Protein-ligand binding affinity prediction exploiting sequence constituent homology.
Protein-ligand binding affinity prediction exploiting sequence constituent homology.
复制标题
DOI：
10.1093/bioinformatics/btad502
复制
发表时间：
2023-08-01
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
分子对接是估算结合构象及其结合亲和力的常用方法。机器学习已经被成功地应用于增强这种亲和力估计。利用这些结构中可用的一些或全部空间和分类信息，已经开发出许多不同复杂性的方法。对这些方法的评估主要是使用来自PDBind的数据集进行的。特别是具有专用测试集的评分函数比较评估(CASF)2007、2013和2016年的数据集。这项工作表明，只需要少量简单的描述符就可以有效地估计这些配合物的结合亲和力，而不需要知道配体的确切结合构象。开发的方法使用少量的配体和蛋白质描述符与梯度增强树相结合，在CASF数据集上显示了高性能。这包括常用的基准CASF2016，在该基准中，它的表现似乎比任何其他方法都要好。对于配基和蛋白质之间的空间关系未知的数据集，这种方法也很有用，就像使用大型ChEMBL衍生数据集所演示的那样。上传到https://github.com/abbiAR/PLBAffinity.的代码和数据
Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying complexity have been developed making use of some or all the spatial and categorical information available in these structures. The evaluation of such methods has mainly been carried out using datasets from PDBbind. Particularly the Comparative Assessment of Scoring Functions (CASF) 2007, 2013, and 2016 datasets with dedicated test sets. This work demonstrates that only a small number of simple descriptors is necessary to efficiently estimate binding affinity for these complexes without the need to know the exact binding conformation of a ligand. The developed approach of using a small number of ligand and protein descriptors in conjunction with gradient boosting trees demonstrates high performance on the CASF datasets. This includes the commonly used benchmark CASF2016 where it appears to perform better than any other approach. This methodology is also useful for datasets where the spatial relationship between the ligand and protein is unknown as demonstrated using a large ChEMBL-derived dataset. Code and data uploaded to https://github.com/abbiAR/PLBAffinity.
登录
查看更多内容
影响因子：
5.8
作者：
Sanchez-Cruz, Norberto;Medina-Franco, Jose L.;Barril, Xavier
通讯作者：
Barril, Xavier
影响因子：
5.6
作者：
Li, Yan;Liu, Zhihai;Wang, Renxiao
通讯作者：
Wang, Renxiao
影响因子：
46.2
作者：
Muratov EN;Bajorath J;Sheridan RP;Tetko IV;Filimonov D;Poroikov V;Oprea TI;Baskin II;Varnek A;Roitberg A;Isayev O;Curtarolo S;Fourches D;Cohen Y;Aspuru-Guzik A;Winkler DA;Agrafiotis D;Cherkasov A;Tropsha A
通讯作者：
Tropsha A
影响因子：
5.6
作者：
Liu, Jie;Wang, Renxiao
通讯作者：
Wang, Renxiao
DOI：
10.1093/bioinformatics/bty593
发表时间：
2018-09-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Öztürk H;Özgür A;Ozkirimli E
通讯作者：
Ozkirimli E