Molecular modelling of quinoline derivatives as telomerase inhibitors through 3D-QSAR, molecular dynamics simulation, and molecular docking techniques

In-silico molecular modelling studies of some camphor imine based compounds as anti-influenza A (H1N1) pdm09 virus agents

The advent of influenza A (H1N1) drug-resistant strains led to the search quest for more potent inhibitors of the influenza A virus, especially in this devastating COVID-19 pandemic era. Hence, the present research utilized some molecular modelling strategies to unveil new camphor imine-based compounds as anti-influenza A (H1N1) pdm09 agents. The 2D-QSAR results revealed GFA-MLR (R2train = 0.9158, Q2=0.8475) and GFA-ANN (R2train = 0.9264, Q2=0.9238) models for the anti-influenza A (H1N1) pdm09 activity prediction which have passed the QSAR model acceptability thresholds. The results from the 3D-QSAR studies also revealed CoMFA (R2train =0.977, Q2=0.509) and CoMSIA_S (R2train =0.976, Q2=0.527) models for activity predictions. Based on the notable information derived from the 2D-QSAR, 3D-QSAR, and docking analysis, ten (10) new camphor imine-based compounds (22a-22j) were designed using the most active compound 22 as the template. Furthermore, the high predicted activity and binding scores of compound 22j were further justified by the high reactive sites shown in the electrostatic potential maps and other quantum chemical calculations. The MD simulation of 22j in the active site of the influenza hemagglutinin (HA) receptor confirmed the dynamic stability of the complex. Moreover, the appraisals of drug-likeness and ADMET properties of the proposed compounds showed zero violation of Lipinski's criteria with good pharmacokinetic profiles. Hence, the outcomes in this work recommend further in-depth in vivo and in-vitro investigations to validate these theoretical findings.Communicated by Ramaswamy H. Sarma.

Molecular docking, 3D-QASR and molecular dynamics simulations of benzimidazole Pin1 inhibitors

Pin1 (protein interacting with never-in-mitosis akinase-1) is a member of the family of peptidylprolyl cis-trans isomerases (PPIases) that specifically recognize and isomerize substrates containing phosphorylated Ser/Thr-Pro sequences. Pin1 is involved in many cellular processes and plays a key role in the cell cycle, transcriptional regulation, cell metabolism, proliferation and differentiation, and its abnormalities lead to degenerative and neoplastic diseases. Pin1 is highly expressed in human cancers and promotes the development of tumors by activating multiple oncogenes and inactivating multiple tumor suppressor genes, making it an attractive target for cancer therapy. In this study, we investigated the binding mechanism and conformational relationship between benzimidazole Pin1 inhibitors and Pin1 proteins by molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, binding free energy calculations and decomposition, and molecular dynamics simulations. Molecular docking and molecular dynamics simulations disclosed the most likely binding pose of benzimidazoles with the Pin1 protein. The results of 3D-QSAR modeling indicated that electrostatic fields, hydrophobic fields and hydrogen bonding play important roles in the binding process of inhibitors to proteins. The binding free energy calculations and energy decomposition indicated that Lys63, Arg69, Cys113, Leu122, Met130, and Ser154 may be key residues in the binding of benzimidazole-based inhibitors to the Pin1 protein. This study provides an important theoretical basis for the design and optimization of benzimidazole compounds.

Molecular docking, 3D-QASR and molecular dynamics simulations of thiazoles Pin1 inhibitors

Pin1 (protein interacting with never-in-mitosis akinase-1) is a member of the PPIase (peptidylprolyl cis-trans isomerase) family. It can interact with a variety of carcinogenic or tumor suppressive phosphorylated proteins. The interaction results in the conformational changes of target proteins, and ultimately regulates the activity of these proteins. These activity changes play a key role in tumorigenesis. Pin1 is an attractive target for cancer therapy due to its over-expression and/or activation in various types of cancer and the disorder of Proline directed phosphorylation. In this study, molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular dynamics (MD) simulations were performed to investigate the structure-activity relationship and binding mechanism of 45 thiazole-class Pin1 inhibitors. Molecular docking studies predict the binding mode and the interactions between the ligand and the receptor protein. The results of the 3 D-QSAR model show that electrostatic field, hydrophobic field and hydrogen bond play important roles in the binding process of inhibitors to protein. Molecular dynamics simulation results reveal that the complex of the ligand and the receptor protein are stable at 300 K. The binding free energy calculation and energy decomposition results show that His59, Cys113, Ser114, Ser115, Leu122, Met130, Gln131, Phe134, Ser154 and His157 may be the key to the inhibitor binding to Pin1 protein. This study provides an important theoretical basis for further development of the new Pin1 inhibitor design. These results can provide more useful information for our further drug design. Communicated by Ramaswamy H. Sarma.

