论文标题
新型算法和高性能云计算使有效的完全量子机械蛋白质配体评分
Novel algorithms and high-performance cloud computing enable efficient fully quantum mechanical protein-ligand scoring
论文作者
论文摘要
通过基于物理的计算对小分子与蛋白质受体的结合进行排名仍然具有挑战性。尽管已经使用自由能法进行了侵害，但是当基础经典的机械力场不足时，这些方法失败了。原则上，量子机械密度功能理论（DFT）评分提供了一种更准确的方法，但是即使有近似值，这尚未在与药物发现相关的时间表和资源上实用。在这里，我们描述了如何使用算法进行DFT计算来克服这种障碍，以扩展可用的云体系结构，从而使无近似值的全密度功能理论适用于大约2500个原子的蛋白质配体复合物，以数十分钟的时间。将其应用于与Mcl1结合的22种配体的现实示例，这表明该系统的密度功能得分优于经典的自由能扰动理论。这增加了将全量子机械评分广泛应用于现实世界中的药物发现管道的可能性。
Ranking the binding of small molecules to protein receptors through physics-based computation remains challenging. Though inroads have been made using free energy methods, these fail when the underlying classical mechanical force fields are insufficient. In principle, a more accurate approach is provided by quantum mechanical density functional theory (DFT) scoring, but even with approximations, this has yet to become practical on drug discovery-relevant timescales and resources. Here, we describe how to overcome this barrier using algorithms for DFT calculations that scale on widely available cloud architectures, enabling full density functional theory, without approximations, to be applied to protein-ligand complexes with approximately 2500 atoms in tens of minutes. Applying this to a realistic example of 22 ligands binding to MCL1 reveals that density functional scoring outperforms classical free energy perturbation theory for this system. This raises the possibility of broadly applying fully quantum mechanical scoring to real-world drug discovery pipelines.
