scholarly journals Structural Insights into the Interactions of Candidal Enolase with Human Vitronectin, Fibronectin and Plasminogen

2020 ◽  
Vol 21 (21) ◽  
pp. 7843 ◽  
Author(s):  
Dorota Satala ◽  
Grzegorz Satala ◽  
Justyna Karkowska-Kuleta ◽  
Michal Bukowski ◽  
Anna Kluza ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Dissociation Constants ◽  
Tight Binding ◽  
Protein Protein Interactions ◽  
Glycolysis Pathway ◽  
Human Proteins ◽  
Activity Center ◽  
Moonlighting Functions ◽  
Structural Insights ◽  
Internal Motif

Significant amounts of enolase—a cytosolic enzyme involved in the glycolysis pathway—are exposed on the cell surface of Candida yeast. It has been hypothesized that this exposed enolase form contributes to infection-related phenomena such as fungal adhesion to human tissues, and the activation of fibrinolysis and extracellular matrix degradation. The aim of the present study was to characterize, in structural terms, the protein-protein interactions underlying these moonlighting functions of enolase. The tight binding of human vitronectin, fibronectin and plasminogen by purified C. albicans and C. tropicalis enolases was quantitatively analyzed by surface plasmon resonance measurements, and the dissociation constants of the formed complexes were determined to be in the 10−7–10−8 M range. In contrast, the binding of human proteins by the S.cerevisiae enzyme was much weaker. The chemical cross-linking method was used to map the sites on enolase molecules that come into direct contact with human proteins. An internal motif 235DKAGYKGKVGIAMDVASSEFYKDGK259 in C. albicans enolase was suggested to contribute to the binding of all three human proteins tested. Models for these interactions were developed and revealed the sites on the enolase molecule that bind human proteins, extensively overlap for these ligands, and are well-separated from the catalytic activity center.

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.

scholarly journals Predicting virus-receptor mutant binding by molecular dynamics simulation

2013 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Protein Interactions ◽  
Tight Binding ◽  
Dynamics Simulation ◽  
Virus Protein ◽  
Protein Protein Interactions ◽  
Distance Curve ◽  
Protein Protein Interaction ◽  
Human Transferrin Receptor ◽  
Machupo Virus ◽  
Receptor Mutant

Existing computational methods to predict protein–protein interaction affinity often perform poorly in important test cases. In particular, the effects of multiple mutations, non-alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here a new method to interrogate affinity differences resulting from mutations in a host-virus protein–protein interface. Our method is based on extensive non-equilibrium all atom simulations: We computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) and estimate affinity using the max imum applied force during a pulling simulation and the area under the force-versus-distance curve. We find that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our method provides an elegant framework to compare the effects of multi ple mutations, individually and jointly, on protein–protein interactions.

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.

scholarly journals Use of periplasmic target protein capture for phage display engineering of tight-binding protein–protein interactions

2011 ◽  
Vol 24 (11) ◽  
pp. 819-828 ◽  
Author(s):  
Bartlomiej G. Fryszczyn ◽  
Nicholas G. Brown ◽  
Wanzhi Huang ◽  
Miriam A. Balderas ◽  
Timothy Palzkill
Keyword(s):  
Phage Display ◽  
Protein Interactions ◽  
Binding Protein ◽  
Tight Binding ◽  
Target Protein ◽  
Protein Protein Interactions ◽  
Protein Capture

On the structure of protein–protein interaction networks

2003 ◽  
Vol 31 (6) ◽  
pp. 1491-1496 ◽  
Author(s):  
A. Thomas ◽  
R. Cannings ◽  
N.A.M. Monk ◽  
C. Cannings
Keyword(s):  
Power Law ◽  
Protein Interactions ◽  
Large Scale ◽  
Underlying Structure ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Approximate Power ◽  
Two Hybrid

We present a simple model for the underlying structure of protein–protein pairwise interaction graphs that is based on the way in which proteins attach to each other in experiments such as yeast two-hybrid assays. We show that data on the interactions of human proteins lend support to this model. The frequency of the number of connections per protein under this model does not follow a power law, in contrast to the reported behaviour of data from large-scale yeast two-hybrid screens of yeast protein–protein interactions. Sampling sub-graphs from the underlying graphs generated with our model, in a way analogous to the sampling performed in large-scale yeast two-hybrid searches, gives degree distributions that differ subtly from the power law and that fit the observed data better than the power law itself. Our results show that the observation of approximate power law behaviour in a sampled sub-graph does not imply that the underlying graph follows a power law.

scholarly journals Identification and characterization of interactions between the vertebrate polycomb-group protein BMI1 and human homologs of polyhomeotic.

