Department of Biostatistics and Medical Informatics, School of Medicine , Acibadem Mehmet Ali Aydinlar University , Istanbul 34752 , Turkey.
J Chem Inf Model. 2019 Sep 23;59(9):3846-3859. doi: 10.1021/acs.jcim.9b00346. Epub 2019 Sep 10.
Extensive usage of molecular docking for computer-aided drug discovery resulted in development of numerous programs with versatile scoring and posing algorithms. Selection of the docking program among these vast number of options is central to the outcome of drug discovery. To this end, comparative assessment studies of docking offer valuable insights into the selection of the optimal tool. Despite the availability of various docking assessment studies, the performance difference of docking programs has not been well addressed on metalloproteins which comprise a substantial portion of the human proteome and have been increasingly targeted for treatment of a wide variety of diseases. This study reports comparative assessment of seven docking programs on a diverse metalloprotein set which was compiled for this study. The refined set of the PDBbind (2017) was screened to gather 710 complexes with metal ion(s) closely located to the ligands (<4 Å). The redundancy was eliminated by clustering and overall 213 complexes were compiled as the nonredundant metalloprotein subset of the PDBbind refined. The scoring, ranking, and posing powers of seven noncommercial docking programs, namely, AutoDock4, AutoDock4, AutoDock Vina, Quick Vina 2, LeDock, PLANTS, and UCSF DOCK6, were comprehensively evaluated on this nonredundant set. Results indicated that PLANTS (80%) followed by LeDock (77%), QVina (76%), and Vina (73%) had the most accurate posing algorithms while AutoDock4 (48%) and DOCK6 (56%) were the least successful in posing. Contrary to their moderate-to-high level of posing success, none of the programs was successful in scoring or ranking of the binding affinities ( ≈ 0). Screening power was further evaluated by using active-decoy ligand sets for a large compilation of metalloprotein targets. PLANTS stood out among other programs to be able to enrich the active ligand for every target, underscoring its robustness for screening of metalloprotein inhibitors. This study provides useful information for drug discovery studies targeting metalloproteins.
广泛应用分子对接进行计算机辅助药物发现，促成了众多具有多功能评分和构象算法的程序的发展。在如此众多的选择中选择对接程序是药物发现结果的关键。为此，对接评估研究的比较为选择最佳工具提供了有价值的见解。尽管有各种对接评估研究，但对接程序的性能差异在构成人类蛋白质组大部分的金属蛋白上尚未得到很好的解决，并且这些金属蛋白已越来越多地成为治疗各种疾病的靶点。本研究报告了对七种对接程序在为该研究专门编制的多样化金属蛋白集上的比较评估。筛选了经过改进的 PDBbind（2017）数据集，以收集 710 个复合物，其中金属离子（s）紧邻配体（<4 Å）。通过聚类消除了冗余，总共编译了 213 个复合物作为 PDBbind 改进后的非冗余金属蛋白子集。对七种非商业对接程序，即 AutoDock4、AutoDock Vina、Quick Vina 2、LeDock、PLANTS、UCSF DOCK6 的评分、排名和构象能力在这个非冗余集上进行了全面评估。结果表明，PLANTS（80%）其次是 LeDock（77%）、QVina（76%）和 Vina（73%）具有最准确的构象算法，而 AutoDock4（48%）和 DOCK6（56%）在构象方面最不成功。与它们中等至高的构象成功率相反，没有一个程序在评分或结合亲和力（≈0）排名方面取得成功。通过使用大型金属蛋白靶标活性-诱饵配体集进一步评估筛选能力。PLANTS 脱颖而出，能够为每个靶标富集活性配体，突出了其筛选金属蛋白抑制剂的稳健性。本研究为针对金属蛋白的药物发现研究提供了有用的信息。
