Greta Bellinzona, Davide Sassera, Alexandre M J J Bonvin
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引用次数: 0
摘要
动机在整个蛋白质组中发现新的蛋白质-蛋白质相互作用(PPIs)为了解新的蛋白质功能和阐明生物体内或生物体之间的系统特性提供了巨大的潜力。虽然计算结构生物学(尤其是 AlphaFold-Multimer)的最新进展促进了这项任务的完成,但大规模筛选的扩展仍是一项挑战,需要大量的计算资源:我们评估了将 AlphaFold-Multimer 生成的模型数量从五个减少到一个对该方法区分真假 PPI 的能力的影响。我们的评估是在一个包含种内和种间 PPI 的数据集上进行的,其中包括来自细菌和真核生物的蛋白质。我们证明,减少采样并不会影响该方法的准确性,从而为 PPI 预测提供了一种更快、更高效、更环保的解决方案:本文使用的代码可从 https://github.com/MIDIfactory/AlphaFastPPi 网站获取。请注意,使用 https://github.com/KosinskiLab/AlphaPulldown 上最新版本的 AlphaPulldown 也能实现同样的效果。
Accelerating protein-protein interaction screens with reduced AlphaFold-Multimer sampling.
Motivation: Discovering new protein-protein interactions (PPIs) across entire proteomes offers vast potential for understanding novel protein functions and elucidate system properties within or between an organism. While recent advances in computational structural biology, particularly AlphaFold-Multimer, have facilitated this task, scaling for large-scale screenings remains a challenge, requiring significant computational resources.
Results: We evaluated the impact of reducing the number of models generated by AlphaFold-Multimer from five to one on the method's ability to distinguish true PPIs from false ones. Our evaluation was conducted on a dataset containing both intra- and inter-species PPIs, which included proteins from bacterial and eukaryotic sources. We demonstrate that reducing the sampling does not compromise the accuracy of the method, offering a faster, efficient, and environmentally friendly solution for PPI predictions.
Availability and implementation: The code used in this article is available at https://github.com/MIDIfactory/AlphaFastPPi. Note that the same can be achieved using the latest version of AlphaPulldown available at https://github.com/KosinskiLab/AlphaPulldown.