Screening of phytochemicals against protease activated receptor 1 (PAR1), a promising target for cancer

Deciphering quinazoline derivatives' interactions with EGFR: a computational quest for advanced cancer therapy through 3D-QSAR, virtual screening, and MD simulations

Introduction

The epidermal growth factor receptor (EGFR) presents a crucial target for combatting cancer mortality.

Methods

This study employs a suite of computational techniques, including 3D-QSAR, ligand-based virtual screening, molecular docking, fingerprinting analysis, ADME, and DFT-based analyses (MESP, HOMO, LUMO), supplemented by molecular dynamics simulations and MMGB/PBSA free energy calculations, to explore the binding dynamics of quinazoline derivatives with EGFR. With strong q2 and r2 values from CoMFA and CoMSIA models, our 3D- QSAR models reliably predict EGFR inhibitors' efficacy.

Results and Discussion

Utilizing a potent model compound as a reference, an E-pharmacophore model was developed to sift through the eMolecules database, identifying 19 virtual screening hits based on ShapeTanimoto, ColourTanimoto, and TanimotoCombo scores. These hits, assessed via 3D- QSAR, showed pIC50 predictions consistent with experimental data. Our analyses elucidate key features essential for EGFR inhibition, reinforced by ADME studies that reveal favorable pharmacokinetic profiles for most compounds. Among the primary phytochemicals examined, potential EGFR inhibitors were identified. Detailed MD simulation analyses on three select ligands-1Q1, 2Q17, and VS1-demonstrated their stability and consistent interaction over 200 ns, with MM/GBSA values corroborating their docking scores and highlighting 1Q1 and VS1's superior EGFR1 affinity. These results position VS1 as an especially promising lead in EGFR1 inhibitor development, contributing valuable insights towards crafting novel, effective EGFR1 inhibitors.

Molecular modeling of novel 2-aminopyridine derivatives as potential JAK2 inhibitors: a rational strategy for promising anticancer agents

Janus kinase 2(JAK2) is a potential target for anticancer drugs in the treatment of numerous myeloproliferative diseases due to its central role in the JAK/STAT signaling cascade. In this study, the binding behavior of 2 amino-pyridine derivatives as JAK2 inhibitors was investigated by using multifaceted strategies including 3D-QSAR, molecular docking, Fingerprint analysis, MD simulations, and MM-PBSA calculations. A credible COMFA (q2 = 0.606 and r2 = 0.919) and COMSIA (q2 = 0.641 and r2 = 0.992) model was developed, where the internal and external validation revealed that the obtained 3D-QSAR models could be capable of predicting bioactivities of JAK2 inhibitors. The structural criteria provided by the contour maps of model were used to computationally develop more potent 100 new JAK2 inhibitors. Docking studies were conducted on the model data set and newly developed compounds (in-house library) to demonstrate their binding mechanism and highlight the key interacting residues within JAK2 active site. The selected docked complexes underwent MD simulation (100 ns), which contributed in the further study of the binding interactions. Binding free energy analyses (MMGB/PBSA) revealed that key residues such as Glu930, Leu932 (hinge region), Asp939 (solvent accessible region), Arg980, Asn981and Asp994 (catalytic site) have a significantly facilitate ligand-protein interactions through H-bonding and van der Waals interactions. The preliminary in-silico ADMET evaluation revealed encouraging results for all the modeled and in-house library compounds. The findings of this research have the potential to offer valuable recommendations for the advancement of novel, potent, and efficacious JAK2 inhibitors. Overall, this work has successfully employed a wide range of computer-based methodologies to understand the interaction dynamics between 2-amino-pyridine derivatives and the JAK2 enzyme, which is a crucial target in myeloproliferative disorders.Communicated by Ramaswamy H. Sarma.

