Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, Bangladesh.
Department of Pharmacology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom.
PLoS One. 2023 Jun 23;18(6):e0287179. doi: 10.1371/journal.pone.0287179. eCollection 2023.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the ongoing pandemic. The main protease (Mpro) is such an attractive drug target due to its importance in maturating several polyproteins during the replication process. In the present study, we used a classification structure-activity relationship (CSAR) model to find substructures that leads to to anti-Mpro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe Mpro inhibitors, and the random forest approach was used to build prediction models from 100 distinct data splits. The data set's modelability (MODI index) was found to be robust, with a value of 0.79 above the 0.65 threshold. The accuracy (89%), sensitivity (89%), specificity (73%), and Matthews correlation coefficient (79%) used to calculate the prediction performance, was also found to be statistically robust. An extensive analysis of the top significant descriptors unveiled the significance of methyl side chains, aromatic ring and halogen groups for Mpro inhibition. Finally, the predictive model is made publicly accessible as a web-app named Mpropred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 Mpro. Later, CMNPD, a marine compound database was screened by our app to predict bioactivity of all the compounds and results revealed significant correlation with their binding affinity to Mpro. Molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) analysis showed improved properties of the complexes. Thus, the knowledge and web-app shown herein can be used to develop more effective and specific inhibitors against the SARS-CoV-2 Mpro. The web-app can be accessed from https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py.
严重急性呼吸系统综合症冠状病毒 2（SARS-CoV-2）疫情于 2019 年爆发，目前仍需要快速临床转化的治疗方法。SARS-CoV-2 刺突糖蛋白频繁发生突变，促使人们考虑寻找另一种治疗靶点来对抗持续的大流行。由于其在复制过程中对几种多蛋白成熟的重要性，主蛋白酶（Mpro）是一个有吸引力的药物靶点。在本研究中，我们使用分类结构-活性关系（CSAR）模型在 758 种非冗余化合物中寻找导致抗 Mpro 活性的亚结构。一组 12 个指纹用于描述 Mpro 抑制剂，使用随机森林方法从 100 个不同的数据分割中构建预测模型。数据集的可建模性（MODI 指数）被发现是稳健的，其值为 0.79，高于 0.65 的阈值。用于计算预测性能的准确性（89%）、敏感性（89%）、特异性（73%）和马修斯相关系数（79%）也被发现具有统计学意义。对顶级显著描述符的广泛分析揭示了甲基侧链、芳环和卤素基团对 Mpro 抑制的重要性。最后，我们构建了一个名为 Mpropred 的网络应用程序，将预测模型公开化，以便用户可以预测化合物对 SARS-CoV-2 Mpro 的生物活性。之后，我们的应用程序对 CMNPD，一个海洋化合物数据库进行了筛选，以预测所有化合物的生物活性，结果显示与它们对 Mpro 的结合亲和力有显著相关性。分子动力学（MD）模拟和分子力学/泊松-玻尔兹曼表面面积（MM/PBSA）分析显示复合物的性质得到了改善。因此，本文中展示的知识和网络应用程序可以用于开发针对 SARS-CoV-2 Mpro 的更有效和更特异的抑制剂。该网络应用程序可通过以下链接访问：https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py。
