SAR and QSAR in Environmental Research最新文献

Ras is identified as a human oncogene which is frequently mutated in human cancers. Among its three isoforms (K, N, and H), KRas is the most frequently mutated. Mutant Ras exhibits reduced GTPase activity, leading to the prolonged activation of its conformation. This extended activation promotes Ras-dependent signalling, contributing to cancer cell survival and growth. In this study, we conducted structure-based virtual screening of 11698 phytochemicals in the IMPPAT 2.0 database to identify inhibitors of KRas. We identified two phytochemicals with fair binding affinity, and their binding patterns with KRas were analysed in detail. Additionally, we performed 200 ns molecular dynamics (MD) simulations of each complex to understand the interaction mechanism of KRas with the newly identified compounds, such as Samaderine E and Bismurrayaquinone A. These phytochemicals bind to the binding site residues ARG41 and ASP54, causing conformational changes in KRas. The RMSD, RMSF, Rg, SASA, hydrogen bond, and secondary structure analysis studies suggested the potential of the selected phytochemicals. The identification of Samaderine E and Bismurrayaquinone A as phytochemicals binding to a functional pocket on KRas, supported by PCA and FEL analysis, highlights their potential as effective therapeutic inhibitors of the KRas oncoprotein.

A comprehensive computational strategy that combined QSAR modelling, molecular docking, and ADMET analysis was used to discover potential inhibitors for β-secretase 1 (BACE-1). A dataset of 1,138 compounds with established BACE-1 inhibitory activities was used to build a QSAR model using mol2vec descriptors and support vector regression. The obtained model demonstrated strong predictive performance (training set: r² = 0.790, RMSE = 0.540, MAE = 0.362; test set: r² = 0.705, RMSE = 0.641, MAE = 0.495), indicating its reliability in identifying potent BACE-1 inhibitors. By applying this QSAR model together with molecular docking, seven compounds (ZINC8790287, ZINC20464117, ZINC8878274, ZINC96116481, ZINC217682404, ZINC217786309 and ZINC96113994) were identified as promising candidates, exhibiting predicted log IC₅₀ values ranging from 0.361 to 1.993 and binding energies ranging from -10.8 to -10.7 kcal/mol. Further analysis using ADMET studies and molecular dynamics simulations provided further support for the potential of compound 279 (ZINC96116481) and compound 945 (ZINC96113994) as drug candidates. However, since our study is purely theoretical, further experimental validation through in vitro and in vivo studies is essential to confirm these promising findings.

Bromodomain-containing protein 4 (BRD4) plays an important role in gene transcription in a variety of diseases, including inflammation and cancer. However, the mechanism by which the BRD4 inhibitors bind selectively to its bromodomain 1 (BRD4-BD1) and bromodomain 2 (BRD4-BD2) remains unclear. Studying the interaction mechanism between bromodomain of BRD4 and inhibitors will provide new ideas for drug development and disease treatment. To explore the molecular mechanism of selective binding of three novel phenoxypyridone Cpd11, Cpd14, and Cpd23 to BRD4-BD1 and BRD4-BD2, respectively, molecular docking, molecular dynamics (MD) simulation, and free energy calculation containing molecular mechanics generalized born surface area (MM-GBSA) and solvation interaction energy (SIE) were achieved. The results show that these three inhibitors have different effects on the internal dynamics of BRD4-BD1 and BRD4-BD2, but the key interactions are similar. Key residues of BRD4-BD1 and BRD4-BD2, Ile146/Val439, Trp81/Trp374, Phe83/Phe375, Val87/Val380, Leu92/Leu385, Leu94/Leu387, Tyr97/Tyr390, and Asn140/Asn433, play a key role in selective binding of BRD4-BD1 and BRD4-BD2 to these three inhibitors. At the same time, non-polar interactions, especially van der Waals interactions, are the main drivers of the interactions of these three inhibitors with BRD4-BD1 and BRD4-BD2. These results provide useful dynamic and energy information for the development of novel highly selective phenoxypyridone inhibitors targeting BRD4-BD2.

