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基于蛋白质-蛋白质相互作用网络的最大流方法对结核分枝杆菌H37Rv潜在药物靶点进行优先级排序

Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network.

作者信息

Melak Tilahun, Gakkhar Sunita

机构信息

Department of Computer Science, Dilla University, Gedeo, Ethiopia,

出版信息

Clin Transl Med. 2015 Dec;4(1):61. doi: 10.1186/s40169-015-0061-6. Epub 2015 Jun 5.

Abstract

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

摘要

背景

尽管实施了多种策略,但结核病仍然是一个极其严重的全球公共卫生问题,每年导致数百万人感染和死亡。这主要是由于耐药性结核菌株的出现。目前针对耐药结核病的治疗策略疗程更长、费用更高且有副作用。这凸显了识别和确定新药靶点优先级的重要性。本研究旨在根据结核分枝杆菌H37Rv的潜在药物靶点与耐药基因的关联来确定其优先级。

方法

利用STRING数据库中的数据集构建病原体的加权蛋白质组相互作用网络。仅考虑数据集中相互作用综合得分值≥770的子集。采用最大流方法对潜在药物靶点进行优先级排序。通过比较基因组和网络中心性分析获得潜在药物靶点。从文献中检索经过整理的耐药基因集。还进行了详细的文献综述和对该方法的额外评估以进行验证。

结果

通过比较基因组分析获得了537种对病原体至关重要且与人类无同源性的蛋白质列表。通过网络中心性测量,发现其中131种位于蛋白质组网络重心的紧邻区域内。这些蛋白质根据其到耐药基因的最大流值进一步排序,并被提议作为病原体的可靠药物靶点。还鉴定了与宿主相互作用的蛋白质以了解感染机制。

结论

基于结核分枝杆菌H37Rv的潜在药物靶点与现有药物耐药基因的关联,成功地确定了它们的优先级,因为抑制与耐药基因有最大流的蛋白质更有可能破坏与这些基因的通信,这被认为会增加靶点的可成药性。对排名前14的蛋白质进行的有针对性的文献综述表明,该列表中的许多蛋白质已被提议作为病原体的药物靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c41/4467812/c3d9a219b1f4/40169_2015_61_Fig1_HTML.jpg

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