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对接小肽仍然是一个巨大的挑战:使用AutoDock Vina进行的评估。

Docking small peptides remains a great challenge: an assessment using AutoDock Vina.

作者信息

Rentzsch Robert, Renard Bernhard Y

出版信息

Brief Bioinform. 2015 Nov;16(6):1045-56. doi: 10.1093/bib/bbv008. Epub 2015 Apr 21.

Abstract

There is a growing interest in the mechanisms and the prediction of how flexible peptides bind proteins, often in a highly selective and conserved manner. While both existing small-molecule docking methods and custom protocols can be used, even short peptides make difficult targets owing to their high torsional flexibility. Any benchmarking should therefore start with those. We compiled a meta-data set of 47 complexes with peptides up to five residues, based on 11 related studies from the past decade. Although their highly varying strategies and constraints preclude direct, quantitative comparisons, we still provide a comprehensive overview of the reported results, using a simple yet stringent measure: the quality of the top-scoring peptide pose. Using the entire data set, this is augmented by our own benchmark of AutoDock Vina, a freely available, fast and widely used docking tool. It particularly addresses non-expert users and was therefore implemented in a highly integrated manner. Guidelines addressing important issues such as the amount of sampling required for result reproducibility are so far lacking. Using peptide docking as an example, this is the first study to address these issues in detail. Finally, to encourage further, standardized benchmarking efforts, the compiled data set is made available in an accessible, transparent and extendable manner.

摘要

人们对柔性肽如何结合蛋白质的机制及预测越来越感兴趣,肽与蛋白质的结合通常具有高度的选择性和保守性。虽然现有的小分子对接方法和定制方案都可以使用,但即使是短肽,由于其高度的扭转灵活性,也成为难以处理的目标。因此,任何基准测试都应从这些方面入手。我们根据过去十年的11项相关研究,汇编了一个包含47个肽长度达五个残基的复合物的元数据集。尽管它们的策略和限制差异很大,无法进行直接的定量比较,但我们仍然使用一种简单而严格的衡量标准:得分最高的肽构象的质量,对已报道的结果进行了全面概述。使用整个数据集,我们还通过自己对AutoDock Vina(一种免费、快速且广泛使用的对接工具)的基准测试进行了补充。它特别针对非专业用户,因此以高度集成的方式实现。目前还缺乏解决诸如结果可重复性所需的采样量等重要问题的指南。以肽对接为例,这是第一项详细解决这些问题的研究。最后,为鼓励进一步的标准化基准测试工作,汇编的数据集以可访问、透明和可扩展的方式提供。

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