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

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Protocol publication

[…] 2D chemical structures of the ligands reported in eligible studies were re-sketched with ChemSketch software. A compound search for chemical information from PubChem was performed with the search function of ChemSketch. Canonical simplified molecular input line entry specification (SMILES), ID code, and all bioactivity information of the compounds were compiled in datasets and saved in sdf format (MDL MOL format) by OpenBabel 2.3.2 software with the settings of “add hydrogen to polar atoms only”, “canonicalize the atom order”, “generate 2D coordinates”, and “use wedge and hash bonds from input”. The chirality of the ligands was disregarded in the present study. Duplicate records were removed so that each record was unique. SIRT1 activators were indicated by IDs with prefix “SA” and SIRT1 inhibitors were indicated by IDs with prefix “SI”. The files containing the compound information in appropriate format were used in subsequent QSAR modelling. [...] To investigate the intermolecular interactions between ligands and SIRT1, we performed semi-flexible docking using AutoDock Vina software. 3D structural information for SIRT1 protein was obtained from the Protein Data Bank (PDB; ID: 4I5I). The information for the catalytic domain of SIRT1 () and NAD+ protein-binding site was used for molecular docking as previously reported. Hydrogen atoms were added to prepare the receptor file, for which the 3D structure was saved in pdbqt format by AutoDock Tools. The ligand file was prepared in the same manner. The receptor and ligand files were then applied to docking in the AutoDock Vina software, which also estimated the binding energy and affinity. […]

Pipeline specifications

Software tools ChemSketch, Open Babel, AutoDock Vina
Applications Drug design, Protein interaction analysis
Diseases Diabetes Mellitus, Neoplasms
Chemicals Amines, NAD