scholarly journals Molecular interactions of the M and E integral membrane proteins of SARS-CoV-2

2021 ◽  
Author(s):  
Viviana Monje-Galvan ◽  
Gregory A. Voth
Keyword(s):  
Membrane Proteins ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Integral Membrane Proteins ◽  
Protein Protein Interactions ◽  
Novel Treatments ◽  
Cellular Processes ◽  
Ongoing Work ◽  
Lipid Environment ◽  
Lipid Protein Interactions

AbstractSpecific lipid-protein interactions are key for cellular processes, and even more so for the replication of pathogens. The COVID-19 pandemic has drastically changed our lives and cause the death of nearly three million people worldwide, as of this writing. SARS-CoV-2 is the virus that causes the disease and has been at the center of scientific research over the past year. Most of the research on the virus is focused on key players during its initial attack and entry into the cellular host; namely the S protein, its glycan shield, and its interactions with the ACE2 receptors of human cells. As cases continue to raise around the globe, and new mutants are identified, there is an urgent need to understand the mechanisms of this virus during different stages of its life cycle. Here, we consider two integral membrane proteins of SARS-CoV-2 known to be important for viral assembly and infectivity. We have used microsecond-long all-atom molecular dynamics to examine the lipid-protein and protein-protein interactions of the membrane (M) and envelope (E) structural proteins of SARS-CoV-2 in a complex membrane model. We contrast the two proposed protein complexes for each of these proteins, and quantify their effect on their local lipid environment. This ongoing work also aims to provide molecular-level understanding of the mechanisms of action of this virus to possibly aid in the design of novel treatments.

scholarly journals Molecular interactions of the M and E integral membrane proteins of SARS-CoV-2

2021 ◽  
Author(s):  
Viviana Monje-Galvan ◽  
Gregory A Voth
Keyword(s):  
Membrane Proteins ◽  
Molecular Interactions ◽  
Protein Interactions ◽  
Integral Membrane Proteins ◽  
Cellular Processes ◽  
Lipid Protein Interactions ◽  
Specific Lipid

Specific lipid-protein interactions are key for cellular processes, and even more so for the replication of pathogens. The COVID-19 pandemic has drastically changed our lives and cause the death of...

scholarly journals Lipid-protein interactions are unique fingerprints for membrane proteins

2017 ◽  
Author(s):  
Valentina Corradi ◽  
Eduardo Mendez-Villuendas ◽  
Helgi I. Ingólfsson ◽  
Ruo-Xu Gu ◽  
Iwona Siuda ◽  
...  
Keyword(s):  
Membrane Proteins ◽  
Protein Interactions ◽  
Cell Membranes ◽  
Spatiotemporal Resolution ◽  
Lipid Dynamics ◽  
Lipid Species ◽  
Lipid Environment ◽  
Lipid Protein Interactions ◽  
Dynamics Simulations ◽  
Asymmetrically Distributed

ABSTRACTCell membranes contain hundreds of different proteins and lipids in an asymmetric arrangement. Understanding the lateral organization principles of these complex mixtures is essential for life and health. However, our current understanding of the detailed organization of cell membranes remains rather elusive, owing to the lack of experimental methods suitable for studying these fluctuating nanoscale assemblies of lipids and proteins with the required spatiotemporal resolution. Here, we use molecular dynamics simulations to characterize the lipid environment of ten membrane proteins. To provide a realistic lipid environment, the proteins are embedded in a model plasma membrane, where more than 60 lipid species are represented, asymmetrically distributed between leaflets. The simulations detail how each protein modulates its local lipid environment through local lipid composition, thickness, curvature and lipid dynamics. Our results provide a molecular glimpse of the complexity of lipid-protein interactions, with potentially far reaching implications for the overall organization of the cell membrane.

scholarly journals Lipid-Protein Interactions of Integral Membrane Proteins: A Comparative Simulation Study

2004 ◽  
Vol 87 (6) ◽  
pp. 3737-3749 ◽  
Author(s):  
Sundeep S. Deol ◽  
Peter J. Bond ◽  
Carmen Domene ◽  
Mark S.P. Sansom
Keyword(s):  
Membrane Proteins ◽  
Protein Interactions ◽  
Simulation Study ◽  
Integral Membrane Proteins ◽  
Lipid Protein Interactions

scholarly journals Inferring interaction partners from protein sequences using mutual information

2018 ◽  
Author(s):  
Anne-Florence Bitbol
Keyword(s):  
Mutual Information ◽  
Protein Interactions ◽  
Sequence Data ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Specific Protein ◽  
Amino Acid Residues ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Interaction Partners

