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 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 A Computational Approach for Protein-Protein Interactions of Bacterial Surface Layer Protein with Human Erb3 and αIIB-β3 Receptors

2021 ◽  
Vol 12 (1) ◽  
pp. 420-430
Keyword(s):  
Surface Layer ◽  
Molecular Docking ◽  
Protein Interactions ◽  
Protein A ◽  
Computational Approach ◽  
Microbial Interactions ◽  
Protein Protein Interactions ◽  
Epithelial Surface ◽  
Surface Receptors ◽  
Surface Layer Protein

Host microbial interactions had significant factors in maintains homeostasis and immune-related activity. One such interaction made by Lactobacillus sp. with Surface layer proteins (Slps) had been studied through a computational approach. Erb3 and αIIB-β3, which are epithelial surface layer receptors, are subjected to interact with the Slp homology model. Both cell surface receptors were subjected to interact through computational docking, followed by molecular dynamics simulations through the coarse-grain method to explore the conformational stability. Through the implementation of the molecular docking for the surface layer protein A, we have shown the surface layer protein A, protein-protein interactions are higher in cellular receptors with epidermal growth factor receptor at an -34.45 ΔG and -51.19 ΔG through molecular docking with Erb3 and αIIB-β3. This study shows the unique interaction of Slp with the epithelial surface receptors like Erb3 and αIIB-β3, which are multipurpose applications in microbial-based drug therapeutics.

scholarly journals Bacteria Use Structural Imperfect Mimicry To Hijack The Host Interactome

2020 ◽  
Author(s):  
Natalia Sanchez de Groot ◽  
Marc Torrent Burgas
Keyword(s):  
Protein Interactions ◽  
Rational Design ◽  
Protein Protein Interactions ◽  
Host Proteins ◽  
Eukaryotic Proteins ◽  
Host Pathogen ◽  
Imperfect Mimicry ◽  
The Way

ABSTRACTBacteria use protein-protein interactions to infect their hosts and hijack fundamental pathways, which ensures their survival and proliferation. Hence, the infectious capacity of the pathogen is closely related to its ability to interact with host proteins. Here, we show that hubs in the host-pathogen interactome are isolated in the pathogen network by adapting the geometry of the interacting interfaces. An imperfect mimicry of the eukaryotic interfaces allows pathogen proteins to actively bind to the host’s target while preventing deleterious effects on the pathogen interactome. Understanding how bacteria recognize eukaryotic proteins may pave the way for the rational design of new antibiotic molecules.

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 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.

High-throughput proteomics and the fight against pathogens

2016 ◽  
Vol 12 (8) ◽  
pp. 2373-2384 ◽  
Author(s):  
Anita Horvatić ◽  
Josipa Kuleš ◽  
Nicolas Guillemin ◽  
Asier Galan ◽  
Vladimir Mrljak ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Animal Welfare ◽  
High Throughput ◽  
Protein Interactions ◽  
Rapid Development ◽  
Protein Protein Interactions ◽  
Major Threat ◽  
Host Pathogen ◽  
Pathogen Protein

Pathogens pose a major threat to human and animal welfare. Understanding the interspecies host–pathogen protein–protein interactions could lead to the development of novel strategies to combat infectious diseases through the rapid development of new therapeutics.

scholarly journals Document classification for mining host pathogen protein–protein interactions

2010 ◽  
Vol 49 (3) ◽  
pp. 155-160 ◽  
Author(s):  
Lanlan Yin ◽  
Guixian Xu ◽  
Manabu Torii ◽  
Zhendong Niu ◽  
Jose M. Maisog ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Document Classification ◽  
Protein Protein Interactions ◽  
Host Pathogen ◽  
Pathogen Protein

scholarly journals Issues in performance evaluation for host–pathogen protein interaction prediction

2016 ◽  
Vol 14 (03) ◽  
pp. 1650011 ◽  
Author(s):  
Wajid Arshad Abbasi ◽  
Fayyaz Ul Amir Afsar Minhas
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Cross Validation ◽  
Protein Protein Interactions ◽  
Evaluation Scheme ◽  
Host Pathogen ◽  
Pathogen Protein ◽  
Protein Interaction Prediction ◽  
Underlying Mechanisms ◽  
Fold Cross Validation

The study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein–protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs. A number of machine learning based methods for predicting host–pathogen interactions (HPI) have been proposed in the literature. The techniques used for assessing the accuracy of such predictors are of critical importance in this domain. In this paper, we question the effectiveness of K-fold cross-validation for estimating the generalization ability of HPI prediction for proteins with no known interactions. K-fold cross-validation does not model this scenario, and we demonstrate a sizable difference between its performance and the performance of an alternative evaluation scheme called leave one pathogen protein out (LOPO) cross-validation. LOPO is more effective in modeling the real world use of HPI predictors, specifically for cases in which no information about the interacting partners of a pathogen protein is available during training. We also point out that currently used metrics such as areas under the precision-recall or receiver operating characteristic curves are not intuitive to biologists and propose simpler and more directly interpretable metrics for this purpose.

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