functional site. Revealing the communication, known as allostery, between these two sites is key to understanding the mechanistic details of these proteins. Residue interaction networks of isolated proteins are commonly used to this end. Membrane proteins, however, are embedded in a lipid bilayer which may contribute to allosteric communication. The fast diffusion of lipids hinders direct use of standard residue interaction networks. Here, we present an extension which includes cofactors such as lipids and small molecules in the network. The novel framework is applied to three membrane proteins: a voltage-gated ion channel (KCNQ1), a G-protein coupled receptor (GPCR - β2 adrenergic receptor) and a pH-gated ion channel (KcsA). Through systematic analysis of the obtained networks and their components, we demonstrate the importance of lipids for membrane protein allostery. Finally, we reveal how small molecules may stabilize different protein states by allosterically coupling and decoupling the protein from the membrane.

TMCC3 localizes at the three-way junctions for the proper tubular network of the endoplasmic reticulum

2019 ◽  
Vol 476 (21) ◽  
pp. 3241-3260
Author(s):  
Sindhu Wisesa ◽  
Yasunori Yamamoto ◽  
Toshiaki Sakisaka
Keyword(s):  
Endoplasmic Reticulum ◽  
Membrane Proteins ◽  
Membrane Protein ◽  
Mammalian Cells ◽  
Coiled Coil ◽  
Transmembrane Domains ◽  
Domain Family ◽  
Coiled Coil Domain ◽  
U2os Cells ◽  
Er Membrane

The tubular network of the endoplasmic reticulum (ER) is formed by connecting ER tubules through three-way junctions. Two classes of the conserved ER membrane proteins, atlastins and lunapark, have been shown to reside at the three-way junctions so far and be involved in the generation and stabilization of the three-way junctions. In this study, we report TMCC3 (transmembrane and coiled-coil domain family 3), a member of the TEX28 family, as another ER membrane protein that resides at the three-way junctions in mammalian cells. When the TEX28 family members were transfected into U2OS cells, TMCC3 specifically localized at the three-way junctions in the peripheral ER. TMCC3 bound to atlastins through the C-terminal transmembrane domains. A TMCC3 mutant lacking the N-terminal coiled-coil domain abolished localization to the three-way junctions, suggesting that TMCC3 localized independently of binding to atlastins. TMCC3 knockdown caused a decrease in the number of three-way junctions and expansion of ER sheets, leading to a reduction of the tubular ER network in U2OS cells. The TMCC3 knockdown phenotype was partially rescued by the overexpression of atlastin-2, suggesting that TMCC3 knockdown would decrease the activity of atlastins. These results indicate that TMCC3 localizes at the three-way junctions for the proper tubular ER network.

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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