Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics Data" to Druggability Simulations
Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics Data" to Druggability Simulations
复制标题
基于网络的多发性硬化症靶点优先排序和候选药物识别：从分析“组学数据”到成药性模拟
DOI：
10.1021/acschemneuro.1c00011
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发表时间：
2021
影响因子：
5
通讯作者：
Guang Hu
中科院分区：
文献类型：
--
作者：
Ji Yang;Hongchun Li;Fan Wang;Fei Xiao;Wenying Yan;Guang Hu
多发性硬化症（MS）是中枢神经系统最常见的慢性炎症性脱髓鞘疾病。虽然目前可用于多发性硬化症的药物提供了症状性益处，但没有治愈性治疗。大规模多组学数据和网络理论的出现为MS药物发现提供了新的机会，因为它们是开发新药的有希望的策略。在这项研究中，我们提出了一个结合生物分子网络建模和结构动力学分析的计算框架，以促进发现具有ms潜在活性的新药。首先，我们开发了一个新的基于最短路径的算法，该算法通过对蛋白质-蛋白质相互作用网络的新拓扑和功能探索来优先考虑差异表达基因。途径富集分析和靶点可药物性评估提示TNF-α-诱导蛋白3 （TNF -α-induced protein 3, TNFAIP3）参与NF-κ B信号传导，可能是ms的潜在治疗靶点。最后，TNFAIP3二聚体的可药物性模拟和突变富集分析显示了两个可药物位点。后续基于药效团模型的两个位点的虚拟筛选产生了30个低能量得分的击中化合物。总之，这种基于分析“组学数据”和进行药物模拟的新方法是一种系统的方法，可以揭示疾病机制并将其与化学空间联系起来，从而开发治疗方法，并可应用于其他复杂疾病。
Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. While the drugs currently available for MS provide symptomatic benefit, there is no curative treatment. The emergence of large-scale multiomics data and network theory provide new opportunities for drug discovery in MS, as these are promising strategies for developing novel drugs. In this study, we proposed a computational framework that combined biomolecular network modeling and structural dynamics analysis to facilitate the discovery of new drugs with potential activity in MS. First, we developed a new shortest path-based algorithm that prioritized differentially expressed genes using a newly topological and functional exploration of protein–protein interaction network. Then, pathway enrichment analysis and an assessment of target druggability suggested that TNF-α-induced protein 3 (TNFAIP3), which is involved in NF-κ B signaling, could be a potential therapeutic target for MS. Finally, druggability simulations and mutation enrichment analysis of the TNFAIP3 dimer presented two druggable sites. Follow-up pharmacophore model-based virtual screening of the two sites yielded 30 hit compounds with low energy scores. In summary, this novel method based on analyzing “omics data” and performing druggability simulations, is a systematic approach that unravels disease mechanisms and links them to the chemical space to develop treatments and can be applied to other complex diseases.