scholarly journals A 3D Structural Interactome to Explore the Impact of Evolutionary Divergence, Population Variation, and Small-molecule Drugs on SARS-CoV-2-Human Protein-Protein Interactions

2020 ◽  
Author(s):  
Shayne D. Wierbowski ◽  
Siqi Liang ◽  
You Chen ◽  
Nicole M. Andre ◽  
Steven M. Lipkin ◽  
...  
Keyword(s):  
Peer Review ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Protein Structures ◽  
Population Variation ◽  
Easy Access ◽  
Evolutionary Divergence ◽  
Viral Agent ◽  
Protein Protein Interactions ◽  
The Impact

AbstractThe recent COVID-19 pandemic has sparked a global public health crisis. Vital to the development of informed treatments for this disease is a comprehensive understanding of the molecular interactions involved in disease pathology. One lens through which we can better understand this pathology is through the network of protein-protein interactions between its viral agent, SARS-CoV-2, and its human host. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV can be explained by rapid evolution along the interface between the Spike protein and its human receptor (ACE2) leading to increased binding affinity. Sequence divergences that modulate other protein-protein interactions may further explain differences in transmission and virulence in this novel coronavirus. To facilitate these comparisons, we combined homology-based structural modeling with the ECLAIR pipeline for interface prediction at residue resolution, and molecular docking with PyRosetta. This enabled us to compile a novel 3D structural interactome meta-analysis for the published interactome network between SARS-CoV-2 and human. This resource includes docked structures for all interactions with protein structures, enrichment analysis of variation along interfaces, predicted ΔΔG between SARS-CoV and SARS-CoV-2 variants for each interaction, predicted impact of natural human population variation on binding affinity, and a further prioritized set of drug repurposing candidates predicted to overlap with protein interfaces†. All predictions are available online† for easy access and are continually updated when new interactions are published.† Some sections of this pre-print have been redacted to comply with current bioRxiv policy restricting the dissemination of purely in silico results predicting potential therapies for SARS-CoV-2 that have not undergone thorough peer-review. The results section titled “Prioritization of Candidate Inhibitors of SARS-CoV-2-Human Interactions Through Binding Site Comparison,” Figure 4, Supplemental Table 9, and all links to our web resource have been removed. Blank headers left in place to preserve structure and item numbering. Our full manuscript will be published in an appropriate journal following peer-review.

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 De novo design of a reversible phosphorylation-dependent switch for membrane targeting

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Leon Harrington ◽  
Jordan M. Fletcher ◽  
Tamara Heermann ◽  
Derek N. Woolfson ◽  
Petra Schwille
Keyword(s):  
Protein Interactions ◽  
Lipid Membrane ◽  
De Novo ◽  
Protein Localization ◽  
Protein Structures ◽  
Spatiotemporal Pattern ◽  
Membrane Targeting ◽  
Protein Protein Interactions ◽  
Reversible Phosphorylation ◽  
Potential Applications

AbstractModules that switch protein-protein interactions on and off are essential to develop synthetic biology; for example, to construct orthogonal signaling pathways, to control artificial protein structures dynamically, and for protein localization in cells or protocells. In nature, the E. coli MinCDE system couples nucleotide-dependent switching of MinD dimerization to membrane targeting to trigger spatiotemporal pattern formation. Here we present a de novo peptide-based molecular switch that toggles reversibly between monomer and dimer in response to phosphorylation and dephosphorylation. In combination with other modules, we construct fusion proteins that couple switching to lipid-membrane targeting by: (i) tethering a ‘cargo’ molecule reversibly to a permanent membrane ‘anchor’; and (ii) creating a ‘membrane-avidity switch’ that mimics the MinD system but operates by reversible phosphorylation. These minimal, de novo molecular switches have potential applications for introducing dynamic processes into designed and engineered proteins to augment functions in living cells and add functionality to protocells.

