Yadav Siddharth, Ahamad Shahzaib, Gupta Dinesh, Mathur Puniti
Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India.
Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India.
J Biomol Struct Dyn. 2023 Mar;41(5):1811-1827. doi: 10.1080/07391102.2021.2024449. Epub 2022 Jan 11.
Therapeutic agents being designed against COVID-19 have targeted either the virus directly or the host cellular machinery. A particularly attractive host target is the ubiquitous and constitutively active serine-threonine kinase, Protein kinase CK2 (CK2). CK2 enhances viral protein synthesis by inhibiting the sequestration of host translational machinery as stress granules and assists in viral egression via association with the N-protein at filopodial protrusions of the infected cell. CK2 inhibitors such as Silmitasertib have been proposed as possible therapeutic candidates in COVID-19 infections. The present study aims to optimize Silmitasertib, develop pharmacophore models and design unique scaffolds to modulate CK2. The lead optimization phase involved the generation of compounds structurally similar to Silmitasertib via bioisostere replacement followed by a multi-stage docking approach to identify drug-like candidates. Molecular dynamics (MD) simulations were performed for two promising candidates (ZINC-43206125 and PC-57664175) to estimate their binding stability and interaction. Top scoring candidates from the lead optimization phase were utilized to build ligand-based pharmacophore models. These models were then merged with structure-based pharmacophores (e-pharmacophores) to build a hybrid hypothesis. This hybrid hypothesis was validated against a decoy set and used to screen a diverse kinase inhibitors library to identify favored chemical features in the retrieved actives. These chemical features include; an anion, an aromatic ring and an H-bond acceptor. Based on the knowledge of these features; scaffold design was carried out which identified phenindiones, carboxylated steroids, macrocycles and peptides as novel scaffolds with the potential to modulate CK2.Communicated by Ramaswamy H. Sarma.
针对新型冠状病毒肺炎(COVID-19)设计的治疗药物,其靶点要么是病毒本身,要么是宿主细胞机制。一个特别有吸引力的宿主靶点是普遍存在且组成型激活的丝氨酸 - 苏氨酸激酶——蛋白激酶CK2(CK2)。CK2通过抑制宿主翻译机制作为应激颗粒的隔离来增强病毒蛋白合成,并通过在受感染细胞的丝状伪足突起处与N蛋白结合来协助病毒出芽。诸如西咪替丁之类的CK2抑制剂已被提议作为COVID-19感染的潜在治疗候选药物。本研究旨在优化西咪替丁,开发药效团模型并设计独特的支架来调节CK2。先导优化阶段包括通过生物电子等排体替换生成与西咪替丁结构相似的化合物,然后采用多阶段对接方法来识别类药物候选物。对两个有前景的候选物(ZINC-43206125和PC-57664175)进行了分子动力学(MD)模拟,以估计它们的结合稳定性和相互作用。利用先导优化阶段得分最高的候选物构建基于配体的药效团模型。然后将这些模型与基于结构的药效团(电子药效团)合并,以构建一个混合假设。该混合假设针对一组诱饵进行了验证,并用于筛选一个多样化的激酶抑制剂库,以识别检索到的活性物质中有利的化学特征。这些化学特征包括:一个阴离子、一个芳香环和一个氢键受体。基于对这些特征的了解,进行了支架设计,确定了菲茚二酮、羧化甾体、大环化合物和肽作为具有调节CK2潜力的新型支架。由拉马斯瓦米·H·萨尔马传达。