An integrated approach with new strategies for QSAR models and lead optimization

BMC Genomics

Research

Li-Jen Chang¹  Hui-Hui Hsu¹  Yen-Chao Hsu¹  Jinn-Moon Yang² 

[1] Institute of Bioinformatics and Systems Biology, National Chiao Tung University, 300, Hsinchu, Taiwan;Institute of Bioinformatics and Systems Biology, National Chiao Tung University, 300, Hsinchu, Taiwan;Department of Biological Science and Technology, National Chiao Tung University, 300, Hsinchu, Taiwan;

关键词: QSAR model;    Computational drug design;    Molecular docking;   

DOI  :  10.1186/s12864-017-3503-2

来源: Springer

【 摘 要 】

BackgroundComputational drug design approaches are important for shortening the time and reducing the cost for drug discovery and development. Among these methods, molecular docking and quantitative structure activity relationship (QSAR) play key roles for lead discovery and optimization. Here, we propose an integrated approach with core strategies to identify the protein-ligand hot spots for QSAR models and lead optimization. These core strategies are: 1) to generate both residue-based and atom-based interactions as the features; 2) to identify compound common and specific skeletons; and 3) to infer consensus features for QSAR models.ResultsWe evaluated our methods and new strategies on building QSAR models of human acetylcholinesterase (huAChE). The leave-one-out cross validation values q2 and r2 of our huAChE QSAR model are 0.82 and 0.78, respectively. The experimental results show that the selected features (resides/atoms) are important for enzymatic functions and stabling the protein structure by forming key interactions (e.g., stack forces and hydrogen bonds) between huAChE and its inhibitors. Finally, we applied our methods to arthrobacter globiformis histamine oxidase (AGHO) which is correlated to heart failure and diabetic.ConclusionsBased on our AGHO QSAR model, we identified a new substrate verified by bioassay experiments for AGHO. These results show that our methods and new strategies can yield stable and high accuracy QSAR models. We believe that our methods and strategies are useful for discovering new leads and guiding lead optimization in drug discovery.

【 授权许可】

CC BY   
© The Author(s). 2017

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