Swim-exercised mice show a decreased level of protein O-GlcNAcylation and expression of O-GlcNAc transferase in heart

2011 ◽  
Vol 111 (1) ◽  
pp. 157-162 ◽  
Author(s):  
Darrell D. Belke
Keyword(s):  
Protein Interactions ◽  
Contractile Function ◽  
Cell Physiology ◽  
Protein Protein Interactions ◽  
Training Exercise ◽  
General Protein ◽  
Exercise Induced ◽  
Glcnac Transferase ◽  
The Impact ◽  
Physiological Hypertrophy

Swim-training exercise in mice leads to cardiac remodeling associated with an improvement in contractile function. Protein O-linked N-acetylglucosamine ( O-GlcNAcylation) is a posttranslational modification of serine and threonine residues capable of altering protein-protein interactions affecting gene transcription, cell signaling pathways, and general cell physiology. Increased levels of protein O-GlcNAcylation in the heart have been associated with pathological conditions such as diabetes, ischemia, and hypertrophic heart failure. In contrast, the impact of physiological exercise on protein O-GlcNAcylation in the heart is currently unknown. Swim-training exercise in mice was associated with the development of a physiological hypertrophy characterized by an improvement in contractile function relative to sedentary mice. General protein O-GlcNAcylation was significantly decreased in swim-exercised mice. This effect was mirrored in the level of O-GlcNAcylation of individual proteins such as SP1. The decrease in protein O-GlcNAcylation was associated with a decrease in the expression of O-GlcNAc transferase (OGT) and glutamine-fructose amidotransferase (GFAT) 2 mRNA. O-GlcNAcase (OGA) activity was actually lower in swim-trained than sedentary hearts, suggesting that it did not contribute to the decreased protein O-GlcNAcylation. Thus it appears that exercise-induced physiological hypertrophy is associated with a decrease in protein O-GlcNAcylation, which could potentially contribute to changes in gene expression and other physiological changes associated with exercise.

scholarly journals Theoretical models of inhibitory activity for inhibitors of protein–protein interactions: targeting menin–mixed lineage leukemia with small molecules

MedChemComm ◽  
2017 ◽  
Vol 8 (12) ◽  
pp. 2216-2227 ◽  
Author(s):  
Wiktoria Jedwabny ◽  
Szymon Kłossowski ◽  
Trupta Purohit ◽  
Tomasz Cierpicki ◽  
Jolanta Grembecka ◽  
...  
Keyword(s):  
Small Molecules ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Empirical Model ◽  
Inhibitory Activity ◽  
Theoretical Models ◽  
Mixed Lineage Leukemia ◽  
Protein Protein Interactions ◽  
Model Based ◽  
Dispersion Terms

A computationally affordable, non-empirical model based on electrostatic multipole and dispersion terms successfully predicts the binding affinity of inhibitors of menin–MLL protein–protein interactions.

scholarly journals Engineered pH-Sensitive Protein G / IgG Interaction

2020 ◽  
Author(s):  
Ramesh K. Jha ◽  
Allison Yankey ◽  
Kalifa Shabazz ◽  
Leslie Naranjo ◽  
Nileena Velappan ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Protein Design ◽  
Protein Interactions ◽  
Rational Design ◽  
Protein G ◽  
Acidic Ph ◽  
Protein Protein Interactions ◽  
Natural Interface ◽  
Complex Stimuli ◽  
Igg Purification

ABSTRACTWhile natural protein-protein interactions have evolved to be induced by complex stimuli, rational design of interactions that can be switched-on-demand still remain challenging in the protein design world. Here, we demonstrate a computationally redesigned natural interface for improved binding affinity could further be mutated to adopt a pH switchable interaction. The redesigned interface of Protein G-IgG Fc domain, when incorporated with histidine and glutamic acid on Protein G (PrG-EHHE), showed a switch in binding affinity by 50-fold when pH was altered from mild acidic to mild basic. The wild type (WT) interface only showed negligible switch. The overall binding affinity at mild acidic pH for PrG-EHHE outperformed the WT PrG interaction. The new reagent PrG-EHHE will be revolutionary in IgG purification since the traditional method of using an extreme acidic pH for elution can be circumvented.Abstract Figure

scholarly journals H2V, a Database for Human Proteins and Genes in Response to SARS-CoV-2, SARS-CoV, and MERS-CoV Infection

2020 ◽  
Author(s):  
Nan Zhou ◽  
Jinku Bao ◽  
Yuping Ning
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interactions ◽  
Viral Infections ◽  
Antiviral Agents ◽  
Differentially Expressed ◽  
Easy Access ◽  
Differentially Expressed Proteins ◽  
Protein Protein Interactions ◽  
Associated Proteins ◽  
Human Proteins

