首页> 外文期刊>Infection, Genetics and Evolution: Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases >Estimating novel potential drug targets of Plasmodium falciparum by analysing the metabolic network of knock-out strains in silico
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Estimating novel potential drug targets of Plasmodium falciparum by analysing the metabolic network of knock-out strains in silico

机译:通过分析计算机中敲除菌株的代谢网络,估计恶性疟原虫的新型潜在药物靶标

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摘要

Malaria is one of the world's most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Biomedical research could enable treating the disease by effectively and specifically targeting essential enzymes of this parasite. However, the parasite has developed resistance to existing drugs making it indispensable to discover new drugs. We have established a simple computational tool which analyses the topology of the metabolic network of P. falciparum to identify essential enzymes as possible drug targets. We investigated the essentiality of a reaction in the metabolic network by deleting (knocking-out) such a reaction in silico. The algorithm selected neighbouring compounds of the investigated reaction that had to be produced by alternative biochemical pathways. Using breadth first searches, we tested qualitatively if these products could be generated by reactions that serve as potential deviations of the metabolic flux. With this we identified 70 essential reactions. Our results were compared with a comprehensive list of 38 targets of approved malaria drugs. When combining our approach with an in silico analysis performed recently [Yeh, I., Hanekamp, T., Tsoka, S., Karp, P.D., Altman, R.B., 2004. Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res. 14, 917-924] we could improve the precision of the prediction results. Finally we present a refined list of 22 new potential candidate targets for P. falciparum, half of which have reasonable evidence to be valid targets against micro-organisms and cancer.
机译:疟疾是世界上最常见和最严重的疾病之一,每年导致约300万人死亡。它最严重的发生是由原生动物恶性疟原虫引起的。生物医学研究可以通过有效且特异性地靶向该寄生虫的必需酶来治疗该疾病。但是,该寄生虫对现有药物产生了抗药性,因此发现新药必不可少。我们已经建立了一个简单的计算工具,该工具可以分析恶性疟原虫代谢网络的拓扑结构,以鉴定必需的酶作为可能的药物靶标。我们通过在计算机上删除(敲除)这样的反应来研究代谢网络中反应的必要性。该算法选择了必须通过替代生化途径产生的所研究反应的邻近化合物。使用广度优先搜索,我们定性地测试了这些产物是否可能由作为代谢通量潜在偏差的反应产生。据此,我们确定了70个基本反应。将我们的结果与38种已批准的疟疾药物靶标进行了比较。当将我们的方法与最近进行的计算机分析相结合时[Yeh,I.,Hanekamp,T.,Tsoka,S.,Karp,PD,Altman,RB,2004。恶性疟原虫代谢的计算分析:组织基因组信息以促进药物治疗发现。基因组研究。 14,14,917-924]可以提高预测结果的精度。最后,我们列出了22个新的恶性疟原虫潜在候选目标的精确清单,其中一半具有合理的证据证明是对抗微生物和癌症的有效目标。

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