The Nipah virus (NiV) is an emerging pathogenic paramyxovirus that causes severe viral infection with a high mortality rate. This study aimed to model the effectual binding of marine sponge-derived natural compounds (MSdNCs) towards RNA-directed RNA polymerase (RdRp) of NiV. Based on the functional relevance, RdRp of NiV was selected as the prospective molecular target and 3D-structure, not available in its native form, was modelled. The effectual binding of selected MSdNCs that fulfilled the pharmacokinetics properties were docked against RdRp and the binding energy (BE) of the interaction was compared with the BE of the interaction between standard antiviral compound Remdesivir and RdRp. The stability of the best-docked pose was further confirmed by molecular dynamics (MD) simulation and binding free energy calculations. The current study revealed that the hypothetical RdRp model showed ideal stereochemical features. Molecular docking, dynamic and energy calculations suggested that Hamigeran-B (1R,3aR,9bR)-7- bromo-6-hydroxy-3a,8-dimethyl-1-propan-2-yl-1,2,3,9b-tetrahydrocyclopenta[a]naphthalene-4,5-dione) is a potent binder (BE: -6.35 kcal/mol) to RdRp when compared with the BE of Remdesivir and RdRp (-4.98 kcal/mol). This study suggests that marine sponge-derived Hamigeran-B is a potential binder to NiV-RdRp and that the present in silico model provides insight for future drug discovery against NiV infections.

This study illustrates the use of chemical fingerprints with machine learning for blood-brain barrier (BBB) permeability prediction. Employing the Blood Brain Barrier Database (B3DB) dataset for BBB permeability prediction, we extracted nine different fingerprints. Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms were used to develop models for permeability prediction. Random Forest recursive Feature Selection (RF-RFS) method was used for extracting informative attributes. An additional database was employed for the validation phase. The results indicate that all nine datasets achieved good performance in training, test and validation stages. We further took MACC Keys fingerprints, one of the best performing models for explainability analysis. For this purpose, we used SHapley Additive exPlanations (SHAP) analysis on this dataset for the identification of key structural features influencing BBB permeability prediction. These features include aliphatic carbons, methyl groups and oxygen-containing groups. This study highlights the effectiveness of different fingerprint descriptors in predicting BBB permeability. SHAP analysis provides value additions to the simulations. These simulations will be of significant help in drug discovery processes, particularly in developing Central Nervous System (CNS) therapeutics.

ErbB2 kinase is a key target in approximately 20% of breast cancer cases; however, ErbB2-positive cells may shift their dependence to ErbB4 upon developing resistance to ErbB2 inhibitors. Targeting ErbB4 presents a viable strategy to address this challenge. This study employs a comprehensive approach combining structure-based pharmacophore modelling, molecular docking, and MM-GBSA calculations to identify novel ErbB4 kinase inhibitors. Critical pharmacophoric features were extracted from the crystal structures of ErbB4-lapatinib, followed by virtual screening of the Chembl database to discover potential small molecule candidates. Furthermore, the ADMET profiles of 11 shortlisted candidates were assessed to verify their pharmacokinetic and toxicity properties, identifying Chembl310724, Chembl521284, and Chembl4168686 as promising inhibitors of ErbB4 kinase activity with the binding free energy (ΔG_bind) values of -99.84, -89.42 and -86.06 kcal/mol, respectively. This integrated methodology not only enhances our understanding of ErbB4 inhibition but also sets a foundation for the rational design of targeted therapies addressing breast cancer with ErbB4 dependency.

Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based ligand screening. Additionally, toxicity prediction, molecular docking, and molecular dynamics (MD) simulations were conducted. NP study identified key genes related to HCC, particularly the enzymes AKT1 and GSK3β. Pathway analysis revealed that crucial pathways like PI3K-AKT and WNT signalling play pivotal roles in HCC progression. Using ML, potential inhibitors for AKT1 and GSK3β were identified, including CHEMBL2177361 and CHEMBL403354 for AKT1, and CHEMBL3652546 and CHEMBL4641631 for GSK3β. post-MD analyses, including RMSD, 2D-RMSD, RMSD cluster, RMSF, PCA, DCCM, residence time analysis, diffusion coefficient, autoencoder-based dimensionality reduction, FEL and MM/GBSA were performed to understand the protein-ligand interactions. The present study reveals the stable interactions of the inhibitors with AKT1 and GSK3β. The binding free energies of all the four complexes were -39.9, -46.8, -41.6, and -45.9 kcal/mol, respectively. This research provides novel insights into the genes and pathways involved in the progression and pathogenesis of HCC using bioinformatics tools. Furthermore, ML-based virtual screening identified potent inhibitors against the target proteins of HCC, such as AKT1 and GSK3β.

