Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
Division of Environmental Science and Ecological Engineering, Korea University, 145 Anam-ro, Seungbuk-gu, Seoul, 02841, Republic of Korea.
BMC Complement Med Ther. 2024 Jun 6;24(Suppl 2):218. doi: 10.1186/s12906-024-04503-4.
Natural herbs are frequently used to treat diseases or to relieve symptoms in many countries. Moreover, as their safety has been proven for a long time, they are considered as main sources of new drug development. However, in many cases, the herbs are still prescribed relying on ancient records and/or traditional practices without scientific evidences. More importantly, the medicinal efficacy of the herbs has to be evaluated in the perspective of MCMT (multi-compound multi-target) effects, but most efforts focus on identifying and analyzing a single compound experimentally. To overcome these hurdles, computational approaches which are based on the scientific evidences and are able to handle the MCMT effects are needed to predict the herb-disease associations.
In this study, we proposed a network-based in silico method to predict the herb-disease associations. To this end, we devised a new network-based measure, WACP (weighted average closest path length), which not only quantifies proximity between herb-related genes and disease-related genes but also considers compound compositions of each herb. As a result, we confirmed that our method successfully predicts the herb-disease associations in the human protein interactome (AUROC = 0.777). In addition, we observed that our method is superior than the other simple network-based proximity measures (e.g. average shortest and closest path length). Additionally, we analyzed the associations between Brassica oleracea var. italica and its known associated diseases more specifically as case studies. Finally, based on the prediction results of the WACP, we suggested novel herb-disease pairs which are expected to have potential relations and their literature evidences.
This method could be a promising solution to modernize the use of the natural herbs by providing the scientific evidences about the molecular associations between the herb-related genes targeted by multiple compounds and the disease-related genes in the human protein interactome.
在许多国家，人们经常使用天然草药来治疗疾病或缓解症状。此外，由于它们的安全性已经得到长期验证，因此被认为是新药开发的主要来源。然而，在许多情况下，这些草药仍然是根据古代记录和/或传统实践来开处方的，而没有科学证据。更重要的是，草药的药用功效必须从多化合物多靶点（MCMT）效应的角度来评估，但大多数研究都集中在实验中识别和分析单一化合物上。为了克服这些障碍，需要基于科学证据并能够处理 MCMT 效应的计算方法来预测草药-疾病关联。
在这项研究中，我们提出了一种基于网络的计算方法来预测草药-疾病关联。为此，我们设计了一种新的基于网络的度量标准，即 WACP（加权平均最近路径长度），它不仅量化了草药相关基因和疾病相关基因之间的接近程度，还考虑了每种草药的化合物组成。结果表明，我们的方法成功地预测了人类蛋白质相互作用组中的草药-疾病关联（AUROC=0.777）。此外，我们观察到我们的方法优于其他简单的基于网络的接近度量标准（例如平均最短和最近路径长度）。此外，我们更具体地分析了 Brassica oleracea var. italica 及其已知相关疾病之间的关联作为案例研究。最后，基于 WACP 的预测结果，我们提出了一些新的草药-疾病对，这些对可能具有潜在的关系，并提供了它们的文献证据。
该方法为现代天然草药的应用提供了一种有前途的解决方案，它为人类蛋白质相互作用组中多种化合物靶向的草药相关基因与疾病相关基因之间的分子关联提供了科学证据。
