Computational investigation of TGF-β receptor inhibitors for treatment of idiopathic pulmonary fibrosis: Field-based QSAR model and molecular dynamics simulation. (October 2018)

• Record Type: Journal Article
• Authors: Li, Wei; Liu, Xue; Muhammad, Suleiman; Shi, JiYue; Meng, YanQiu; Wang, Jian
• Is Part Of: Computational biology and chemistry. Volume 76(2018)
• Page Start: 139
• Page End: 150
• Publication Date: 2018-10
• Subjects: Idiopathic pulmonary fibrosis -- Molecular dynamics -- TGF-β receptor -- Structure–activity relationships; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.07.002
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Molecular dynamics simulation for representative ALK5-ligand complexes was carried out to reveal their stability. The field-based QSAR model (Q 2 = 0.548, R 2 training = 0.840, R 2 test = 0.750) owns good predictive capabilities. Interaction analysis of ALK5-ligand complexes could provide valuable information about the key residues. Abstract: The discovery of drugs relevant to transforming growth factor β (TGF-β) receptor inhibitors have been considered as a considerable challenge during therapy idiopathic pulmonary fibrosis diseases. For the first time, herein we illustrate a field-based quantitative structure-activity relationship (QSAR) model and molecular dynamics (MD) simulations for novel 7-substituted-pyrazolo [4, 3-b] pyridine derivatives with biological activity for the TGF-β receptor, with an attempt of elucidating the 3D structural features that are essential for the activity. Results demonstrate that the field-based model (Q 2 = 0.548, R 2 training = 0.840, R 2 test = 0.750) are acceptable with good predictive capabilities. In addition, MD studies were also carried out on the training set with the aim of exploring their binding modes in the active pocket of TGF-β receptor, resulting in some of the crucial structural fragments which are responsible for inhibitory activity. Therefore, we summarized the following features required for TGF-β receptor inhibition: electronegative in region1, bulky groups in region2 and smaller groups in region3. Based on the model and related information, we hope the above information provides an important insight for understanding the interactions of the inhibitors and TGF-β receptor, which may be useful in discovering novel potent inhibitors.