Evteev Sergei A, Ereshchenko Alexey V, Ivanenkov Yan A
The Federal State Unitary Enterprise Dukhov Automatics Research Institute, Moscow 127055, Russia.
J Chem Inf Model. 2023 Feb 27;63(4):1124-1132. doi: 10.1021/acs.jcim.2c01413. Epub 2023 Feb 6.
Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning algorithms, the existing solutions do not provide significant advantages over nonmachine learning approaches and there is still a big room for improvement. The low ability to identify protein-ligand-binding sites makes available approaches inapplicable to automated drug design. Here, we present SiteRadar, a new algorithm for mapping cavities that are likely to bind a small-molecule ligand. SiteRadar shows higher accuracy in binding site identification compared with FPocket and PUResNet. SiteRadar demonstrates an ability to detect up to 74% of true ligand-binding sites according to the top N + 2 metric and usually covers approximately 80% of ligand atoms. Therefore, SiteRadar can be regarded as a promising solution for implementation into algorithms for automated drug design.
识别蛋白质表面的配体结合位点是基于结构的药物设计中的关键步骤。尽管已经提出了多种技术，包括那些使用机器学习算法的技术，但现有解决方案相对于非机器学习方法并没有显著优势，仍有很大的改进空间。识别蛋白质-配体结合位点的能力较低使得现有方法不适用于自动化药物设计。在此，我们提出了SiteRadar，一种用于绘制可能结合小分子配体的腔的新算法。与FPocket和PUResNet相比，SiteRadar在结合位点识别方面显示出更高的准确性。根据前N + 2指标，SiteRadar能够检测到高达74%的真实配体结合位点，并且通常覆盖大约80%的配体原子。因此，SiteRadar可被视为一种有前途的解决方案，可用于自动化药物设计算法中。
