Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term

Zheng, Liangzhen2,3; Meng, Jintao2,4; Jiang, Kai5; Lan, Haidong7; Wang, Zechen8; Lin, Mingzhi3; Li, Weifeng8; Guo, Hongwei5,6; Wei, Yanjie2; Mu, Yuguang1

2022-03-01

10.1093/bib/bbac051

BRIEFINGS IN BIOINFORMATICS   影响因子和分区

1467-5463

1477-4054

Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein-ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future.

scoring function machine learning molecular docking reversal virtual screening virtual screening

SCI

英语

其他

Singapore Ministry of Education (MOE)[MOE-T2EP30120-0007] ; Singapore MOE[RG27/21] ; National Key Research and Development Program of China[2018YFB0204403] ; Strategic Priority CAS Project[XDB38050100] ; Key Research and Development Project of Guangdong Province[2021B0101310002] ; Shenzhen Basic Research Fund[RCYX2020071411473419] ; Shenzhen KQTD Project["KQTD20200820113106007","JSGG20201102163800001"] ; CAS Key Lab[2011DP173015] ; Youth Innovation Promotion Association, CAS[Y2021101] ; Natural Science Foundation of Shandong Province[ZR2020JQ04] ; National Natural Science Foundation of China[11874238]

Biochemistry & Molecular Biology ; Mathematical & Computational Biology

Biochemical Research Methods ; Mathematical & Computational Biology

WOS:000769083500001

OXFORD UNIV PRESS

COMPUTER SCIENCE

Web of Science

1.Nanyang Technol Univ, Sch Biol Sci, 60 Nanyang Dr, Singapore 637551, Singapore
2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
3.Shanghai Zelixir Biotech Co Ltd, Shenzhen, Peoples R China
4.Natl Supercomp Ctr Shenzhen, Shenzhen, Peoples R China
5.Southern Univ Sci & Technol SUSTech, Dept Biol, Shenzhen, Peoples R China
6.Southern Univ Sci & Technol SUSTech, Inst Plant & Food Sci, Shenzhen, Peoples R China
7.Tencent AI Lab, Shenzhen, Peoples R China
8.Shandong Univ, Sch Phys, Jinan, Shandong, Peoples R China

Zheng, Liangzhen,Meng, Jintao,Jiang, Kai,et al. Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term[J]. BRIEFINGS IN BIOINFORMATICS,2022.

Zheng, Liangzhen.,Meng, Jintao.,Jiang, Kai.,Lan, Haidong.,Wang, Zechen.,...&Mu, Yuguang.(2022).Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term.BRIEFINGS IN BIOINFORMATICS.

Zheng, Liangzhen,et al."Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term".BRIEFINGS IN BIOINFORMATICS (2022).