Discovering drug targets for neglected diseases using a pharmacophylogenomic cloud workflow

Kary A. C. S. Ocaña, Daniel de Oliveira, Jonas Dias, Eduardo S. Ogasawara, M. Mattoso
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引用次数: 13

Abstract

Illnesses caused by parasitic protozoan are a research priority. A representative group of these illnesses is the commonly known as Neglected Tropical Diseases (NTD). NTD specially attack low socioeconomic population around the world and new anti-protozoan inhibitors are needed and several drug discovery projects focus on researching new drug targets. Pharmacophylogenomics is a novel bioinformatics field that aims at reducing the time and the financial cost of the drug discovery process. Pharmacophylogenomic analyses are applied mainly in the early stages of the research phase in drug discovery. Pharmacophylogenomic analysis executes several bioinformatics programs in a coherent flow to identify homologues sequences, construct phylogenetic trees and execute evolutionary and structural experiments. This way, it can be modeled as scientific workflows. Pharmacophylogenomic analysis workflows are complex, computing and data intensive and may execute during weeks. This way, it benefits from parallel execution. We propose SciPPGx, a scientific workflow that aims at providing thorough inferring support for pharmacophylogenomic hypotheses. SciPPGx is executed in parallel in a cloud using SciCumulus workflow engine. Experiments show that SciPPGx considerably reduces the total execution time up to 97.1% when compared to a sequential execution. We also present representative biological results taking advantage of the inference covering several related bioinformatics overviews.
使用药理学云工作流发现被忽视疾病的药物靶点
由寄生原生动物引起的疾病是研究的重点。这些疾病的一个代表性群体是通常被忽视的热带病(NTD)。NTD特别针对世界各地低社会经济人群,需要新的抗原生动物抑制剂,一些药物发现项目正在研究新的药物靶点。药物基因组学是一个新的生物信息学领域,旨在减少药物发现过程的时间和财务成本。药物基因组学分析主要应用于药物发现的早期研究阶段。药物基因组学分析在一个连贯的流程中执行几个生物信息学程序,以识别同源序列,构建系统发育树并执行进化和结构实验。这样,就可以将其建模为科学工作流。药物基因组学分析工作流程复杂,计算和数据密集,可能在数周内完成。这样,它就可以从并行执行中获益。我们提出SciPPGx,这是一个科学的工作流程,旨在为药物基因组学假设提供全面的推断支持。SciPPGx在云中使用SciCumulus工作流引擎并行执行。实验表明,与顺序执行相比,SciPPGx大大减少了总执行时间,最多可减少97.1%。我们还介绍了代表性的生物学结果,利用涵盖几个相关生物信息学概述的推理。
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