Identifying new compounds with minimal side effects to enhance patients' quality of life is the ultimate goal of drug discovery. Due to the expensive and time-consuming nature of experimental investigations and the scarcity of data in traditional QSAR studies, deep transfer learning models, such as the BERT model, have recently been suggested. This study evaluated the model's performance in predicting the anti-proliferative activity of five cancer cell lines (HeLa, MCF7, MDA-MB231, PC3, and MDA-MB) using over 3,000 synthesized molecules from PubChem. The results indicated that the model could predict the class of designed small molecules with acceptable accuracy for most cell lines, except for PC3 and MDA-MB. The model's performance was further tested on an in-house dataset of approximately 25 small molecules per cell line, based on IC50 values. The model accurately predicted the biological activity class for HeLa with an accuracy of $0.77\pm 0.4$ and demonstrated acceptable performance for MCF7 and MDA-MB231, with accuracy between 0.56 and 0.66. However, the results were less reliable for PC3 and HepG2. In conclusion, the ChemBERTa fine-tuned model shows potential for predicting outcomes on in-house datasets.

CDK/Cyclins are dysregulated in several human cancers. Recent studies showed inhibition of CDK4/6 was responsible for controlling cell cycle progression and cancer cell growth. In the present study, atom-based and field-based 3D-QSAR, virtual screening, molecular docking and molecular dynamics studies were done for the development of novel pyrrolo[2,3-d]pyrimidine (P2P) derivatives as anticancer agents. The developed models showed good Q² and r² values for atom-based 3D-QSAR, which were equal to 0.7327 and 0.8939, whereas for field-based 3D-QSAR the values were 0.8552 and 0.6255, respectively. Molecular docking study showed good-binding interactions with amino acid residues such as VAL-101, HIE-100, ASP-104, ILE-19, LYS-147 and GLU-99, important for CDK4/6 inhibitory activity by using PDB ID: 5L2S. Pharmacophore hypothesis (HHHRR_1) was used in the screening of ZINC database. The top scored ZINC compound ZINC91325512 showed binding interactions with amino acid residues VAL-101, ILE-19, and LYS-147. Enumeration study revealed that the screened compound R1 showed binding interactions with VAL 101 and GLN 149 residues. Furthermore, the Molecular dynamic study showed compound R1, ZINC91325512 and ZINC04000264 having RMSD values of 1.649, 1.733 and 1.610 Å, respectively. These ZINC and enumerated compounds may be used for the development of novel pyrrolo[2,3-d]pyrimidine derivatives as anticancer agent.

Diabetes is attributed to an increased vulnerability to bacterial infection linked to unregulated hyperglycaemia. The present study highlights the formulation of nanoparticles with phyto-compound piperine (PIP) encapsulated within non-toxic biodegradable polymer poly-lactide co-glycolide (PLGA) which showed a variety in surface functionality, biocompatibility, and the ability to tailor an optimized release rate from its polymeric enclosure. The observations revealed that nanopiperine (NPIP) pre-treatment in mice inhibited alteration in hepatic tissue architecture and hepato-biochemical parameters in diabetes and its associated bacterial infections. NPIP also decreased the propensity of lipids to undergo an oxidation process and stabilized the membrane lipids in vivo, thereby lowering oxidative stress and preventing enzymatic activation of CYP1A1. This result is corroborated with the in silico molecular docking study where PIP binding with CYP1A1 gave -11.32 Kcal/mol dock score value. The antibacterial activity of PIP was further demonstrated by the in silico PIP and Ef-Tu protein-binding efficacy revealing -6.48 Kcal/mol score value which was coupled with the results of in vitro studies where the zone of inhibition assay with NPIP against Staphylococcus aureus and Escherichia coli. Thus, NPIP could serve as a potential drug candidate in modulating targeted proteins to inhibit the progression of hyperglycaemia and its associated microbes.