Abstract The ongoing COVID-19 pandemic in the world is caused by SARS-CoV-2, a new coronavirus firstly discovered in the end of 2019. It has led to more than 10 million confirmed cases and more than 500,000 confirmed deaths across 216 countries by 1 July 2020, according to WHO statistics. SARS-CoV-2, SARS-CoV, and MERS-CoV are alike, killing people, impairing economy, and inflicting long-term impacts on the society. However, no specific drug or vaccine has been approved as a cure for these viruses. The efforts to develop antiviral measures are hampered by insufficient understanding of molecular responses of human to viral infections. In this study, we collected experimentally validated human proteins that interact with SARS-CoV-2 proteins, human proteins whose expression, translation and phosphorylation levels experience significantly changes after SARS-CoV-2 or SARS-CoV infection, human proteins that correlate with COVID-19 severity, and human genes whose expression levels significantly changed upon SARS-CoV-2 or MERS-CoV infection. A database, H2V, was then developed for easy access to these data. Currently H2V includes: 332 human-SARS-CoV-2 protein-protein interactions; 65 differentially expressed proteins, 232 differentially translated proteins, 1298 differentially phosphorylated proteins, 204 severity associated proteins, and 4012 differentially expressed genes responding to SARS-CoV-2 infection; 66 differentially expressed proteins responding to SARS-CoV infection; and 6981 differentially expressed genes responding to MERS-CoV infection. H2V can help to understand the cellular responses associated with SARS-CoV-2, SARS-CoV and MERS-CoV infection. It is expected to speed up the development of antiviral agents and shed light on the preparation for potential coronavirus emergency in the future.Database url: http://www.zhounan.org/h2v

scholarly journals Molecular dynamics shows complex interplay and long-range effects of post-translational modifications in yeast protein interactions

2021 ◽  
Vol 17 (5) ◽  
pp. e1008988
Author(s):  
Nikolina ŠoŠtarić ◽  
Vera van Noort
Keyword(s):  
Molecular Dynamics ◽  
Conformational Changes ◽  
Protein Interactions ◽  
Protein Structures ◽  
Cellular Protein ◽  
Free Energy Calculations ◽  
Protein Protein Interactions ◽  
Post Translational Modifications ◽  
Protein Properties ◽  
Dynamics Simulations

Post-translational modifications (PTMs) play a vital, yet often overlooked role in the living cells through modulation of protein properties, such as localization and affinity towards their interactors, thereby enabling quick adaptation to changing environmental conditions. We have previously benchmarked a computational framework for the prediction of PTMs’ effects on the stability of protein-protein interactions, which has molecular dynamics simulations followed by free energy calculations at its core. In the present work, we apply this framework to publicly available data on Saccharomyces cerevisiae protein structures and PTM sites, identified in both normal and stress conditions. We predict proteome-wide effects of acetylations and phosphorylations on protein-protein interactions and find that acetylations more frequently have locally stabilizing roles in protein interactions, while the opposite is true for phosphorylations. However, the overall impact of PTMs on protein-protein interactions is more complex than a simple sum of local changes caused by the introduction of PTMs and adds to our understanding of PTM cross-talk. We further use the obtained data to calculate the conformational changes brought about by PTMs. Finally, conservation of the analyzed PTM residues in orthologues shows that some predictions for yeast proteins will be mirrored to other organisms, including human. This work, therefore, contributes to our overall understanding of the modulation of the cellular protein interaction networks in yeast and beyond.

scholarly journals FrustratometeR: an R-package to compute Local frustration in protein structures, point mutants and MD simulations

2020 ◽  
Author(s):  
Atilio O. Rausch ◽  
Maria I. Freiberger ◽  
Cesar O. Leonetti ◽  
Diego M. Luna ◽  
Leandro G. Radusky ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Protein Structures ◽  
Md Simulations ◽  
R Package ◽  
Protein Protein Interactions ◽  
Large Scale Analysis ◽  
Functional Aspects ◽  
Catalytic Sites ◽  
Polypeptide Chains

Once folded natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i.e. they have highly frustrated regions. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein-protein interactions, small ligand recognition, catalytic sites and allostery. Here we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and MD trajectories, allowing straightforward integration of local frustration analysis in to pipelines for protein structural analysis.Availability and implementation: https://github.com/proteinphysiologylab/frustratometeR

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