AbstractSpecific protein-protein interactions are crucial in most cellular processes. They enable multiprotein complexes to assemble and to remain stable, and they allow signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interacting partners, and thus in correlations between their sequences. Pairwise maximum-entropy based models have enabled successful inference of pairs of amino-acid residues that are in contact in the three-dimensional structure of multi-protein complexes, starting from the correlations in the sequence data of known interaction partners. Recently, algorithms inspired by these methods have been developed to identify which proteins are specific interaction partners among the paralogous proteins of two families, starting from sequence data alone. Here, we demonstrate that a slightly higher performance for partner identification can be reached by an approximate maximization of the mutual information between the sequence alignments of the two protein families. This stands in contrast with structure prediction of proteins and of multiprotein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information. Our findings entail that the statistical dependences allowing interaction partner prediction from sequence data are not restricted to the residue pairs that are in direct contact at the interface between the partner proteins.Author summarySpecific protein-protein interactions are at the heart of most intra-cellular processes. Mapping these interactions is thus crucial to a systems-level understanding of cells, and has broad applications to areas such as drug targeting. Systematic experimental identification of protein interaction partners is still challenging. However, a large and rapidly growing amount of sequence data is now available. Recently, algorithms have been proposed to identify which proteins interact from their sequences alone, thanks to the co-variation of the sequences of interacting proteins. These algorithms build upon inference methods that have been used with success to predict the three-dimensional structures of proteins and multi-protein complexes, and their focus is on the amino-acid residues that are in direct contact. Here, we propose a simpler method to identify which proteins interact among the paralogous proteins of two families, starting from their sequences alone. Our method relies on an approximate maximization of mutual information between the sequences of the two families, without specifically emphasizing the contacting residue pairs. We demonstrate that this method slightly outperforms the earlier one. This result highlights that partner prediction does not only rely on the identities and interactions of directly contacting amino-acids.

Methods to analyse composition and dynamics of macromolecular complexes

2013 ◽  
Vol 41 (5) ◽  
pp. 1235-1241 ◽  
Author(s):  
Heinrich Heide ◽  
Ilka Wittig
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Biosynthesis ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Macromolecular Complexes ◽  
Cellular Processes ◽  
Recent Developments ◽  
Dna Replication And Repair ◽  
Health And Disease

Macromolecular complexes are involved in a broad spectrum of cellular processes including protein biosynthesis, protein secretion and degradation, metabolism, DNA replication and repair, and signal transduction along with other important biological processes. The analysis of protein complexes in health and disease is important to gain insights into cellular physiology and pathophysiology. In the last few decades, research has focused on the identification and the dynamics of macromolecular complexes. Several techniques have been developed to isolate native protein complexes from cells and tissues to allow further characterization by microscopic and proteomic analysis. In the present paper, we provide a brief overview of proteomic methods that can be used to identify protein–protein interactions, focusing on recent developments to study the entire complexome of a biological sample.

Ca2+-Activated K+ Channels: From Protein Complexes to Function

2010 ◽  
Vol 90 (4) ◽  
pp. 1437-1459 ◽  
Author(s):  
Henrike Berkefeld ◽  
Bernd Fakler ◽  
Uwe Schulte
Keyword(s):  
Protein Interactions ◽  
Muscle Tone ◽  
Protein Complexes ◽  
Neuronal Firing ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
And Function ◽  
And Control ◽  
Proteomic Research ◽  
Partner Proteins

Molecular research on ion channels has demonstrated that many of these integral membrane proteins associate with partner proteins, often versatile in their function, or even assemble into stable macromolecular complexes that ensure specificity and proper rate of the channel-mediated signal transduction. Calcium-activated potassium (KCa) channels that link excitability and intracellular calcium concentration are responsible for a wide variety of cellular processes ranging from regulation of smooth muscle tone to modulation of neurotransmission and control of neuronal firing pattern. Most of these functions are brought about by interaction of the channels' pore-forming subunits with distinct partner proteins. In this review we summarize recent insights into protein complexes associated with KCa channels as revealed by proteomic research and discuss the results available on structure and function of these complexes and on the underlying protein-protein interactions. Finally, the results are related to their significance for the function of KCa channels under cellular conditions.

scholarly journals Lights, Camera, Interaction: Studying Protein–Protein Interactions of the ER Protein Translocase in Living Cells

2021 ◽  
Vol 22 (19) ◽  
pp. 10358
Author(s):  
Mark Sicking ◽  
Martin Jung ◽  
Sven Lang
Keyword(s):  
Membrane Proteins ◽  
Protein Interactions ◽  
Mammalian Cells ◽  
Protein Complexes ◽  
Living Cells ◽  
Ease Of Use ◽  
Protein Protein Interactions ◽  
Regulatory Subunits ◽  
Complex Interactions ◽  
Er Membrane

Various landmark studies have revealed structures and functions of the Sec61/SecY complex in all domains of live demonstrating the conserved nature of this ancestral protein translocase. While the bacterial homolog of the Sec61 complex resides in the plasma membrane, the eukaryotic counterpart manages the transfer of precursor proteins into or across the membrane of the endoplasmic reticulum (ER). Sec61 complexes are accompanied by a set of dynamically recruited auxiliary proteins assisting the transport of certain precursor polypeptides. TRAP and Sec62/Sec63 are two auxiliary protein complexes in mammalian cells that have been characterized by structural and biochemical methods. Using these ER membrane protein complexes for our proof-of-concept study, we aimed to detect interactions of membrane proteins in living mammalian cells under physiological conditions. Bimolecular luminescence complementation and competition was used to demonstrate multiple protein–protein interactions of different topological layouts. In addition to the interaction of the soluble catalytic and regulatory subunits of the cytosolic protein kinase A, we detected interactions of ER membrane proteins that either belong to the same multimeric protein complex (intra-complex interactions: Sec61α–Sec61β, TRAPα–TRAPβ) or protein complexes in juxtaposition (inter-complex interactions: Sec61α–TRAPα, Sec61α–Sec63, and Sec61β–Sec63). In the process, we established further control elements like synthetic peptide complementation for expression profiling of fusion constructs and protease-mediated reporter degradation demonstrating the cytosolic localization of a reporter complementation. Ease of use and flexibility of the approach presented here will spur further research regarding the dynamics of protein–protein interactions in response to changing cellular conditions in living cells.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

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