Peripheral Membrane Proteins

1996 ◽  
pp. 355-403 ◽  
Author(s):  
Barbara A. Seaton ◽  
Mary F. Roberts
Physchem ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 152-162
Author(s):  
Miquel Pons

A large number of peripheral membrane proteins transiently interact with lipids through a combination of weak interactions. Among them, electrostatic interactions of clusters of positively charged amino acid residues with negatively charged lipids play an important role. Clusters of charged residues are often found in intrinsically disordered protein regions, which are highly abundant in the vicinity of the membrane forming what has been called the disordered boundary of the cell. Beyond contributing to the stability of the lipid-bound state, the pattern of charged residues may encode specific interactions or properties that form the basis of cell signaling. The element of this code may include, among others, the recognition, clustering, and selective release of phosphatidyl inositides, lipid-mediated protein-protein interactions changing the residence time of the peripheral membrane proteins or driving their approximation to integral membrane proteins. Boundary effects include reduction of dimensionality, protein reorientation, biassing of the conformational ensemble of disordered regions or enhanced 2D diffusion in the peri-membrane region enabled by the fuzzy character of the electrostatic interactions with an extended lipid membrane.


Langmuir ◽  
2017 ◽  
Vol 33 (26) ◽  
pp. 6572-6580 ◽  
Author(s):  
Raphael Dos Santos Morais ◽  
Olivier Delalande ◽  
Javier Pérez ◽  
Liza Mouret ◽  
Arnaud Bondon ◽  
...  

2000 ◽  
Vol 11 (4) ◽  
pp. 1421-1432 ◽  
Author(s):  
Ozlem Ugur ◽  
Teresa L. Z. Jones

XLαs is a splice variant of the heterotrimeric G protein, Gαs, found on Golgi membranes in cells with regulated and constitutive secretion. We examined the role of the alternatively spliced amino terminus of XLαs for Golgi targeting with the use of subcellular fractionation and fluorescence microscopy. XLαs incorporated [3H]palmitate, and mutation of cysteines in a cysteine-rich region inhibited this incorporation and lessened membrane attachment. Deletion of a proline-rich region abolished Golgi localization of XLαs without changing its membrane attachment. The proline-rich and cysteine-rich regions together were sufficient to target the green fluorescent protein, a cytosolic protein, to Golgi membranes. The membrane attachment and Golgi targeting of the fusion protein required the putative palmitoylation sites, the cysteine residues in the cysteine-rich region. Several peripheral membrane proteins found at the Golgi have proline-rich regions, including a Gαi2 splice variant, dynamin II, βIII spectrin, comitin, and a Golgi SNARE, GS32. Our results suggest that proline-rich regions can be a Golgi-targeting signal for G protein α subunits and possibly for other peripheral membrane proteins as well.


1999 ◽  
Vol 9 (22) ◽  
pp. R857-R860 ◽  
Author(s):  
Paul C. Driscoll ◽  
Anne-Lise Vuidepot

2011 ◽  
Vol 7 (6) ◽  
pp. e1002067 ◽  
Author(s):  
Diana Morozova ◽  
Gernot Guigas ◽  
Matthias Weiss

2021 ◽  
Author(s):  
Alexios Chatzigoulas ◽  
Zoe Cournia

Motivation: Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane binding domains of peripheral membrane proteins are usually not known. The development of fast and efficient algorithms predicting the protein-membrane interface would shed light into the accessibility of membrane-protein interfaces by drug-like molecules. Results: Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating residues. We utilize available experimental data in the literature for training 21 machine learning classifiers and a voting classifier. Evaluation of the ensemble classifier accuracy produced a macro-averaged F1 score = 0.92 and an MCC = 0.84 for predicting correctly membrane-penetrating residues on unknown proteins of an independent test set. Availability and implementation: The python code for predicting protein-membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.


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