School of Life Science, Northwestern Polytechnical University, Xi'an, 710072, China.
Institute of Basic Research in Clinical MedicineChina Academy of Chinese Medical Sciences, Beijing, 100700, China.
J Transl Med. 2022 Feb 3;20(1):67. doi: 10.1186/s12967-022-03279-w.
Chinese herbal medicine is made up of hundreds of natural drug molecules and has played a major role in traditional Chinese medicine (TCM) for several thousand years. Therefore, it is of great significance to study the target of natural drug molecules for exploring the mechanism of treating diseases with TCM. However, it is very difficult to determine the targets of a fresh natural drug molecule due to the complexity of the interaction between drug molecules and targets. Compared with traditional biological experiments, the computational method has the advantages of less time and low cost for targets screening, but it remains many great challenges, especially for the molecules without social ties.
This study proposed a novel method based on the Cosine-correlation and Similarity-comparison of Local Network (CSLN) to perform the preliminary screening of targets for the fresh natural drug molecules and assign weights to them through a trained parameter.
The performance of CSLN is superior to the popular drug-target-interaction (DTI) prediction model GRGMF on the gold standard data in the condition that is drug molecules are the objects for training and testing. Moreover, CSLN showed excellent ability in checking the targets screening performance for a fresh-natural-drug-molecule (scenario simulation) on the TCMSP (13 positive samples in top20), meanwhile, Western-Blot also further verified the accuracy of CSLN.
In summary, the results suggest that CSLN can be used as an alternative strategy for screening targets of fresh natural drug molecules.
中草药由数百种天然药物分子组成，在中国传统医学（TCM）中已有数千年的历史，因此，研究天然药物分子的靶点对于探索用 TCM 治疗疾病的机制具有重要意义。然而，由于药物分子与靶点之间相互作用的复杂性，确定新鲜天然药物分子的靶点非常困难。与传统的生物实验相比，计算方法在靶点筛选方面具有时间短、成本低的优点，但仍存在许多巨大的挑战，特别是对于没有社会关系的分子。
本研究提出了一种基于余弦相关和局部网络相似性比较（CSLN）的新方法，用于对新鲜天然药物分子的靶点进行初步筛选，并通过训练参数为其分配权重。
在以药物分子为训练和测试对象的情况下，CSLN 的性能优于流行的药物-靶标相互作用（DTI）预测模型 GRGMF 在金标准数据上的性能。此外，CSLN 在 TCMSP （前 20 个中有 13 个阳性样本）上对新鲜天然药物分子的靶点筛选性能检查中表现出出色的能力，同时 Western-Blot 也进一步验证了 CSLN 的准确性。
总之，结果表明 CSLN 可作为筛选新鲜天然药物分子靶点的替代策略。
