scholarly journals Design, Synthesis, Biological Evaluation, and Molecular Modeling of 2-Difluoromethylbenzimidazole Derivatives as Potential PI3Kα Inhibitors

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 387
Author(s):  
Xiangcong Wang ◽  
Moxuan Zhang ◽  
Ranran Zhu ◽  
Zhongshan Wu ◽  
Fanhong Wu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Modeling ◽  
3D Qsar ◽  
Binding Mode ◽  
Biological Evaluation ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Comfa Model ◽  
Qsar Models

PI3Kα is one of the potential targets for novel anticancer drugs. In this study, a series of 2-difluoromethylbenzimidazole derivatives were studied based on the combination of molecular modeling techniques 3D-QSAR, molecular docking, and molecular dynamics. The results showed that the best comparative molecular field analysis (CoMFA) model had q2 = 0.797 and r2 = 0.996 and the best comparative molecular similarity indices analysis (CoMSIA) model had q2 = 0.567 and r2 = 0.960. It was indicated that these 3D-QSAR models have good verification and excellent prediction capabilities. The binding mode of the compound 29 and 4YKN was explored using molecular docking and a molecular dynamics simulation. Ultimately, five new PI3Kα inhibitors were designed and screened by these models. Then, two of them (86, 87) were selected to be synthesized and biologically evaluated, with a satisfying result (22.8 nM for 86 and 33.6 nM for 87).

scholarly journals Computational Investigation of Adenosine 5′- (α, β-methylene)- diphosphate (AMPCP) Derivatives as Ecto-5′-nucleotidase (CD73) Inhibitors by Using 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulations

2021 ◽  
Author(s):  
Jiatong Wen ◽  
Heng Zhang ◽  
Churen Meng ◽  
Di Zhou ◽  
Gang Chen ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Tumor Cells ◽  
Binding Free Energy ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Comfa Model ◽  
Structure Activity

Abstract CD73, as a surface enzyme anchored on the outside of the cell membrane via glycosylphosphatidylinositol (GPI), can convert the AMP in the tumor cell microenvironment into adenosine to promote the growth of tumor cells. It has been overexpressed in many different types of human tumors, such as gastric cancer, pancreatic cancer, liver cancer and other tumor cells. Therefore, targeted inhibitors of CD73 are considered as potential tumor treatment methods. Due to the low bioavailability of nucleoside CD73 inhibitors, it is necessary to develop new inhibitors. In this study, through molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular dynamics (MD) simulations, a series of CD73 inhibitors were calculated and studied to reveal their structure-activity relationships. Through molecular docking studies, explore the possible mode of interaction between inhibitors and protein. Subsequently, a 3D-QSAR model was established by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). For the best CoMFA model, the Q2 and R2 values ​​are 0.708 and 0.983, respectively, while for the best CoMSIA model, the Q2 and R2 values ​​are 0.809 and 0.992, respectively. In addition, the stability of the complex formed by the two inhibitors and CD73 was evaluated by molecular dynamics simulation, and the results are consistent with the results of molecular docking and 3D-QSAR research. Finally, the binding free energy was calculated by the surface area method (MM-GBSA), and the results are consistent with the activities that Van Der Waals and Coulomb contribute the most during the binding process of the molecule to the CD73 protein. In conclusion, our research provides valuable information for the further development of CD73 inhibitors.

In Silico Studies of Piperidine Derivatives as Protein Kinase B Inhibitors through 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation

2021 ◽  
Vol 18 ◽  
Author(s):  
Sisi Liu ◽  
Yaxin Li ◽  
Jin Wang ◽  
Xue Rui ◽  
Haobo Tian ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
Protein Kinase ◽  
Protein Kinase B ◽  
Similarity Index ◽  
3D Qsar ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
In Silico Studies

Background: Protein kinase B (Akt) is a serine/threonine-protein kinase that drives the diverse physiological process. Akt is a promising therapeutic target, which involves cancer cell growth, survival, proliferation and metabolism. Objective: The study aims to design highly active Akt inhibitors and to elucidate the structural requirements for their biological activity, we analyzed the key binding features and summarized the structural determinants for their bioactivities. Methods: A series of piperidine derivatives have been investigated employing three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulation. Results: The statistics of the comparative molecular field analysis (CoMFA) model (Q2=0.631, R2=0.951) and the comparative molecular similarity index analysis (CoMSIA) model (Q2=0.663, R2=0.966) indicated that our 3D-QSAR model was accurate and reliable. Besides, the stability of receptor-ligand interactions under physiological conditions was then evaluated by molecular dynamics simulation, in agreement with the molecular docking results. Conclusion: Our study provided valuable insights for the discovery of potent Akt inhibitors.

scholarly journals 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulation Study of Thieno[3,2-b]Pyrrole-5- Carboxamide Derivatives as LSD1 Inhibitors

2019 ◽  
Author(s):  
Meilan Huang ◽  
yongtao Xu ◽  
Zihao He ◽  
Min Yang ◽  
Hongyi Liu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Binding Interaction ◽  
Contour Maps

