Guariero Fernando Loza, Macedo Eduardo Pantoja de, Laia Elise Bittencourt de, Evaristo Joseph Albert Medeiros, Evaristo Geisa Paulino Caprini, Zanchi Fernando Berton
Laboratório de Bioinformática e Química Medicinal, Fundação Oswaldo Cruz Rondônia (LABIOQUIM-Fiocruz-RO), Porto Velho, Rondônia, Brazil.
Departamento de Química, Universidade Federal de Rondônia (UNIR), Porto Velho, Rondônia, Brazil.
J Comput Chem. 2025 Sep 5;46(23):e70225. doi: 10.1002/jcc.70225.
Virtual screening through molecular docking represents a fundamental computational methodology extensively employed in the identification of therapeutic compounds for malaria and other parasitic diseases. Although numerous software platforms are available, including AutodockGPU, the command-line interface requirements present significant barriers to non-specialized users, while multi-target screening protocols introduce additional complexity in receptor preparation procedures. To address these limitations, we developed Plasmodocking, a comprehensive web-based platform designed to automate molecular docking simulations against predefined Plasmodium falciparum targets (https://plasmodocking-unir.ecotechamazonia.com.br/). The platform enables users to submit up to 10 molecular structures (.sdf format) for automated AutodockGPU screening against 38 pre-configured parasite targets, facilitating systematic comparison of binding energies with co-crystallized ligands. Developed using Python and Next.js, Plasmodocking accelerates malaria drug discovery by enabling simultaneous multi-target docking simulations within a single experimental framework. The open-source codebase is available at: https://github.com/LABIOQUIM/PlasmoDocking-Client.
通过分子对接进行虚拟筛选是一种基本的计算方法,在识别疟疾和其他寄生虫病的治疗化合物方面得到广泛应用。尽管有许多软件平台可供使用,包括AutodockGPU,但命令行界面要求对非专业用户构成了重大障碍,而多靶点筛选协议在受体准备程序中引入了额外的复杂性。为了解决这些限制,我们开发了Plasmodocking,这是一个基于网络的综合平台,旨在针对预定义的恶性疟原虫靶点自动进行分子对接模拟(https://plasmodocking-unir.ecotechamazonia.com.br/)。该平台允许用户提交多达10个分子结构(.sdf格式),以便针对38个预配置的寄生虫靶点进行自动AutodockGPU筛选,便于与共结晶配体进行结合能的系统比较。Plasmodocking使用Python和Next.js开发,通过在单个实验框架内实现同步多靶点对接模拟,加速了疟疾药物的发现。开源代码库可在以下网址获取:https://github.com/LABIOQUIM/PlasmoDocking-Client。