Rabaan Ali A, Halwani Muhammad A, Garout Mohammed, Alotaibi Jawaher, AlShehail Bashayer M, Alotaibi Nouf, Almuthree Souad A, Alshehri Ahmad A, Alshahrani Mohammed Abdulrahman, Othman Basim, Alqahtani Abdulaziz, Alissa Mohammed
Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, 31311, Dhahran, Saudi Arabia.
College of Medicine, Alfaisal University, 11533, Riyadh, Saudi Arabia.
Mol Divers. 2024 Oct;28(5):3261-3278. doi: 10.1007/s11030-023-10753-0. Epub 2023 Nov 5.
Marburg virus disease (MVD) is caused by the Marburg virus, a one-of-a-kind zoonotic RNA virus from the genus Filovirus. Thus, this current study employed AI-based QSAR and molecular docking-based virtual screening for identifying potential binders against the target protein (nucleoprotein (NP)) of the Marburg virus. A total of 2727 phytochemicals were used for screening, out of which the top three compounds (74977521, 90470472, and 11953909) were identified based on their predicted bioactivity (pIC50) and binding score (< - 7.4 kcal/mol). Later, MD simulation in triplicates and trajectory analysis were performed which showed that 11953909 and 74977521 had the most stable and consistent complex formations and had the most significant interactions with the highest number of hydrogen bonds. PCA (principal component analysis) and FEL (free energy landscape) analysis indicated that these compounds had favourable energy states for most of the conformations. The total binding free energy of the compounds using the MM/GBSA technique showed that 11953909 (ΔG = - 30.78 kcal/mol) and 74977521 (ΔG = - 30 kcal/mol) had the highest binding affinity with the protein. Overall, this in silico pipeline proposed that the phytochemicals 11953909 and 74977521 could be the possible binders of NP. This study aimed to find phytochemicals inhibiting the protein's function and potentially treating MVD.
马尔堡病毒病（MVD）由马尔堡病毒引起，马尔堡病毒是丝状病毒属中一种独特的人畜共患RNA病毒。因此，本研究采用基于人工智能的定量构效关系（QSAR）和基于分子对接的虚拟筛选方法，以鉴定针对马尔堡病毒靶蛋白（核蛋白（NP））的潜在结合剂。总共使用了2727种植物化学物质进行筛选，其中根据预测的生物活性（pIC50）和结合分数（< -7.4 kcal/mol）确定了排名前三的化合物（74977521、90470472和11953909）。随后进行了三次重复的分子动力学（MD）模拟和轨迹分析，结果表明11953909和74977521具有最稳定和一致的复合物形成，并且与最多数量的氢键具有最显著的相互作用。主成分分析（PCA）和自由能景观（FEL）分析表明，这些化合物在大多数构象中具有有利的能量状态。使用MM/GBSA技术计算的化合物总结合自由能表明，11953909（ΔG = -30.78 kcal/mol）和74977521（ΔG = -30 kcal/mol）与该蛋白具有最高的结合亲和力。总体而言，这种计算机模拟流程表明植物化学物质11953909和74977521可能是NP的结合剂。本研究旨在寻找抑制该蛋白功能并可能治疗马尔堡病毒病的植物化学物质。
