A Comparative Study of Cluster Detection Algorithms in Protein–Protein Interaction for Drug Target Discovery and Drug Repurposing

ABSTRACTHost factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. This knowledge can be utilized to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets.ImportanceSeasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year, with a constant threat of pandemic infection. Even so, the FDA has only approved four treatments, two of which are unsuited for at risk groups such as children and those with breathing complications. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. Controllability analyses identify key regulating host factors of the infected cell’s progression, findings which are supported by biological context. These results are beneficial to future studies of influenza virus, both experimental and computational.

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Modelling human protein interaction networks as metric spaces has potential in disease research and drug target discovery

BMC Systems Biology ◽

10.1186/1752-0509-8-68 ◽

2014 ◽

Vol 8 (1) ◽

pp. 68 ◽

Cited By ~ 5

Author(s):

Emad Fadhal ◽

Eric C Mwambene ◽

Junaid Gamieldien

Keyword(s):

Protein Interaction ◽

Drug Target ◽

Metric Spaces ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Human Protein ◽

Drug Target Discovery ◽

Target Discovery ◽

Disease Research ◽

Human Protein Interaction

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The Role of Emerging Genomics and Proteomics Technologies in Cancer Drug Target Discovery

Current Cancer Drug Targets ◽

10.2174/1568009043481588 ◽

2004 ◽

Vol 4 (2) ◽

pp. 111-124 ◽

Cited By ~ 13

Author(s):

Patrick Onyango

Keyword(s):

Drug Target ◽

Cancer Drug ◽

Drug Target Discovery ◽

Target Discovery ◽

Genomics And Proteomics

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Abstract 2139: Enabling cancer drug target discovery through genome-scale identification of synthetic lethal paralog pairs

10.1158/1538-7445.am2021-2139 ◽

2021 ◽

Author(s):

Phoebe C. Parrish ◽

James D. Thomas ◽

Shriya Kamlapurkar ◽

Robert K. Bradley ◽

Alice H. Berger

Keyword(s):

Drug Target ◽

Cancer Drug ◽

Drug Target Discovery ◽

Target Discovery ◽

Synthetic Lethal ◽

Genome Scale

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An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors

Molecules ◽

10.3390/molecules23123174 ◽

2018 ◽

Vol 23 (12) ◽

pp. 3174 ◽

Cited By ~ 1

Author(s):

Xin Xue ◽

Gang Bao ◽

Hai-Qing Zhang ◽

Ning-Yi Zhao ◽

Yuan Sun ◽

...

Keyword(s):

Protein Interaction ◽

Drug Target ◽

P53 Protein ◽

Pharmacophore Model ◽

Cellular Level ◽

Complex Structures ◽

Ligand Complex ◽

Null Cell ◽

Protein Protein Interaction ◽

First Time

: 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.

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Application of genomics and proteomics in drug target discovery

Genetics and Molecular Research ◽

10.4238/2014.january.10.11 ◽

2014 ◽

Vol 13 (1) ◽

pp. 198-204 ◽

Cited By ~ 1

Author(s):

H.M. Zhang ◽

Z.R. Nan ◽

G.Q. Hui ◽

X.H. Liu ◽

Y. Sun

Keyword(s):

Drug Target ◽

Drug Target Discovery ◽

Target Discovery ◽

Genomics And Proteomics

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Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis

BMC Biotechnology ◽

10.1186/s12896-021-00680-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Masoumeh Adhami ◽

Balal Sadeghi ◽

Ali Rezapour ◽

Ali Akbar Haghdoost ◽

Habib MotieGhader

Keyword(s):

Network Analysis ◽

Protein Interaction ◽

Target Therapy ◽

Interaction Network ◽

Drug Repurposing ◽

Ppi Network ◽

Protein Protein Interaction ◽

Candidate Drug ◽

Starting Point ◽

Experimental Validations

Abstract Background The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules. Results Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19. Conclusions Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.

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