scholarly journals Structural basis of O-GlcNAc recognition by mammalian 14-3-3 proteins

2018 ◽  
Vol 115 (23) ◽  
pp. 5956-5961 ◽  
Author(s):  
Clifford A. Toleman ◽  
Maria A. Schumacher ◽  
Seok-Ho Yu ◽  
Wenjie Zeng ◽  
Nathan J. Cox ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Protein Interactions ◽  
Posttranslational Modification ◽  
Structural Basis ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Downstream Signaling ◽  
Biochemical Effects ◽  
Signaling Mechanisms ◽  
Human Proteins

O-GlcNAc is an intracellular posttranslational modification that governs myriad cell biological processes and is dysregulated in human diseases. Despite this broad pathophysiological significance, the biochemical effects of most O-GlcNAcylation events remain uncharacterized. One prevalent hypothesis is that O-GlcNAc moieties may be recognized by “reader” proteins to effect downstream signaling. However, no general O-GlcNAc readers have been identified, leaving a considerable gap in the field. To elucidate O-GlcNAc signaling mechanisms, we devised a biochemical screen for candidate O-GlcNAc reader proteins. We identified several human proteins, including 14-3-3 isoforms, that bind O-GlcNAc directly and selectively. We demonstrate that 14-3-3 proteins bind O-GlcNAc moieties in human cells, and we present the structures of 14-3-3β/α and γ bound to glycopeptides, providing biophysical insights into O-GlcNAc-mediated protein–protein interactions. Because 14-3-3 proteins also bind to phospho-serine and phospho-threonine, they may integrate information from O-GlcNAc and O-phosphate signaling pathways to regulate numerous physiological functions.

scholarly journals Structural basis for the interaction of SARS-CoV-2 virulence factor nsp1 with Pol α - Primase

2021 ◽  
Author(s):  
Mairi L Kilkenny ◽  
Charlotte E Veale ◽  
Amir Guppy ◽  
Steven W Hardwick ◽  
Dimitri Y Chirgadze ◽  
...  
Keyword(s):  
Virulence Factor ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Structural Basis ◽  
Structural Protein ◽  
Protein Protein Interactions ◽  
Major Virulence Factor ◽  
Biochemical Characterisation ◽  
Human Proteins ◽  
Primase Complex

The molecular mechanisms that drive the infection by the SARS-CoV-2 coronavirus, the causative agent of the COVID-19 (Coronavirus disease-2019) pandemic, are under intense current scrutiny, to understand how the virus operates and to uncover ways in which the disease can be prevented or alleviated. Recent cell-based analyses of SARS-CoV-2 protein - protein interactions have mapped the human proteins targeted by the virus. The DNA polymerase α - primase complex or primosome, responsible for initiating DNA synthesis in genomic duplication, was identified as a target of nsp1 (non structural protein 1), a major virulence factor in the SARS-CoV-2 infection. Here, we report the biochemical characterisation of the interaction between nsp1 and the primosome and the cryoEM structure of the primosome - nsp1 complex. Our data provide a structural basis for the reported interaction between the primosome and nsp1. They suggest that Pol α - primase plays a part in the immune response to the viral infection, and that its targeting by SARS-CoV-2 aims to interfere with such function.

scholarly journals Structural basis of death domain signaling in the p75 neurotrophin receptor

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Zhi Lin ◽  
Jason Y Tann ◽  
Eddy TH Goh ◽  
Claire Kelly ◽  
Kim Buay Lim ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Binding Sites ◽  
Receptor Activation ◽  
Neurotrophin Receptor ◽  
Structural Basis ◽  
P75 Neurotrophin Receptor ◽  
Death Domain ◽  
Protein Protein Interactions ◽  
Dimer Interface ◽  
Downstream Signaling

Death domains (DDs) mediate assembly of oligomeric complexes for activation of downstream signaling pathways through incompletely understood mechanisms. Here we report structures of complexes formed by the DD of p75 neurotrophin receptor (p75NTR) with RhoGDI, for activation of the RhoA pathway, with caspase recruitment domain (CARD) of RIP2 kinase, for activation of the NF-kB pathway, and with itself, revealing how DD dimerization controls access of intracellular effectors to the receptor. RIP2 CARD and RhoGDI bind to p75NTR DD at partially overlapping epitopes with over 100-fold difference in affinity, revealing the mechanism by which RIP2 recruitment displaces RhoGDI upon ligand binding. The p75NTR DD forms non-covalent, low-affinity symmetric dimers in solution. The dimer interface overlaps with RIP2 CARD but not RhoGDI binding sites, supporting a model of receptor activation triggered by separation of DDs. These structures reveal how competitive protein-protein interactions orchestrate the hierarchical activation of downstream pathways in non-catalytic receptors.

