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迈向鉴定针对默克尔细胞多瘤病毒的天然抗病毒候选药物:计算药物设计方法

Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches.

作者信息

Asseri Amer H, Alam Md Jahidul, Alzahrani Faisal, Khames Ahmed, Pathan Mohammad Turhan, Abourehab Mohammed A S, Hosawi Salman, Ahmed Rubaiat, Sultana Sifat Ara, Alam Nazia Fairooz, Alam Nafee-Ul, Alam Rahat, Samad Abdus, Pokhrel Sushil, Kim Jin Kyu, Ahammad Foysal, Kim Bonglee, Tan Shing Cheng

机构信息

Biochemistry Department, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia.

Centre for Artificial Intelligence in Precision Medicines, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia.

出版信息

Pharmaceuticals (Basel). 2022 Apr 20;15(5):501. doi: 10.3390/ph15050501.

Abstract

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between -6.5 and -7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus.

摘要

默克尔细胞癌(MCC)是一种罕见的侵袭性皮肤癌,主要由默克尔细胞多瘤病毒(MCPyV)引起。大多数MCC肿瘤表达MCPyV大T(LT)抗原,并在癌蛋白的促生长活性中起重要作用。截短的LT通过激活病毒复制机制促进肿瘤发生以及宿主细胞增殖,而在人类中抑制这种蛋白会大幅降低与相应癌症相关的细胞生长。我们的研究旨在鉴定能够通过阻断病毒LT蛋白的活性来抑制恶性肿瘤(尤其是侵袭性肿瘤)增殖的小分子样天然抗病毒候选物。为了鉴定针对目标蛋白的潜在化合物,本研究应用了包括分子对接、ADME(吸收、分布、代谢和排泄)、毒性、分子动力学(MD)模拟以及分子力学广义玻恩表面积(MM-GBSA)方法在内的计算药物设计。最初,使用分子对接模拟方法对从104种药用植物中分离出的总共2190种植物化学物质进行了筛选,从而鉴定出结合能最高的前五种化合物,其范围在-6.5至-7.6千卡/摩尔之间。基于ADME和毒性特征评估了所选化合物的有效性和安全性。250纳秒的MD模拟证实了所选化合物与目标蛋白活性位点(AS)结合的稳定性。此外,MM-GBSA分析用于确定化合物与目标蛋白结合的结合自由能(ΔG bind)的高值。通过计算方法鉴定出的五种化合物,泡桐素(CID:3084131)、猕猴桃内酯(CID:11537736)、表没食子儿茶素3-O-肉桂酸酯(CID:21629801)、环水龙骨素(CID:162464)和石蒜碱(CID:73065),可用作治疗与MCPyV相关癌症的先导化合物。然而,需要进一步的湿实验室研究来评估这些药物对病毒的活性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d9b/9146542/07790ce299f3/pharmaceuticals-15-00501-g001.jpg

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