title: Identifying HER2 inhibitors from natural products database.
abstract: The relationship between abnormal HER2 expression and cancer is important in cancer therapeutics. Formation and spread of cancer cells may be restricted by inhibiting HER2. We conducted ligand-based and structure-based studies to assess the potency of natural compounds as potential HER2 inhibitors. Multiple linear regression (MLR) and support vector machine (SVM) models were constructed to predict biological activities of natural compounds, and molecular dynamics (MD) was used to assess their stability with HER2 under a dynamic environment. Predicted bioactivities of the natural compounds ranged from 6.014-9.077 using MLR (r(2) = 0.7954) and 5.122-6.950 using SVM (r(2) = 0.8620). Both models were in agreement and suggest bioactivity based on candidate structure. Conformation changes caused by MD favored the formation of stabilizing H-bonds. All candidates had higher stability than Lapinatib, which may be due to the number and spatial distribution of additional H-bonds and hydrophobic interactions. Amino acids Lys724 and Lys736 are critical for binding in HER2, and Thr798, Cys805, and Asp808 are also important for increased stability. Candidates may block the entrance to the ATP binding site located within the inner regions and prevent downstream activation of HER2. Our multidirectional approach indicates that the natural compounds have good ligand efficacy in addition to stable binding affinities to HER2, and should be potent candidates of HER2 inhibitors. With regard to drug design, designing HER2 inhibitors with carboxyl or carbonyl groups available for H-bond formation with Lys724 and Lys736, and benzene groups for hydrophobic contact with Cys805 may improve protein-ligand stability.
authors.affiliation: Laboratory of Computational and Systems Biology, China Medical University, Taichung, Taiwan.
authors.forename: Shun-Chieh
authors.initials: SC
authors.lastname: Yang
authors.forename: Su-Sen
authors.initials: SS
authors.lastname: Chang
authors.forename: Calvin Yu-Chian
authors.initials: CY
authors.lastname: Chen
mesh_terms: D000596:Amino Acids / Q000378:metabolism; D001688:Biological Products / Q000032:analysis* / Q000494:pharmacology*; D019992:Databases as Topic; D006801:Humans; D006860:Hydrogen Bonding / Q000187:drug effects; D008024:Ligands; D016014:Linear Models; D056004:Molecular Dynamics Simulation; D047428:Protein Kinase Inhibitors / Q000032:analysis* / Q000737:chemistry / Q000494:pharmacology*; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000037:antagonists & inhibitors* / Q000378:metabolism; D060388:Support Vector Machine; D013816:Thermodynamics
chemical_list: D000596:Amino Acids; D001688:Biological Products; D008024:Ligands; D047428:Protein Kinase Inhibitors; D018719:Erb-b2 Receptor Tyrosine Kinases