Histone Lysine Specific Demethylase 1 (LSD1) is overexpressed in many cancers and become a new target for anticancer drugs. In recent years, the small molecule inhibitors with various structures targeting LSD1 have been reported. Here we report the binding interaction modes of a series of thieno[3,2-b]pyrrole-5-carboxamides LSD1 inhibitors using molecular docking, three dimensional quantitative structure-activity relationship (3D-QSAR). Comparative molecular field analysis (CoMFA q2=0.783, r2=0.944, r2pred=0.851) and Comparative molecular similarity indices analysis (CoMSIA q2=0.728, r2=0.982, r2pred=0.814) were used to establish 3D-QSAR models, which had good verification and prediction capabilities. Based on the contour maps and the information of molecular docking, 8 novel small molecules were designed in silico, among which compounds D4, D5 and D8 with high predictive activity were subjected to further molecular dynamics simulations (MD), and their possible binding modes were explored. It was found that Asn535 plays a crucial role in stabilizing the inhibitors. Furthermore, the ADME and bioavailability prediction for D4, D5 and D8 were carried out. The results would provide valuable guidance for designing new reversible LSD1 inhibitors in the future.

scholarly journals In silico Prediction of Novel SARS-CoV 3CL pro Inhibitors: a Combination of 3D-QSAR, Molecular Docking, ADMET Prediction, and Molecular Dynamics Simulation

2021 ◽  
Vol 12 (4) ◽  
pp. 5100-5115
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Predictive Ability ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Qsar Study ◽  
Determination Coefficient ◽  
Pharmacokinetic Properties

The Chymotrypsin-like protease (3CLpro) is a drug target in the coronavirus because of its role in processing the polyproteins that are translated from the viral RNA. This study applied 3D quantitative structure-activity relationship (3D-QSAR), molecular docking, and ADMET prediction on a series of SARS-CoV 3CLpro inhibitors. The 3D-QSAR study was applied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods, which gave the cross-validation coefficient (Q2) values of 0.64 and 0.80, the determination coefficient (R2) values of 0.998 and 0.993 and the standard error of the estimate (SEE) values of 0.046 and 0.091, respectively. The acceptable values of the determination coefficient (R2 test) to CoMFA and CoMSIA respectively corresponding to values of 0.725 and 0.690 utilizing a test set of seven molecules prove the high predictive ability of this model. Molecular docking analysis was utilized to validate 3D-QSAR methods and explain the binding site interactions and affinity between the most active ligands and the SARS-CoV 3CLpro receptor. Based on these results, a novel series of compounds were predicted, and their pharmacokinetic properties were verified using drug-likeness and ADMET prediction. Finally, the best-docked candidate molecules were subjected to molecular dynamics (MD) simulation to affirm their dynamic behavior and stability.

scholarly journals Simulation studies, 3D QSAR and molecular docking on a point mutation of protein kinase B with flavonoids targeting ovarian Cancer

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Suchitra Maheswari Ajjarapu ◽  
Apoorv Tiwari ◽  
Gohar Taj ◽  
Dev Bukhsh Singh ◽  
Sakshi Singh ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
Protein Kinase ◽  
Point Mutation ◽  
Protein Kinase B ◽  
3D Qsar ◽  
Dynamics Simulation ◽  
Simulation Studies

Abstract Background Ovarian cancer is the world’s dreaded disease and its prevalence is expanding globally. The study of integrated molecular networks is crucial for the basic mechanism of cancer cells and their progression. During the present investigation, we have examined different flavonoids that target protein kinases B (AKT1) protein which exerts their anticancer efficiency intriguing the role in cross-talk cell signalling, by metabolic processes through in-silico approaches. Method Molecular dynamics simulation (MDS) was performed to analyze and evaluate the stability of the complexes under physiological conditions and the results were congruent with molecular docking. This investigation revealed the effect of a point mutation (W80R), considered based on their frequency of occurrence, with AKT1 protein. Results The ligand with high docking scores and favourable behaviour on dynamic simulations are proposed as potential W80R inhibitors. A virtual screening analysis was performed with 12,000 flavonoids satisfying Lipinski’s rule of five according to which drug-likeness is predicted based on its pharmacological and biological properties to be active and taken orally. The pharmacokinetic ADME (adsorption, digestion, metabolism, and excretion) studies featured drug-likeness. Subsequently, a statistically significant 3D-QSAR model of high correlation coefficient (R2) with 0.822 and cross-validation coefficient (Q2) with 0.6132 at 4 component PLS (partial least square) were used to verify the accuracy of the models. Taxifolin holds good interactions with the binding domain of W80R, highest Glide score of − 9.63 kcal/mol with OH of GLU234 and H bond ASP274 and LEU156 amino acid residues and one pi-cation interaction and one hydrophobic bond with LYS276. Conclusion Natural compounds have always been a richest source of active compounds with a wide variety of structures, therefore, these compounds showed a special inspiration for medical chemists. The present study has aimed molecular docking and molecular dynamics simulation studies on taxifolin targeting W80R mutant protein of protein kinase B/serine- threonine kinase/AKT1 (EC:2.7.11.1) protein of ovarian cancer for designing therapeutic intervention. The expected result supported the molecular cause in a mutant form which resulted in a gain of ovarian cancer. Here we discussed validations computationally and yet experimental evaluation or in vivo studies are endorsed for further study. Several of these compounds should become the next marvels for early detection of ovarian cancer.

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