Computational Insight into Protein Tyrosine Phosphatase 1B Inhibition: A Case Study of the Combined Ligand- and Structure-Based Approach.
Xiangyu ZhangHailun JiangWei LiJian WangMao-Sheng ChengPublished in: Computational and mathematical methods in medicine (2017)
Protein tyrosine phosphatase 1B (PTP1B) is an attractive target for treating cancer, obesity, and type 2 diabetes. In our work, the way of combined ligand- and structure-based approach was applied to analyze the characteristics of PTP1B enzyme and its interaction with competitive inhibitors. Firstly, the pharmacophore model of PTP1B inhibitors was built based on the common feature of sixteen compounds. It was found that the pharmacophore model consisted of five chemical features: one aromatic ring (R) region, two hydrophobic (H) groups, and two hydrogen bond acceptors (A). To further elucidate the binding modes of these inhibitors with PTP1B active sites, four docking programs (AutoDock 4.0, AutoDock Vina 1.0, standard precision (SP) Glide 9.7, and extra precision (XP) Glide 9.7) were used. The characteristics of the active sites were then described by the conformations of the docking results. In conclusion, a combination of various pharmacophore features and the integration information of structure activity relationship (SAR) can be used to design novel potent PTP1B inhibitors.
Keyphrases
- molecular dynamics
- type diabetes
- protein protein
- molecular docking
- amino acid
- structure activity relationship
- metabolic syndrome
- molecular dynamics simulations
- binding protein
- public health
- machine learning
- cardiovascular disease
- papillary thyroid
- healthcare
- small molecule
- physical activity
- weight gain
- adipose tissue
- transcription factor
- young adults
- dna binding
- neural network