Dataset Information

Design and Screening of New Lead Compounds for Autism Based on QSAR Model and Molecular Docking Studies.

ABSTRACT: The purpose of the present study aims to develop a satisfactory model for predicting pro-social and pro-cognitive effects on azinesulfonamides of cyclic amine derivatives as potential antipsychotics. The three dimensional-quantitative structure affinity relationship (3D-QSAR) study was performed on a series of azinesulfonamides of cyclic amine derivative using comparative molecular similarity indices analysis (CoMSIA). The best statistical model of CoMSIA q2, r2, SEE and F values are 0.664, 0.973, 0.087, and 82.344, respectively. Based on the model contour maps and the highest activity structure of the 43rd compound, serial new structures were designed and the 43k1 compound was selected as the best structure. The dock results showed a good binding of 43k1 with the protein (PDB ID: 6A93). The QSAR model analysis of the contour maps can help us to provide guidelines for finding novel potential antipsychotics.

SUBMITTER: Chen Y

PROVIDER: S-EPMC9657114 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Design and Screening of New Lead Compounds for Autism Based on QSAR Model and Molecular Docking Studies.

Chen Yongjian Y Ma Kang K Xu Peilong P Si Hongzong H Duan Yunbo Y Zhai Honglin H

Molecules (Basel, Switzerland) 20221026 21

The purpose of the present study aims to develop a satisfactory model for predicting pro-social and pro-cognitive effects on azinesulfonamides of cyclic amine derivatives as potential antipsychotics. The three dimensional-quantitative structure affinity relationship (3D-QSAR) study was performed on a series of azinesulfonamides of cyclic amine derivative using comparative molecular similarity indices analysis (CoMSIA). The best statistical model of CoMSIA q2, r2, SEE and F values are 0.664, 0.97 ...[more]

PMID: 36364109

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Project description:Treatment of several autoimmune diseases and types of cancer has been an intense area of research over the past two decades. Many signaling pathways that regulate innate and/or adaptive immunity, as well as those that induce overexpression or mutation of protein kinases, have been targeted for drug discovery. One of the serine/threonine kinases, Interleukin-1 Receptor Associated Kinase 4 (IRAK4) regulates signaling through various Toll-like receptors (TLRs) and interleukin-1 receptor (IL1R). It controls diverse cellular processes including inflammation, apoptosis, and cellular differentiation. MyD88 gain-of-function mutations or overexpression of IRAK4 has been implicated in various types of malignancies such as Waldenström macroglobulinemia, B cell lymphoma, colorectal cancer, pancreatic ductal adenocarcinoma, breast cancer, etc. Moreover, over activation of IRAK4 is also associated with several autoimmune diseases. The significant role of IRAK4 makes it an interesting target for the discovery and development of potent small molecule inhibitors. A few potent IRAK4 inhibitors such as PF-06650833, RA9 and BAY1834845 have recently entered phase I/II clinical trial studies. Nevertheless, there is still a need of selective inhibitors for the treatment of cancer and various autoimmune diseases. A great need for the same intrigued us to perform molecular modeling studies on 4,6-diaminonicotinamide derivatives as IRAK4 inhibitors. We performed molecular docking and dynamics simulation of 50 ns for one of the most active compounds of the dataset. We also carried out MM-PBSA binding free energy calculation to identify the active site residues, interactions of which are contributing to the total binding energy. The final 50 ns conformation of the most active compound was selected to perform dataset alignment in a 3D-QSAR study. Generated RF-CoMFA (q2 = 0.751, ONC = 4, r2 = 0.911) model revealed reasonable statistical results. Overall results of molecular dynamics simulation, MM-PBSA binding free energy calculation and RF-CoMFA model revealed important active site residues of IRAK4 and necessary structural properties of ligand to design more potent IRAK4 inhibitors. We designed few IRAK4 inhibitors based on these results, which possessed higher activity (predicted pIC50) than the most active compounds of the dataset selected for this study. Moreover, ADMET properties of these inhibitors revealed promising results and need to be validated using experimental studies.

Project description:Tyrosine threonine kinase (TTK) is the key component of the spindle assembly checkpoint (SAC) that ensures correct attachment of chromosomes to the mitotic spindle and thereby their precise segregation into daughter cells by phosphorylating specific substrate proteins. The overexpression of TTK has been associated with various human malignancies, including breast, colorectal and thyroid carcinomas. TTK has been validated as a target for drug development, and several TTK inhibitors have been discovered. In this study, ligand and structure-based alignment as well as various partial charge models were used to perform 3D-QSAR modelling on 1H-Pyrrolo[3,2-c] pyridine core containing reported inhibitors of TTK protein using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches to design better active compounds. Different statistical methods i.e., correlation coefficient of non-cross validation (r2), correlation coefficient of leave-one-out cross-validation (q2), Fisher's test (F) and bootstrapping were used to validate the developed models. Out of several charge models and alignment-based approaches, Merck Molecular Force Field (MMFF94) charges using structure-based alignment yielded highly predictive CoMFA (q2 = 0.583, Predr2 = 0.751) and CoMSIA (q2 = 0.690, Predr2 = 0.767) models. The models exhibited that electrostatic, steric, HBA, HBD, and hydrophobic fields play a key role in structure activity relationship of these compounds. Using the contour maps information of the best predictive model, new compounds were designed and docked at the TTK active site to predict their plausible binding modes. The structural stability of the TTK complexes with new compounds was confirmed using MD simulations. The simulation studies revealed that all compounds formed stable complexes. Similarly, MM/PBSA method based free energy calculations showed that these compounds bind with reasonably good affinity to the TTK protein. Overall molecular modelling results suggest that newly designed compounds can act as lead compounds for the optimization of TTK inhibitors.

Dataset Information

Design and Screening of New Lead Compounds for Autism Based on QSAR Model and Molecular Docking Studies.

Publications

Design and Screening of New Lead Compounds for Autism Based on QSAR Model and Molecular Docking Studies.

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