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An in silico protocol for identifying mTOR inhibitors from natural products

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Abstract

The mammalian target of rapamycin (mTOR) is an anti-cancer target. In this study, we propose an in silico protocol for identifying mTOR inhibitors from the ZINC natural product database. First, a three-dimensional quantitative structure–activity relationship pharmacophore model was built based on known mTOR inhibitors. The model was validated with an external test set, Fischer’s randomization method, a decoy set and pharmacophore mapping conformation testing. The results showed that the model can predict the mTOR inhibition activity of the tested compounds. Virtual screening was performed based on the best pharmacophore model, and the results were then filtered using a molecular docking approach. In addition, molecular mechanics/generalized born surface area analysis was used to refine the selected candidates. The top 20 natural products were selected as potential mTOR inhibitors, and their structural scaffolds could serve as building blocks in designing drug-like molecules for mTOR inhibition.

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Acknowledgments

This work was partly supported by a Grant from the National High Technology Research and Development Program of China (863 Program; No. 2012AA020307), Guangdong Recruitment Program of Creative Research Groups, the National Natural Science Foundation of China (Nos. 81001372, 81173470), and the Special Funding Program for the National Supercomputer Center in Guangzhou (2012Y2-00048/2013Y2-00045, 201200000037).

Conflict of interest

The authors declare no competing financial interest.

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Authors and Affiliations

  1. Research Center for Drug Discovery & Institute of Human Virology, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, China

    Lei Chen, Ling Wang, Qiong Gu & Jun Xu

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  1. Lei Chen
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  2. Ling Wang
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  4. Jun Xu
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Correspondence to Qiong Gu or Jun Xu.

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Chen, L., Wang, L., Gu, Q. et al. An in silico protocol for identifying mTOR inhibitors from natural products. Mol Divers 18, 841–852 (2014). https://doi.org/10.1007/s11030-014-9543-5

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