A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.
A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.
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DOI：
10.1371/journal.pntd.0004300
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发表时间：
2016-01
影响因子：
3.8
通讯作者：
Agüero F
中科院分区：
文献类型：
--
作者：
Berenstein AJ;Magariños MP;Chernomoretz A;Agüero F
由于缺乏市场激励，被忽视疾病的药物开发历来受到阻碍。包含来自高通量筛选的化学信息的公共领域资源的出现正在改变这些疾病的药物发现的前景。在这项工作中，我们利用了广泛研究的生物体，如人类，小鼠，E。大肠杆菌和酵母等，以开发一种新的综合网络模型，优先考虑和识别候选药物靶标被忽视的病原体蛋白质组，和生物活性药物样分子。我们建模的基因组（蛋白质）和化学（生物活性化合物）的数据作为一个多层加权网络图，利用生物活性数据在221个物种，1.7 105化合物之间的化学相似性和1.67 105蛋白质之间的几个功能关系。这些关系包括同源性，蛋白质结构域的共享，并共同参与定义的生化途径。我们展示了这个网络图的应用程序的新的候选目标的优先级排序的问题，根据已知的化合物目标关联图中的信息。我们通过对已知的小鼠和克氏锥虫目标进行交叉验证来验证这种策略，并表明我们的方法优于经典的基于验证的方法。此外，我们的模型提供了额外的灵活性，因为可以考虑两种不同的网络定义，在这两种情况下都可以找到质上不同但合理的候选目标。我们还展示了该网络的应用，以建议在高通量筛选中对恶性疟原虫有活性的孤儿化合物的靶点。在这种情况下，我们的方法为查询分子提供了一个简化的目标蛋白质优先级列表，并显示出为每个化合物提出新的可测试假设的能力。此外，我们发现我们的网络模型所强调的一些预测得到了独立实验验证的支持，如文献中事后发现的那样。被忽视的热带疾病是人类传染病，往往与贫穷有关。从历史上看，制药行业缺乏兴趣导致缺乏良好的药物来对抗导致这些疾病的大多数病原体。最近，随着公共领域化学资源的出现和来自高通量筛选测定的数据的发布，开放化学信息的可用性增加。我们在这项工作中的目的是利用广泛研究的生物体，如人类，小鼠，E。大肠杆菌和酵母等，以优先考虑和识别被忽视的病原体蛋白质组中的候选药物靶点，以及药物样生物活性分子，以促进针对被忽视疾病的药物开发。我们解决这个问题的方法依赖于应用生物信息学和计算生物学策略来模拟大型数据集，这些数据集涵盖了完整的蛋白质组和来自公开来源的广泛的化学信息。因此，我们能够优先考虑药物靶点，并确定孤儿生物活性药物的潜在靶点。
Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature. Neglected tropical diseases are human infectious diseases that are often associated with poverty. Historically, lack of interest from the pharmaceutical industry resulted in the lack of good drugs to combat the majority of the pathogens that cause these diseases. Recently, the availability of open chemical information has increased with the advent of public domain chemical resources and the release of data from high throughput screening assays. Our aim in this work was to make use of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to prioritize and identify candidate drug targets in neglected pathogen proteomes, and drug-like bioactive molecules to foster drug development against neglected diseases. Our approach to the problem relied on applying bioinformatics and computational biology strategies to model large datasets spanning complete proteomes and extensive chemical information from publicly available sources. As a result, we were able to prioritize drug targets and identify potential targets for orphan bioactive drugs.
影响因子：
1.9
作者：
McGowan S
通讯作者：
McGowan S