Functional Characterization of Lipid Regulatory Effects of Three Genes Using Knockout Mouse Models

Integrative analysis that combines genome-wide association data with expression quantitative trait analysis and network representation may illuminate causal relationships between genes and diseases. To identify causal lipid genes, we utilized genotype, gene expression, protein-protein interaction networks, and phenotype data from 5,257 Framingham Heart Study participants and performed Mendelian randomization to investigate possible mechanistic explanations for observed associations. We selected three putatively causal candidate genes (ABCA6, ALDH2, and SIDT2) for lipid traits (LDL cholesterol, HDL cholesterol and triglycerides) in humans and conducted mouse knockout studies for each gene to confirm its causal effect on the corresponding lipid trait. We conducted the RNA-seq from mouse livers to explore transcriptome-wide alterations after knocking out the target genes. Our work builds upon a lipid-related gene network and expands upon it by including protein-protein interactions. These resources, along with the innovative combination of emerging analytical techniques, provide a groundwork upon which future studies can be designed to more fully understand genetic contributions to cardiovascular diseases.

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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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Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry

Scientific Reports ◽

10.1038/s41598-017-02522-5 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 14

Author(s):

Ravindra Kumar ◽

Sabindra K. Samal ◽

Samapika Routray ◽

Rupesh Dash ◽

Anshuman Dixit

Keyword(s):

Oral Cancer ◽

Candidate Genes ◽

Pathway Analysis ◽

Protein Interactions ◽

Target Genes ◽

Human Cancer ◽

Centrality Measures ◽

Protein Protein Interactions ◽

Tissue Samples ◽

Protein Protein Interaction

Abstract In the recent years, bioinformatics methods have been reported with a high degree of success for candidate gene identification. In this milieu, we have used an integrated bioinformatics approach assimilating information from gene ontologies (GO), protein–protein interaction (PPI) and network analysis to predict candidate genes related to oral squamous cell carcinoma (OSCC). A total of 40973 PPIs were considered for 4704 cancer-related genes to construct human cancer gene network (HCGN). The importance of each node was measured in HCGN by ten different centrality measures. We have shown that the top ranking genes are related to a significantly higher number of diseases as compared to other genes in HCGN. A total of 39 candidate oral cancer target genes were predicted by combining top ranked genes and the genes corresponding to significantly enriched oral cancer related GO terms. Initial verification using literature and available experimental data indicated that 29 genes were related with OSCC. A detailed pathway analysis led us to propose a role for the selected candidate genes in the invasion and metastasis in OSCC. We further validated our predictions using immunohistochemistry (IHC) and found that the gene FLNA was upregulated while the genes ARRB1 and HTT were downregulated in the OSCC tissue samples.

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CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry-based protein-protein interaction/association data

Molecular Biology of the Cell ◽

10.1091/mbc.e21-05-0257 ◽

2021 ◽

pp. mbc.E21-05-0257

Author(s):

Erick F. Velasquez ◽

Yenni A. Garcia ◽

Ivan Ramirez ◽

Ankur A. Gholkar ◽

Jorge Z. Torres

Keyword(s):

Mass Spectrometry ◽

Protein Interactions ◽

Biological Function ◽

Association Network ◽

Computational Pipeline ◽

Protein Protein Interactions ◽

Bioinformatic Pipeline ◽

Protein Protein Interaction ◽

Association Data ◽

Go Terms

The elucidation of a protein's interaction/association network is important for defining its biological function. Mass spectrometry-based proteomic approaches have emerged as powerful tools for identifying protein-protein interactions (PPIs) and protein-protein associations (PPAs). However, interactome/association experiments are difficult to interpret considering the complexity and abundance of data that is generated. Although tools have been developed to quantitatively identify protein interactions/associations, there is still a pressing need for easy-to-use tools that allow users to contextualize their results. To address this, we developed CANVS, a computational pipeline that cleans, analyzes, and visualizes mass spectrometry-based interactome/association data. CANVS is wrapped as an interactive Shiny dashboard, allowing users to easily interface with the pipeline. With simple requirements, users can analyze complex experimental data and create PPI/A networks. The application integrates systems biology databases like BioGRID and CORUM to contextualize the results. Furthermore, CANVS features a Gene Ontology tool that allows users to identify relevant GO terms in their results and create visual networks with proteins associated with relevant GO terms. Overall, CANVS is an easy-to-use application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying interactome/association data.

