Comparison of Computational Tools for Protein-Protein Interaction (PPI) Mapping and Analysis

2013 ◽  
Vol 63 (1) ◽  
Author(s):  
Geok Wei Leong ◽  
Sheau Chen Lee ◽  
Cher Chien Lau ◽  
Peter Klappa ◽  
Mohd Shahir Shamsir Omar
Keyword(s):  
Protein Interactions ◽  
Network Visualization ◽  
Database Integration ◽  
File Format ◽  
Output File ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Computational Tools ◽  
Protein Protein Interaction ◽  
Visualization Tools

Several visualization tools for the mapping of protein-protein interactions have been developed in recent years. However, a systematic comparison of the virtues and limitations of different PPI visualization tools has not been carried out so far. In this study, we compare seven commonly used visualization tools, based on input and output file format, layout algorithm, database integration, Gene Ontology annotation and accessibility of each tool. The assessment was carried out based on brain disease datasets. Our suggested tools, NAViGaTOR, Cytoscape and Gephi perform competitively as PPI network visualization tools, can be a reference for future researches on PPI mapping and analysis. 

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

2018 ◽  
Vol 18 (20) ◽  
pp. 1719-1736 ◽  
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.

scholarly journals Network Pharmacology study on the mechanism of MKA Polyherbal Formulation in combating Respiratory Diseases

2020 ◽  
Vol 9 (6) ◽  
pp. 385-391
Author(s):  
T Poongodi ◽  
◽  
TH Nazeema ◽  
Keyword(s):  
Mechanism Of Action ◽  
Protein Interactions ◽  
Respiratory Diseases ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Egfr Inhibition ◽  
Protein Protein Interaction ◽  
Polyherbal Formulation

The Multi-targeted action of Polyherbal formulation is responsible for enhanced therapeutic efficacy in combating various diseases. But, understanding the mode of action of herbal medicine remains a challenge because of its complex metabolomics. Network pharmacology-based approach enables to explore the mechanism of action of polyherbal formulation in biological system. In present investigation, we have explored the molecular mechanism of action of the Polyherbal formulation MKA comprising of three botanicals Mimusops elengi L., Kedrostis foetidissima (Jacq.) Cogn. and Artemisia vulgaris L. in treating respiratory diseases by network pharmacology-based approach. The protein targets were mined from Binding database for the bioactive present in MKA. The disease associated targets were identified using Open target Platform. Based on ligand-target interactions, it was interpreted that MKA could alleviate the symptoms of respiratory disease by multiple mechanisms like EGFR inhibition by Quercetin and Quercetin-3-O-rhamnoside, KDR inhibition by Quercetin, STAT-3 inhibition by β-sitosterol- β-Dglucoside, TRPV1 inhibition by phytol acetate, etc. The Protein-protein interaction (PPI) network was constructed using STRING database. KEGG pathway based functional enrichment was also predicted for the PPI network. It was found that multiple ligand-target interactions and protein-protein interactions is responsible for pharmacological activity of MKA in respiratory diseases.

scholarly journals Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Lei Yang ◽  
Xianglong Tang
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Protein Complexes ◽  
False Negative ◽  
Biological Database ◽  
Interacting Proteins ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Biological Meaning

Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

scholarly journals A system biology approach based on metabolic biomarkers and protein–protein interactions for identifying pathways underlying schizophrenia and bipolar disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Md. Altaf-Ul-Amin ◽  
Kazuhisa Hirose ◽  
João V. Nani ◽  
Lucas C. Porta ◽  
Ljubica Tasic ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Protein Interactions ◽  
Diagnostic Methods ◽  
Brain Diseases ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Clinical Interviews ◽  
Protein Protein Interaction ◽  
High Prevalence ◽  
Metabolic Biomarkers

AbstractMental disorders (MDs), including schizophrenia (SCZ) and bipolar disorder (BD), have attracted special attention from scientists due to their high prevalence and significantly debilitating clinical features. The diagnosis of MDs is still essentially based on clinical interviews, and intensive efforts to introduce biochemical based diagnostic methods have faced several difficulties for implementation in clinics, due to the complexity and still limited knowledge in MDs. In this context, aiming for improving the knowledge in etiology and pathophysiology, many authors have reported several alterations in metabolites in MDs and other brain diseases. After potentially fishing all metabolite biomarkers reported up to now for SCZ and BD, we investigated here the proteins related to these metabolites in order to construct a protein–protein interaction (PPI) network associated with these diseases. We determined the statistically significant clusters in this PPI network and, based on these clusters, we identified 28 significant pathways for SCZ and BDs that essentially compose three groups representing three major systems, namely stress response, energy and neuron systems. By characterizing new pathways with potential to innovate the diagnosis and treatment of psychiatric diseases, the present data may also contribute to the proposal of new intervention for the treatment of still unmet aspects in MDs.

scholarly journals Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yanghe Feng ◽  
Qi Wang ◽  
Tengjiao Wang
Keyword(s):  
Protein Interactions ◽  
Drug Target ◽  
Drug Targets ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
Protein Protein Interaction Networks

The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target’s homologue set containing 102 potential target proteins is predicted in the paper.

scholarly journals InteractoMIX: a suite of computational tools to exploit interactomes in biological and clinical research

2016 ◽  
Vol 44 (3) ◽  
pp. 917-924 ◽  
Author(s):  
Daniel Poglayen ◽  
Manuel Alejandro Marín-López ◽  
Jaume Bonet ◽  
Oriol Fornes ◽  
Javier Garcia-Garcia ◽  
...  
Keyword(s):  
Clinical Research ◽  
Protein Interactions ◽  
Biological Processes ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Computational Tools ◽  
Cellular Processes ◽  
Structural Details ◽  
One Stop ◽  
Health And Disease

Virtually all the biological processes that occur inside or outside cells are mediated by protein–protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com).

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

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.

Decoding Protein-protein Interactions: An Overview

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.

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

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.

scholarly journals Universal Screening Methods and Applications of ThermoFluor®

2006 ◽  
Vol 11 (7) ◽  
pp. 854-863 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document