Exploring potential inhibitors against Kyasanur forest disease by utilizing molecular dynamics simulations and ensemble docking

Kyasanur forest disease (KFD) is a tick-borne, neglected tropical disease, caused by KFD virus (KFDV) which belongs to <i>Flavivirus</i> (Flaviviridae family). This emerging viral disease is a major threat to humans. Currently, vaccination is the only controlling method against the KFDV, and its effectiveness is very low. An effective control strategy is required to combat this emerging tropical disease using the existing resources. In this regard, <i>in silico</i> drug repurposing method offers an effective strategy to find suitable antiviral drugs against KFDV proteins. Drug repurposing is an effective strategy to identify new use for approved or investigational drugs that are outside the scope of their initial usage and the repurposed drugs have lower risk and higher safety compared to <i>de novo</i> developed drugs, because their toxicity and safety issues are profoundly investigated during the preclinical trials in human/other models. In the present work, we evaluated the effectiveness of the FDA approved and natural compounds against KFDV proteins using <i>in silico</i> molecular docking and molecular simulations. At present, no experimentally solved 3D structures for the KFD viral proteins are available in Protein Data Bank and hence their homology model was developed and used for the analysis. The present analysis successfully developed the reliable homology model of NS3 of KFDV, in terms of geometry and energy contour. Further, <i>in silico</i> molecular docking and molecular dynamics simulations successfully presented four FDA approved drugs and one natural compound against the NS3 homology model of KFDV. Communicated by Ramaswamy H. Sarma

基萨努森林病（Kyasanur forest disease, KFD）是一种经蜱传播的被忽视热带病，由隶属于黄病毒科（Flaviviridae）黄病毒属（Flavivirus）的基萨努森林病病毒（KFDV）引发。该新发病毒性疾病对人类健康构成重大威胁。目前，疫苗接种是唯一可用于对抗KFDV的防控手段，但其保护效力极低。亟需依托现有资源开发有效的防控策略，以应对这一新兴热带传染病。在此背景下，计算机辅助（in silico）药物重定位方法为靶向KFDV蛋白的抗病毒药物筛选提供了可行路径。药物重定位是指为获批或在研药物挖掘其初始适应症之外的新用途的有效策略；相较于全新（de novo）开发的药物，重定位药物的风险更低、安全性更高，因为其毒性与安全性问题已在人体或其他模型的临床前试验中得到充分研究。本研究通过计算机辅助分子对接与分子模拟技术，评估了FDA获批药物及天然化合物对KFDV蛋白的作用效果。目前蛋白质数据银行（Protein Data Bank, PDB）中尚无KFD病毒蛋白的实验解析三维结构，因此本研究构建了其同源模型用于后续分析。本分析成功构建了几何特性与能量分布均可靠的KFDV NS3蛋白同源模型。进一步通过计算机辅助分子对接与分子动力学模拟，本研究筛选得到4种FDA获批药物及1种天然化合物，可靶向作用于KFDV NS3蛋白同源模型。本文由Ramaswamy H. Sarma通讯。