Discovery of cancer-related new drug target proteins from re-constructed human disease network based on protein-protein interaction netowk

Author(s):  
Yoon Kyeong Lee ◽  
Hak Yong Kim
2011 ◽  
Vol 19 (7) ◽  
pp. 783-788 ◽  
Author(s):  
Xuehong Zhang ◽  
Ruijie Zhang ◽  
Yongshuai Jiang ◽  
Peng Sun ◽  
Guoping Tang ◽  
...  

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yonghyun Nam ◽  
Dong-gi Lee ◽  
Sunjoo Bang ◽  
Ju Han Kim ◽  
Jae-Hoon Kim ◽  
...  

Abstract Background The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing ‘n-of-1 utility’ (n potential diseases of one patient) to human disease network—the translational disease network. Results We first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network. Conclusions The proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.


2011 ◽  
Vol 29 (1) ◽  
pp. 55-72 ◽  
Author(s):  
Kenneth Wysocki ◽  
Leslie Ritter

Using bioinformatics computational tools, network maps that integrate the complex interactions of genetics and diseases have been developed. The purpose of this review is to introduce the reader to new approaches in understanding disease–gene associations using network maps, with an emphasis on how the human disease network (HDN) map (or diseasome) was constructed. A search was conducted in PubMed using the years 1999–2011 and using key words diseasome, molecular interaction, interactome, protein–protein interaction, and gene. The information reviewed included journal reviews, open source and webbased databases, and open source computational tools.


2016 ◽  
Vol 43 (6) ◽  
pp. 349-367 ◽  
Author(s):  
Olfat Al-Harazi ◽  
Sadiq Al Insaif ◽  
Monirah A. Al-Ajlan ◽  
Namik Kaya ◽  
Nduna Dzimiri ◽  
...  

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3174 ◽  
Author(s):  
Xin Xue ◽  
Gang Bao ◽  
Hai-Qing Zhang ◽  
Ning-Yi Zhao ◽  
Yuan Sun ◽  
...  

: The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of Ki from 102 to 103 nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.


The task of predicting target proteins for new drug discovery is typically difficult. Target proteins are biologically most important to control a keen functional process. The recent research of experimental and computational -based approaches has been widely used to predict target proteins using biological networks analysis techniques. Perhaps with available methods and statistical algorithm needs to be modified and should be clearer to tag the main target. Meanwhile identifying wrong protein leads to unwanted molecular interaction and pharmacological activity. In this research work, a novel method to identify essential target proteins using integrative graph coloring algorithm has been proposed. The proposed integrative approach helps to extract essential proteins in protein-protein interaction network (PPI) by analyzing neighborhood of the active target protein. Experimental results reviewed based on protein-protein interaction network for homosapiens showed that AEIAPP based approach shows an improvement in the essential protein identification by assuming the source protein as biologically proven protein. The AEIAPP statistical model has been compared with other state of art approaches on human PPI for various diseases to produce good accurate outcome in faster manner with little memory consumption.


2020 ◽  
Author(s):  
SANGEETA KUMARI

Abstract Objective This study’s primary goal is unraveling the mechanism of action of bioactives of Curcuma longa L. at the molecular level using protein-protein interaction network.Results We used target proteins to create protein-protein interaction network (PPIN) and identified significant node and edge attributes of PPIN. We identified the cluster of proteins in the PPIN, which were used to identify enriched pathways. . We identified closeness centrality and jaccard score as most important node and edge attribute of the PPIN respectively. The enriched pathways of various clusters were overlapped suggesting synergistic mechanism of action. The three pathways found to be common among three clusters were Gonadotropin-releasing hormone receptor pathway, Endothelin signaling pathway, and Inflammation mediated by chemokine and cytokine signaling pathway.


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