Protein–protein interactions of HPV–Chlamydia trachomatis–human and their potential in cervical cancer

Aim: HPV is an important cause of cervical cancer, but Chlamydia trachomatis (CT) is suspiciously involved in this disease ranging from direct to its involvement as a cofactor with HPV. We performed this study to understand the interaction of HPV and C. trachomatis with humans and its contribution to cervical cancer. Materials & methods: Host–pathogen and pathogen–pathogen protein–protein interaction maps of HPV/CT/human were prepared and compared to analyze interactions during single/coinfection of C. trachomatis and HPV. The interacting human proteins were detected by their involvement in cervical cancer. Results: C. trachomatis may interact with several cancer associated proteins while HPV and C. trachomatis largely interact with different human proteins, suggesting different pathogenesis. Conclusion: C. trachomatis coinfection with HPV may modulate cervical cancer development.

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

10.21203/rs.3.rs-61338/v1 ◽

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

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On the structure of protein–protein interaction networks

Biochemical Society Transactions ◽

10.1042/bst0311491 ◽

2003 ◽

Vol 31 (6) ◽

pp. 1491-1496 ◽

Cited By ~ 47

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.

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Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

10.21203/rs.3.rs-28592/v1 ◽

2020 ◽

Author(s):

Kalyani B. Karunakaran ◽

N. Balakrishnan ◽

Madhavi K. Ganapathiraju

Keyword(s):

Viral Infection ◽

Protein Interactions ◽

Keyword Search ◽

Protein Protein Interactions ◽

Host Proteins ◽

Viral Genomes ◽

Protein Protein Interaction ◽

Viral Budding ◽

Human Proteins ◽

Protein Interaction Prediction

Abstract World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://hagrid.dbmi.pitt.edu/corona that allows not only keyword search but also queries such as “PPIs where one protein is associated with ‘virus’ and the interactors with 'pulmonary'”.

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An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.80.3.17 ◽

2020 ◽

Vol 80 (03) ◽

Author(s):

Yu-Miao Zhang ◽

Jun Wang ◽

Tao Wu

Keyword(s):

Subcellular Localization ◽

Protein Interaction ◽

Protein Interactions ◽

Epidermal Cells ◽

Cyclin Dependent Kinase ◽

Protein Subcellular Localization ◽

Protein Protein Interactions ◽

Efficient System ◽

Protein Protein Interaction ◽

Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

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Decoding Protein-protein Interactions: An Overview

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200226105312 ◽

2020 ◽

Vol 20 (10) ◽

pp. 855-882

Author(s):

Olivia Slater ◽

Bethany Miller ◽

Maria Kontoyianni

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Drug Repurposing ◽

Protein Docking ◽

Target Space ◽

Protein Protein Interactions ◽

X Ray Crystallography ◽

Protein Protein Interaction ◽

Interaction Sites ◽

Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Sun Sook Chung ◽

Joseph C F Ng ◽

Anna Laddach ◽

N Shaun B Thomas ◽

Franca Fraternali

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Interaction Network ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Short Loop ◽

New Strategy ◽

Loop Network ◽

Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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Cervical cancer development, chemoresistance, and therapy: a snapshot of involvement of microRNA

Molecular and Cellular Biochemistry ◽

10.1007/s11010-021-04249-4 ◽

2021 ◽

Author(s):

Tandrima Mitra ◽

Selvakumar Elangovan

Keyword(s):

Cervical Cancer ◽

Cancer Development ◽

Cervical Cancer Development

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miR-122 Inhibits the Cervical Cancer Development by Targeting the Oncogene RAD21

Biochemical Genetics ◽

10.1007/s10528-021-10098-z ◽

2021 ◽

Author(s):

Yanling Yang ◽

Yang Liu ◽

Wei Liu ◽

Chunyang Li ◽

Yuan Liu ◽

...

Keyword(s):

Cervical Cancer ◽

Cancer Development ◽

Cervical Cancer Development

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Universal Screening Methods and Applications of ThermoFluor®

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057106292746 ◽

2006 ◽

Vol 11 (7) ◽

pp. 854-863 ◽

Cited By ~ 124

Author(s):

Maxwell D. Cummings ◽

Michael A. Farnum ◽

Marina I. Nelen

Keyword(s):

Protein Interactions ◽

Protein Function ◽

Protein Unfolding ◽

Direct Detection ◽

Functional Characterization ◽

Screening Methods ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Bacterial Enzyme ◽

Research Problems

The genomics revolution has unveiled a wealth of poorly characterized proteins. Scientists are often able to produce milligram quantities of proteins for which function is unknown or hypothetical, based only on very distant sequence homology. Broadly applicable tools for functional characterization are essential to the illumination of these orphan proteins. An additional challenge is the direct detection of inhibitors of protein-protein interactions (and allosteric effectors). Both of these research problems are relevant to, among other things, the challenge of finding and validating new protein targets for drug action. Screening collections of small molecules has long been used in the pharmaceutical industry as 1 method of discovering drug leads. Screening in this context typically involves a function-based assay. Given a sufficient quantity of a protein of interest, significant effort may still be required for functional characterization, assay development, and assay configuration for screening. Increasingly, techniques are being reported that facilitate screening for specific ligands for a protein of unknown function. Such techniques also allow for function-independent screening with better characterized proteins. ThermoFluor®, a screening instrument based on monitoring ligand effects on temperature-dependent protein unfolding, can be applied when protein function is unknown. This technology has proven useful in the decryption of an essential bacterial enzyme and in the discovery of a series of inhibitors of a cancer-related, protein-protein interaction. The authors review some of the tools relevant to these research problems in drug discovery, and describe our experiences with 2 different proteins.

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