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. 2016 Jan 25:6:19312.
doi: 10.1038/srep19312.

Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products

Yunan Sun  1 , Hui Zhou  1 , Hongmei Zhu  1 , Siu-wai Leung  1   2
Affiliations

Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products

Yunan Sun et al. Sci Rep. .

Abstract

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase, and its dysregulation can lead to ageing, diabetes, and cancer. From 346 experimentally confirmed SIRT1 inhibitors, an inhibitor structure pattern was generated by inductive logic programming (ILP) with DMax Chemistry Assistant software. The pattern contained amide, amine, and hetero-aromatic five-membered rings, each of which had a hetero-atom and an unsubstituted atom at a distance of 2. According to this pattern, a ligand-based virtual screening of 1 444 880 active compounds from Chinese herbs identified 12 compounds as inhibitors of SIRT1. Three compounds (ZINC08790006, ZINC08792229, and ZINC08792355) had high affinity (-7.3, -7.8, and -8.6 kcal/mol, respectively) for SIRT1 as estimated by molecular docking software AutoDock Vina. This study demonstrated a use of ILP and background knowledge in machine learning to facilitate virtual screening.

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Figures

Figure 1
Figure 1. A reaction catalysed by SIRT1.
Figure 2
Figure 2. Flow diagram of the literature search and study selection.
Figure 3
Figure 3. Reference structures and performance of the inhibitor model.
(a) Reference structures of inhibitors with high IC50 values. (b) Reference structures of inhibitors with low IC50 values. (c) Predicted–actual scatter diagram of the inhibitor model. (d) Cumulative response curve of the inhibitor model, showing the percentage of hits (y-axis) within the first n percent of data (x-axis). (e) Lift curve of the inhibitor model, showing observation from the first top n percent of data about how many times the model outperformed a random model (y-axis). (f) ROC curve of the inhibitor model, showing the percentage of non-hits (x-axis: false alarms) to obtain a particular percentage of hits.
Figure 4
Figure 4. Reference structures of the inhibitor binding model.
(a) Reference structures of inhibitors with high binding energy. (b) Reference structures of inhibitors with low binding energy.
Figure 5
Figure 5. Structures (source, binding energy) of the 12 potential inhibitors identified by virtual screening.
TCMT: Traditional Chinese Medicines@Taiwan; TCMID: Traditional Chinese Medicine Integrated Database.
Figure 6
Figure 6. SIRT1 active pocket.
See this image and copyright information in PMC

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