Integrated computational approaches for designing potent pyrimidine-based CDK9 inhibitors: 3D-QSAR, docking, and molecular dynamics simulations

CDK9 is an emerging target for the development of anticancer drugs. The development of CDK9 inhibitors with significant potency had consistently posed a formidable challenge. In the current research, a number of computational methodologies, such as, 3D-QSAR, molecular docking, fingerprint analysis, molecular dynamic (MD) simulations followed by MMGB/PBSA and ADMET studies were used systemically to uncover the binding mechanism of pyrimidine derivatives against CDK9. The CoMFA and CoMSIA models having high q2 (0.53, 0.54) and r2 values (0.96, 0.93) respectively indicating that model could accurately predict the bioactivities of CDK9 inhibitors. Using the R-group exploration technique implemented by the Spark™ by Cresset group, the structural requirements revealed by the contour maps of model were utilized strategically to create an in-house library of 100 new CDK9 inhibitors. Additionally, the compounds from the in-house library were mapped into 3D-QSAR model which predicted pIC50 values comparable to the experimental values. A comparison between 3D-QSAR generated contours and molecular docking conformation of ligands was performed to elucidate the essentials of CDK9 inhibitor design. MD simulations (100 ns) were performed on the selected docked complexes A21, A14 and D98 which contributed in validating the binding interactions. According to the findings of binding free energy analysis (MMGB/PBSA), It was observed that residues CYS106 and GLU107 had a considerable tendency to facilitate ligand-protein interactions via H-bond interactions. The aforementioned findings have the potential to enhance researchers comprehension of the mechanism underlying CDK9 inhibition and may be utilized in the development of innovative and efficacious CDK9 inhibitors.

Molecular Docking, Validation, Dynamics Simulations, and Pharmacokinetic Prediction of Phytochemicals Isolated From Croton dichogamus Against the HIV-1 Reverse Transcriptase

The human immunodeficiency virus (HIV) infection and the associated acquired immune deficiency syndrome (AIDS) remain global challenges even after decades of successful treatment, with eastern and southern Africa still bearing the highest burden of disease. Following a thorough computational study, we report top 10 phytochemicals isolated from Croton dichogamus as potent reverse transcriptase inhibitors. The pentacyclic triterpenoid, aleuritolic acid (L12) has displayed best docking pose with binding energy of -8.48 kcal/mol and Ki of 0.61 μM making it superior in binding efficiency when compared to all docked compounds including the FDA-approved drugs. Other phytochemicals such as crotoxide A, crothalimene A, crotodichogamoin B and crotonolide E have also displayed strong binding energies. These compounds could further be investigated as potential antiretroviral medication.

Identification of Potent Chloride Intracellular Channel Protein 1 Inhibitors from Traditional Chinese Medicine through Structure-Based Virtual Screening and Molecular Dynamics Analysis

Chloride intracellular channel 1 (CLIC1) is involved in the development of most aggressive human tumors, including gastric, colon, lung, liver, and glioblastoma cancers. It has become an attractive new therapeutic target for several types of cancer. In this work, we aim to identify natural products as potent CLIC1 inhibitors from Traditional Chinese Medicine (TCM) database using structure-based virtual screening and molecular dynamics (MD) simulation. First, structure-based docking was employed to screen the refined TCM database and the top 500 TCM compounds were obtained and reranked by X-Score. Then, 30 potent hits were achieved from the top 500 TCM compounds using cluster and ligand-protein interaction analysis. Finally, MD simulation was employed to validate the stability of interactions between each hit and CLIC1 protein from docking simulation, and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) analysis was used to refine the virtual hits. Six TCM compounds with top MM-GBSA scores and ideal-binding models were confirmed as the final hits. Our study provides information about the interaction between TCM compounds and CLIC1 protein, which may be helpful for further experimental investigations. In addition, the top 6 natural products structural scaffolds could serve as building blocks in designing drug-like molecules for CLIC1 inhibition.

Molecular interaction fingerprint approaches for GPCR drug discovery

Protein-ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein-ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein-ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.