Biomolecular Network-based Study of a Parasitic Disease and Therapeutic Drugs

ICT Focus Pub Date : 2022-09-29 DOI:10.58873/sict.v1i1.31
Altankhuyag Avirmed, Uranchimeg Erdenedalai, Selenge Erdenechimeg, Yansen Su, Tseren-Onolt Ishdorj
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Abstract

Computational drug repurposing methods, particularly biomolecular network-based disease-drug-target interaction models, are essential tools for integrating large-scale heterogenous molecular information and revealing functional mechanisms, as well as for main regulatory modules of interactants which can be useful in developing new drugs. In the present study, a drug-centric network for a parasitic disease (Echinococcosis) and therapeutic drugs have been considered. A complex network with more than 12,000 vertices and more than 33,000 edges representing interactions of 84 echinococcosis-related genes with associated proteins was built and analyzed. The networks of disease similarity and drug similarity were constructed based on the complex network. As a result, three drugs (D08356, D00701, and D00506) associated with three candidate diseases through three pathways and a protein complex have been extracted. This effort tries to predict the anti-echinococcosis effects of the drugs’ combinations with benzimidazole.
基于生物分子网络的寄生虫病及治疗药物研究
计算药物再利用方法,特别是基于生物分子网络的疾病-药物-靶点相互作用模型,是整合大规模异质分子信息和揭示功能机制的重要工具,也是相互作用主要调控模块的重要工具,可用于开发新药。在本研究中,一个以药物为中心的网络寄生虫病(棘球蚴病)和治疗药物已被考虑。构建并分析了一个包含超过12,000个顶点和超过33,000个边的复杂网络,代表了84种棘球蚴病相关基因与相关蛋白的相互作用。在复杂网络的基础上构建了疾病相似网络和药物相似网络。结果,三种药物(D08356、D00701和D00506)通过三种途径和一种蛋白复合物与三种候选疾病相关。本研究试图预测这些药物与苯并咪唑联合使用的抗棘球蚴病效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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