An Integrated Prediction Method for Identifying Protein-Protein Interactions

2020 ◽  
Vol 17 (4) ◽  
pp. 271-286
Author(s):  
Chang Xu ◽  
Limin Jiang ◽  
Zehua Zhang ◽  
Xuyao Yu ◽  
Renhai Chen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Feature Vector ◽  
Prediction Method ◽  
Feature Representation ◽  
Protein Interaction Networks ◽  
Learning Approach ◽  
Biological Processes ◽  
Integrated Learning ◽  
Protein Protein Interactions ◽  
Training Process

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

scholarly journals Targeted disruption of PKC from AKAP Signaling Complexes

2021 ◽  
Author(s):  
Ameya J. Limaye ◽  
George N. Bendzunas ◽  
Eileen Kennedy
Keyword(s):  
Protein Kinase C ◽  
Protein Kinase ◽  
Signaling Pathways ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Targeted Disruption ◽  
Physiological Processes ◽  
Kinase C ◽  
P Protein

Protein Kinase C (PKC) is a member of the AGC subfamily of kinases and regulates a wide array of signaling pathways and physiological processes. Protein-protein interactions involving PKC and its...

scholarly journals A computational interactome and functional annotation for the human proteome

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
José Ignacio Garzón ◽  
Lei Deng ◽  
Diana Murray ◽  
Sagi Shapira ◽  
Donald Petrey ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Enrichment Analysis ◽  
Human Proteome ◽  
Gene Set Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Functional Relationships ◽  
Structural Relationships ◽  
Human Proteins ◽  
Validation Tests

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function.

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 An Integrative Computational Approach for the Prediction of Human-Plasmodium Protein-Protein Interactions

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Kais Ghedira ◽  
Yosr Hamdi ◽  
Abir El Béji ◽  
Houcemeddine Othman
Keyword(s):  
Protein Interactions ◽  
Computational Approach ◽  
Protein Protein Interactions ◽  
Human Red Blood Cells ◽  
Pharmacological Targets ◽  
Host Pathogen ◽  
Human Proteins ◽  
Life Threatening ◽  
Similarity Method ◽  
Human Plasmodium

Host-pathogen molecular cross-talks are critical in determining the pathophysiology of a specific infection. Most of these cross-talks are mediated via protein-protein interactions between the host and the pathogen (HP-PPI). Thus, it is essential to know how some pathogens interact with their hosts to understand the mechanism of infections. Malaria is a life-threatening disease caused by an obligate intracellular parasite belonging to the Plasmodium genus, of which P. falciparum is the most prevalent. Several previous studies predicted human-plasmodium protein-protein interactions using computational methods have demonstrated their utility, accuracy, and efficiency to identify the interacting partners and therefore complementing experimental efforts to characterize host-pathogen interaction networks. To predict potential putative HP-PPIs, we use an integrative computational approach based on the combination of multiple OMICS-based methods including human red blood cells (RBC) and Plasmodium falciparum 3D7 strain expressed proteins, domain-domain based PPI, similarity of gene ontology terms, structure similarity method homology identification, and machine learning prediction. Our results reported a set of 716 protein interactions involving 302 human proteins and 130 Plasmodium proteins. This work provides a list of potential human-Plasmodium interacting proteins. These findings will contribute to better understand the mechanisms underlying the molecular determinism of malaria disease and potentially to identify candidate pharmacological targets.

Prediction of Protein-Protein Interactions Between Human Host and Two Mycobacterial Organisms

2013 ◽  
pp. 175-187
Author(s):  
Oruganty Krishnadev ◽  
Shveta Bisht ◽  
Narayanaswamy Srinivasan
Keyword(s):  
Protein Interactions ◽  
Serine Proteases ◽  
Cellular Membrane ◽  
Human Pathogens ◽  
Protein Protein Interactions ◽  
Human Host ◽  
Interaction Partners ◽  
Human Proteins ◽  
Mycobacterial Antigens ◽  
Ras Signalling

The genomes of many human pathogens have been sequenced but the protein-protein interactions across a pathogen and human are still poorly understood. The authors apply a simple homology-based method to predict protein-protein interactions between human host and two mycobacterial organisms viz., M.tuberculosis and M.leprae. They focused on secreted proteins of pathogens and cellular membrane proteins to restrict to uncovering biologically significant and feasible interactions. Predicted interactions include five mycobacterial proteins of yet unknown function, thus suggesting a role for these proteins in pathogenesis. The authors predict interaction partners for secreted mycobacterial antigens such as MPT70, serine proteases and other proteins interacting with human proteins, such as toll-like receptors, ras signalling proteins and immune maintenance proteins, that are implicated in pathogenesis. These results suggest that the list of predicted interactions is suitable for further analysis and forms a useful step in the understanding of pathogenesis of these mycobacterial organisms.

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