1997 ◽  
Vol 17 (4) ◽  
pp. 2326-2335 ◽  
Author(s):  
M J Gunster ◽  
D P Satijn ◽  
K M Hamer ◽  
J L den Blaauwen ◽  
D de Bruijn ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Expression Patterns ◽  
Polycomb Group ◽  
Polycomb Group Protein ◽  
Protein Protein Interactions ◽  
Human Proteins ◽  
Group Protein ◽  
The Individual ◽  
Domain I ◽  
Homology Domains

In Drosophila melanogaster, the Polycomb-group (PcG) genes have been identified as repressors of gene expression. They are part of a cellular memory system that is responsible for the stable transmission of gene activity to progeny cells. PcG proteins form a large multimeric, chromatin-associated protein complex, but the identity of its components is largely unknown. Here, we identify two human proteins, HPH1 and HPH2, that are associated with the vertebrate PcG protein BMI1. HPH1 and HPH2 coimmunoprecipitate and cofractionate with each other and with BMI1. They also colocalize with BMI1 in interphase nuclei of U-2 OS human osteosarcoma and SW480 human colorectal adenocarcinoma cells. HPH1 and HPH2 have little sequence homology with each other, except in two highly conserved domains, designated homology domains I and II. They share these homology domains I and II with the Drosophila PcG protein Polyhomeotic (Ph), and we, therefore, have named the novel proteins HPH1 and HPH2. HPH1, HPH2, and BMI1 show distinct, although overlapping expression patterns in different tissues and cell lines. Two-hybrid analysis shows that homology domain II of HPH1 interacts with both homology domains I and II of HPH2. In contrast, homology domain I of HPH1 interacts only with homology domain II of HPH2, but not with homology domain I of HPH2. Furthermore, BMI1 does not interact with the individual homology domains. Instead, both intact homology domains I and II need to be present for interactions with BMI1. These data demonstrate the involvement of homology domains I and II in protein-protein interactions and indicate that HPH1 and HPH2 are able to heterodimerize.

scholarly journals Protein-protein interactions of cytochromes P450 3A4 and 3A5 with their intermediate redox partners cytochromes b5

2015 ◽  
Vol 61 (4) ◽  
pp. 468-474
Author(s):  
O.V. Gnedenko ◽  
A.S. Ivanov ◽  
E.O. Yablokov ◽  
S.A. Usanov ◽  
D.V. Mukha ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Dissociation Constants ◽  
Cytochromes P450 ◽  
Cytochrome B5 ◽  
Electrochemical Analysis ◽  
Monooxygenase System ◽  
Protein Protein Interactions ◽  
Reduction Potentials ◽  
Drug Biotransformation ◽  
Redox Partners

Molecular interactions between proteins redox partners (cytochromes Р450 3А4, 3А5 and cytochrome b5) within the monooxygenase system, which is known to be involved in drug biotransformation, were investigated. Human cytochromes Р450 3A4 and 3А5 (CYP3A4 and CYP3A5) form complexes with various cytochromes b5: the microsomal (b5mc) and mitochondrial (b5om) forms of this protein, as well as with 2 “chimeric” proteins, b5(om-mc), b5(mc-om). Kinetic constants and equilibrium dissociation constants were determined by the SPR biosensor. Essential distinction between CYP3A4 and CYP3A5 was only observed upon their interactions with cytochrome b5om. Electroanalytical characteristics of electrodes with immobilized hemoproteins were obtained. The electrochemical analysis of CYP3A4, CYP3A5, b5mc, b5om, b5(om-mc), and b5(mc-om) immobilized on screen printed graphite electrodes modified with membranous matrix revealed that these proteins have very close reduction potentials -0.435  -0.350 V (vs. Ag/AgCl). Cytochrome b5mc was shown to be capable of stimulating the electrocatalytic activity of CYP3A4 in the presence of its substrate testosterone.

scholarly journals Predictions of Protein-Protein Interactions in Schistosoma Mansoni

2017 ◽  
Author(s):  
Javona White Bear ◽  
James H. McKerrow
Keyword(s):  
Schistosoma Mansoni ◽  
Language Processing ◽  
Protein Interactions ◽  
Species Interactions ◽  
Human Protein ◽  
Comparative Approach ◽  
Application Framework ◽  
Protein Protein Interactions ◽  
Protease Inhibition ◽  
Human Proteins

AbstractBackgroundSchistosoma mansoni invasion of the human host involves a variety of cross-species protein-protein interactions. The pathogen expresses a diverse arsenal of proteins that facilitate the breach of physical and biochemical barriers present in skin, evasion of the immune system, and digestion of human hemoglobin, allowing schistosomes to reside in the host for years. However, only a small number of specific interactions between S. mansoni and human proteins have been identified. We present and apply a protocol that generates testable predictions of S. mansoni-human protein interactions.MethodsIn this study, we first predict S. mansoni-human protein interactions based on similarity to known protein complexes. Putative interactions were then scored and assessed using several contextual filters, including the use of annotation automatically derived from literature using a simple natural language processing methodology. Our method predicted 7 out of the 10 previously known cross-species interactions.ConclusionsSeveral predictions that warrant experimental follow-up were presented and discussed, including interactions involving potential vaccine candidate antigens, protease inhibition, and immune evasion. The application framework provides an integrated methodology for investigation of host-pathogen interactions and an extensive source of orthogonal data for experimental analysis. We have made the predictions available online for community perusal.Author SummaryThe S. mansoni parasite is the etiological agent of the disease Schistomiasis. However, protein-protein interactions have been experimentally characterized that relate to pathogenesis and establishment of infection. As with many pathogens, the understanding of these interactions is a key component for the development of new vaccines. In this project, we have applied a computational whole-genome comparative approach to aid in the prediction of interactions between S. mansoni and human proteins and to identify important proteins involved in infection. The results of applying this method recapitulate several previously characterized interactions, as well as suggest additional ones as potential therapeutic targets.

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