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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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Ways into Understanding HIF Inhibition

Cancers ◽

10.3390/cancers13010159 ◽

2021 ◽

Vol 13 (1) ◽

pp. 159

Author(s):

Tina Schönberger ◽

Joachim Fandrey ◽

Katrin Prost-Fingerle

Keyword(s):

Protein Interactions ◽

Target Genes ◽

Protein Protein Interactions ◽

Low Oxygen ◽

Drug Candidates ◽

Open Questions ◽

Wide Range ◽

Hif Pathway

Hypoxia is a key characteristic of tumor tissue. Cancer cells adapt to low oxygen by activating hypoxia-inducible factors (HIFs), ensuring their survival and continued growth despite this hostile environment. Therefore, the inhibition of HIFs and their target genes is a promising and emerging field of cancer research. Several drug candidates target protein–protein interactions or transcription mechanisms of the HIF pathway in order to interfere with activation of this pathway, which is deregulated in a wide range of solid and liquid cancers. Although some inhibitors are already in clinical trials, open questions remain with respect to their modes of action. New imaging technologies using luminescent and fluorescent methods or nanobodies to complement widely used approaches such as chromatin immunoprecipitation may help to answer some of these questions. In this review, we aim to summarize current inhibitor classes targeting the HIF pathway and to provide an overview of in vitro and in vivo techniques that could improve the understanding of inhibitor mechanisms. Unravelling the distinct principles regarding how inhibitors work is an indispensable step for efficient clinical applications and safety of anticancer compounds.

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Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

eLife ◽

10.7554/elife.12509 ◽

2016 ◽

Vol 5 ◽

Cited By ~ 56

Author(s):

Dan Tan ◽

Qiang Li ◽

Mei-Jun Zhang ◽

Chao Liu ◽

Chengying Ma ◽

...

Keyword(s):

Protein Interactions ◽

Structural Information ◽

Affinity Purification ◽

Protein Protein Interactions ◽

C Elegans ◽

Protein Protein Interaction ◽

Lysine Residues ◽

Spacer Arm ◽

Cross Linker ◽

Protein Nucleic Acid

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.

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Structure-aware protein–protein interaction site prediction using deep graph convolutional network

Bioinformatics ◽

10.1093/bioinformatics/btab643 ◽

2021 ◽

Author(s):

Qianmu Yuan ◽

Jianwen Chen ◽

Huiying Zhao ◽

Yaoqi Zhou ◽

Yuedong Yang

Keyword(s):

Protein Interactions ◽

Spatial Information ◽

Screening Tools ◽

Supplementary Information ◽

Protein Protein Interactions ◽

Convolutional Network ◽

Source Codes ◽

Site Prediction ◽

Protein Protein Interaction ◽

Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

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Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases

Current Topics in Medicinal Chemistry ◽

10.2174/1568026618666181025114903 ◽

2018 ◽

Vol 18 (20) ◽

pp. 1719-1736 ◽

Cited By ~ 3

Author(s):

Sharanya Sarkar ◽

Khushboo Gulati ◽

Manikyaprabhu Kairamkonda ◽

Amit Mishra ◽

Krishna Mohan Poluri

Keyword(s):

Small Molecule ◽

Protein Interactions ◽

Small Molecule Inhibitors ◽

Computational Techniques ◽

Protein Protein Interactions ◽

Computational Tools ◽

Computational Approaches ◽

Protein Protein Interaction ◽

Wide Range ◽

Therapeutic Measures

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

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