Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein‐protein interaction network
Abstract BackgroundIn 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 str...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-12-01
|
| Series: | Clinical and Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40169-015-0061-6 |
| _version_ | 1819013876598964224 |
|---|---|
| author | Tilahun Melak Sunita Gakkhar |
| author_facet | Tilahun Melak Sunita Gakkhar |
| author_sort | Tilahun Melak |
| collection | DOAJ |
| description | Abstract BackgroundIn 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 Mycobacteriumtuberculosis H37Rv based on their flow to resistance genes. MethodsThe 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. ResultsA 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. ConclusionPotential drug targets of Mycobacteriumtuberculosis 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. |
| first_indexed | 2024-12-21T02:06:55Z |
| format | Article |
| id | doaj.art-f9bfba225891468798896bbe144e4db3 |
| institution | Directory Open Access Journal |
| issn | 2001-1326 |
| language | English |
| last_indexed | 2024-12-21T02:06:55Z |
| publishDate | 2015-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Medicine |
| spelling | doaj.art-f9bfba225891468798896bbe144e4db32022-12-21T19:19:30ZengWileyClinical and Translational Medicine2001-13262015-12-0141n/an/a10.1186/s40169-015-0061-6Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein‐protein interaction networkTilahun Melak0Sunita Gakkhar1Department of Computer ScienceDilla UniversityGedeoEthiopiaDepartment of MathematicsIIT RoorkeeIndiaAbstract BackgroundIn 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 Mycobacteriumtuberculosis H37Rv based on their flow to resistance genes. MethodsThe 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. ResultsA 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. ConclusionPotential drug targets of Mycobacteriumtuberculosis 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.https://doi.org/10.1186/s40169-015-0061-6Centrality measuresDrug‐resistance tuberculosisEssential genesProteome networkResistance genes |
| spellingShingle | Tilahun Melak Sunita Gakkhar Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein‐protein interaction network Clinical and Translational Medicine Centrality measures Drug‐resistance tuberculosis Essential genes Proteome network Resistance genes |
| title | Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein‐protein interaction network |
| title_full | Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein‐protein interaction network |
| title_fullStr | Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein‐protein interaction network |
| title_full_unstemmed | Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein‐protein interaction network |
| title_short | Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein‐protein interaction network |
| title_sort | maximum flow approach to prioritize potential drug targets of mycobacterium tuberculosis h37rv from protein protein interaction network |
| topic | Centrality measures Drug‐resistance tuberculosis Essential genes Proteome network Resistance genes |
| url | https://doi.org/10.1186/s40169-015-0061-6 |
| work_keys_str_mv | AT tilahunmelak maximumflowapproachtoprioritizepotentialdrugtargetsofmycobacteriumtuberculosish37rvfromproteinproteininteractionnetwork AT sunitagakkhar maximumflowapproachtoprioritizepotentialdrugtargetsofmycobacteriumtuberculosish37rvfromproteinproteininteractionnetwork |