Motivation

Protein–peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs.

Results

Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein–peptide complex. We show that it outperforms leading peptide docking methods on two protein–peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein–peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein–protein interactions and interactions with disordered proteins.

Availability and implementation

ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP.

Supplementary information

Supplementary dataSupplementary data are available at Bioinformatics online.

中文翻译：

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AutoDock CrankPep：结合折叠和对接来预测蛋白质-肽复合物

动机

蛋白质-肽相互作用介导了多种细胞和生物学功能。在过去的几年中，预测这些相互作用的方法引起了很多兴趣，正如目前临床试验中基于肽的治疗性分子的数量迅速增长所证明的那样。对于现有的自动对接软件程序，肽的大小和灵活性已显示出挑战。

结果

在这里，我们简要介绍一下AutoDock CrankPep或ADCP，这是一种将柔性肽固定到刚性受体中的新方法。ADCP在蛋白质产生的电位场中折叠肽，以预测蛋白质-肽复合物。我们在两个通常用于基准对接方法的蛋白质-肽数据集上显示出优于领先的肽对接方法：LEADS-PEP和peptiDB，它由长度最多为15个氨基酸的肽组成。除了这些数据集之外，ADCP还可靠地对接了一组蛋白质-肽复合物，其中包含长度范围为16至20个氨基酸的肽。ADCP在这些较长肽段上的强大性能可以对肽介导的蛋白-蛋白相互作用以及与无序蛋白的相互作用进行精确建模。

可用性和实施

ADCP根据LGPL 2.0开源许可证进行分发，可从http://adcp.scripps.edu获得。源代码位于https://github.com/ccsb-scripps/ADCP。

补充资料

补充数据补充数据可从Bioinformatics在线获得。