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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein‐protein interaction network
|
|---|---|
| Published in |
Clinical and Translational Medicine, June 2015
|
| DOI | 10.1186/s40169-015-0061-6 |
| Pubmed ID | |
| Authors |
Tilahun Melak, Sunita Gakkhar |
| Abstract |
In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv based on their flow to resistance genes. The weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ≥770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation. A list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism. Potential drug targets of Mycobacterium tuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to resistance genes is more likely to disrupt the communication to these genes. Purposely selected literature review of the top 14 proteins showed that many of them in this list were proposed as drug targets of the pathogen. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 30 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 30% |
| Researcher | 4 | 13% |
| Student > Doctoral Student | 3 | 10% |
| Student > Bachelor | 3 | 10% |
| Student > Postgraduate | 3 | 10% |
| Other | 3 | 10% |
| Unknown | 5 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 17% |
| Computer Science | 5 | 17% |
| Chemistry | 3 | 10% |
| Medicine and Dentistry | 3 | 10% |
| Immunology and Microbiology | 3 | 10% |
| Other | 5 | 17% |
| Unknown | 6 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 July 2015.
All research outputs
#15,091,226
of 25,374,647 outputs
Outputs from Clinical and Translational Medicine
#427
of 1,060 outputs
Outputs of similar age
#136,435
of 280,810 outputs
Outputs of similar age from Clinical and Translational Medicine
#2
of 6 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,060 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,810 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.