Structure-based virtual screening and molecular dynamics simulation for the identification of sphingosine kinase-2 inhibitors as potential analgesics

Neuropathic pain is due to an injury or disease of the somatosensory nervous system, which accounts for a significant economical and health burden to society. Due to poor understanding of their underlying mechanisms, the available treatments merely provide symptomatic relief and precipitates a variety of adverse effects. This suggests that there is an unmet medical need that must be addressed with effective strategies for the development of novel therapeutics. Sphingosine kinase 2 (SphK2) is an oncogenic lipid kinase that has emerged as a promising target for chronic pain and other diseases. In the present study, we have explored the structure-based virtual high-throughput screening of the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE) to identify potent natural products as inhibitors of SphK2. A molecular docking study was performed to calculate binding affinities and specificity to identify potential leads against SphK2. Initially, hits were selected by the implementation of absorption, distribution, metabolism, excretion and toxicity properties, Lipinski rule, and PAINS filters. The top-scoring hits also exhibiting an optimal ADMET profile were subjected to MM/GBSA free binding free energy calculation and molecular dynamics simulation. The results from molecular dynamics simulation revealed a stable ligand -SphK2 complex with protein and ligand RMSD within reasonable limits. Overall, we identified compounds, NuBBE_972 and NuBBE_1107 as potential inhibitors of SphK2 with optimal pharmacokinetic properties which have the potential to be developed as novel therapeutics for the management of chronic pain. Communicated by Ramaswamy H. Sarma

神经病理性疼痛由躯体感觉神经系统的损伤或疾病引发，给社会带来了沉重的经济与健康负担。由于对其潜在致病机制认知不足，现有治疗手段仅能缓解症状，且会引发多种不良反应。这表明当前存在未被满足的医疗需求，亟需通过有效策略开发新型治疗药物。鞘氨醇激酶2（Sphingosine kinase 2, SphK2）是一种致癌性脂质激酶，现已成为慢性疼痛与其他疾病的极具潜力的治疗靶点。本研究针对天然产物生物测定、生态生理学与生物合成核心数据库（Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database, NuBBE）开展基于结构的虚拟高通量筛选，以筛选出可作为SphK2抑制剂的高效天然产物。研究通过分子对接实验计算配体与靶点的结合亲和力与特异性，以筛选抗SphK2的潜在先导化合物。首先，通过筛选吸收、分布、代谢、排泄与毒性（ADMET）性质、Lipinski规则以及PAINS过滤器，初步获得命中化合物。随后，对同时具备最优ADMET特性的高分命中化合物开展MM/GBSA结合自由能计算与分子动力学模拟。分子动力学模拟结果显示，配体与SphK2形成的复合物稳定性良好，蛋白质与配体的均方根偏差（RMSD）均处于合理区间。总体而言，本研究筛选得到NuBBE_972与NuBBE_1107两种化合物，它们作为SphK2潜在抑制剂具备最优的药代动力学特性，有望开发为用于慢性疼痛管理的新型治疗药物。本文由Ramaswamy H. Sarma转交。