Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing “Omics Data” to Druggability Simulations

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.多发性硬化（Multiple sclerosis, MS）是中枢神经系统最常见的慢性炎症性脱髓鞘疾病。目前临床可用的MS治疗药物仅能提供症状获益，尚无根治手段。大规模多组学数据与网络理论的出现为MS的药物研发提供了新机遇，二者是开发新型治疗药物的极具前景的策略。本研究提出了一种整合生物分子网络建模与结构动力学分析的计算框架，以助力发掘具有MS潜在治疗活性的新型药物。首先，我们开发了一种全新的基于最短路径的算法，通过对蛋白质相互作用网络开展全新的拓扑与功能探究，对差异表达基因进行优先级排序。随后，通路富集分析与靶点成药性评估显示，参与核因子κB（NF-κB）信号通路的肿瘤坏死因子α诱导蛋白3（TNF-α-induced protein 3, TNFAIP3）可作为MS的潜在治疗靶点。最后，对TNFAIP3二聚体开展成药性模拟与突变富集分析，确定了两个可药用位点。针对这两个位点基于药效团模型的虚拟筛选最终得到30个能量得分较低的命中化合物。综上，这种基于“组学数据”分析与成药性模拟的新型方法是一套系统性研究策略，可阐明疾病机制并将其与化学空间相联结以开发治疗手段，同时亦可推广应用于其他复杂疾病。