Targeting the HIV-1 Tat and Human Tat Protein Complex through Natural Products: An In Silico Docking and Molecular Dynamics Simulation Approach

Background:

Tat protein is considered essential for substantial HIV-1 replication, and is also required to break HIV-1 latency, resulting in productive HIV replication. The multifaceted regulatory role of HIV Tat and the fact that it is expressed in the early stages of HIV infection justify its potential as an anti-HIV drug target.

Objective:

The present study was undertaken with the aim to target HIV-1 Tat protein with natural compounds which could help in identifying potential inhibitors against HIV-1 Tat.

Methods:

In this study, we compared the binding of Tat protein and Human P-TEFb Tat protein complex (TPC) with phyto-steroids and terpenes to evaluate their potential for HIV-1 treatment. The docking ability of plant products with HIV-1 Tat and TPC was studied with respect to dissociation constant, geometric shape complementary score, approximate interface area, and binding energy using Patch dock and YASARA. Molecular dynamics simulation was set up to investigate the interactions of the natural compounds with Tat protein and human tat protein complex (TPC).

Results:

The binding energy and dissociation constant of Diosgenin, Catharanthine and Ginkgolide A with Tat and TPC were comparable to antiretroviral drugs, Maraviroc and Emtricitabine. The natural products, Diosgenin, Ginkgolide A and Catharanthine, showed the highest binding energy and were stable with Tat protein and TPC in the entire MD simulation run.

Conclusion:

The natural products, Diosgenin, Ginkgolide A and Catharanthine, showed highest binding energy and were stable with Tat protein and TPC in the entire MD simulation run. The binding energy and dissociation constant of Diosgenin, Catharanthine and Ginkgolide A with Tat and TPC were comparable to antiretroviral drugs, Maraviroc and Emtricitabine.

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Background

Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).

Results

The 2D-QSAR modeling results showed GFA-MLR ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9192, Q2 = 0.8767, R2adj = 0.8991, RMSE = 0.0959, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8943, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7745) and GFA-ANN ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9227, Q2 = 0.9212, RMSE = 0.0940, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8831, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9620, Q2 = 0.643) and CoMSIA_SED ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.8770, Q2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> − 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents.

Conclusion

The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Influenza virus disease is one of the most infectious diseases responsible for many human deaths, and the high mutability of the virus causes drug resistance effects in recent times. As such, it became necessary to explore more inhibitors that could avert future influenza pandemics. The present research utilized some in-silico modelling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions on some 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase (NA) inhibitors. The 2D-QSAR modelling results revealed GFA-MLR (R train 2 =0.8414, Q2 = 0.7680) and GFA-ANN (R train ​ 2 =0.8754, Q2 = 0.8753) models with the most relevant descriptors (MATS3i, SpMax5_Bhe, minsOH and VE3_D) for predicting the inhibitory activities of the molecules which has passed the global criteria of accepting QSAR models. The results of the 3D-QSAR modelling results showed that CoMFA_ES (R train ​ 2 =0.9030, Q2 = 0.5390) and CoMSIA_EA (R train 2 =0.880, Q2 = 0.547) models are having good predicting ability among other developed models. The molecules were virtually screened via molecular docking simulation with the active site of NA protein receptor (pH1N1) which confirms their resilient potency when compared with zanamivir standard drug. Molecule 11 as the most potent molecule formed more H-bond interactions with the key residues such as TRP178, ARG152, ARG292, ARG371, and TYR406 that triggered the catalytic reactions for NA inhibition. Furthermore, six (6) molecules (9, 10, 11, 17, 22, and 31) with relatively high inhibitory activities and docking scores were identified as the possible leads for in-silico exploration of novel NA inhibitors. The drug-likeness and ADMET predictions of the lead molecules revealed non-violation of Lipinski's rule and good pharmacokinetic profiles respectively, which are important guidelines for rational drug design. Hence, the outcome of this study overlaid a solid foundation for the in-silico design and exploration of novel NA inhibitors with improved potency.

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

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.