pyCaverDock: Python implementation of the popular tool for analysis of ligand transport with advanced caching and batch calculation support.

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Bioinformatics Pub Date : 2023-08-01 DOI:10.1093/bioinformatics/btad443

Ondrej Vavra, Jakub Beranek, Jan Stourac, Martin Surkovsky, Jiri Filipovic, Jiri Damborsky, Jan Martinovic, David Bednar

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引用次数: 0

Abstract

Summary: Access pathways in enzymes are crucial for the passage of substrates and products of catalysed reactions. The process can be studied by computational means with variable degrees of precision. Our in-house approximative method CaverDock provides a fast and easy way to set up and run ligand binding and unbinding calculations through protein tunnels and channels. Here we introduce pyCaverDock, a Python3 API designed to improve user experience with the tool and further facilitate the ligand transport analyses. The API enables users to simplify the steps needed to use CaverDock, from automatizing setup processes to designing screening pipelines.

Availability and implementation: pyCaverDock API is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://loschmidt.chemi.muni.cz/caverdock/.

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pyCaverDock: Python实现的流行工具，用于分析配体传输，具有高级缓存和批处理计算支持。

摘要:酶的通路对催化反应的底物和产物的传递至关重要。这个过程可以用不同精度的计算方法来研究。我们的内部近似方法CaverDock提供了一种快速简便的方法，通过蛋白质通道和通道建立和运行配体结合和解结合计算。在这里，我们介绍pyCaverDock，这是一个Python3 API，旨在改善用户使用该工具的体验，并进一步促进配体传输分析。该API使用户能够简化使用CaverDock所需的步骤，从自动化设置过程到设计筛选管道。可用性和实现:pyCaverDock API是在Python 3中实现的，可以在https://loschmidt.chemi.muni.cz/caverdock/上免费获得详细的文档和实际示例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.