Maia Eduardo H B, Medaglia Lucas Rolim, da Silva Alisson Marques, Taranto Alex G
Laboratório de Quêmica Farmaĉutica Medicinal, Universidade Federal de São João Del-Rei, Divinópolis 35501-296, Minas Gerais, Brazil.
Centro Federal de Educação Tecnológica de Minas Gerais, CEFET-MG, Campus Divinópolis, Divinópolis 35503-822, MG, Brazil.
ACS Omega. 2020 Mar 20;5(12):6628-6640. doi: 10.1021/acsomega.9b04403. eCollection 2020 Mar 31.
Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.
计算机辅助药物设计(CADD)方法对新药研发做出了巨大贡献。在CADD方法中,虚拟筛选(VS)可以用具有成为药物所需的理想物理化学和药效团特征的分子来丰富化合物库。为此有许多免费工具可用,但它们难以使用且没有图形用户界面。此外,必须使用几个免费工具来完成整个VS过程,这要求用户处理一个软件程序的结果以便能在另一个程序中使用,增加了人为错误的潜在来源。而且,一些软件程序需要高级计算技能的知识,如编程语言。这种情况促使我们开发了分子架构师(MolAr)。MolAr是一个具有简单直观界面的工作流程,以集成和自动化的形式运行,以执行从蛋白质制备(同源建模和质子化状态)到虚拟筛选的整个VS过程。MolAr通过AutoDock Vina、DOCK 6或两者的共识来进行VS。进行了两个案例研究以证明MolAr的性能。在第一个研究中,评估了使用MolAr用于DNA-配体系统的可行性。AutoDock Vina和DOCK 6在DNA-配体系统中进行VS时都显示出良好的结果。然而,使用共识虚拟筛选能够丰富结果。根据ROC曲线下面积和富集因子,共识VS能更好地预测活性配体的位置。第二个案例研究是针对DUD-E数据库中的8个靶点以及每个靶点的10个活性配体进行的。结果表明,将AutoDock Vina提供的最终配体构象作为DOCK 6的输入,在VS中可使DOCK 6的ROC曲线提高多达42%。这些案例研究表明MolAr能够进行VS过程,是一个易于使用且有效的工具。MolAr可在http://www.drugdiscovery.com.br/software/免费下载。