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疟原虫对接:一种用于靶向恶性疟原虫酶的虚拟筛选的用户友好型开源网络工具。

PlasmoDocking: A User-Friendly Open-Source Web Tool for Virtual Screening Targeting Plasmodium falciparum Enzymes.

作者信息

Guariero Fernando Loza, Macedo Eduardo Pantoja de, Laia Elise Bittencourt de, Evaristo Joseph Albert Medeiros, Evaristo Geisa Paulino Caprini, Zanchi Fernando Berton

机构信息

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.

出版信息

J Comput Chem. 2025 Sep 5;46(23):e70225. doi: 10.1002/jcc.70225.

Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bb1/12410038/c75c286fa9e0/JCC-46-0-g003.jpg

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