Insight into structural requirements of ACE inhibitory dipeptides: QSAR and molecular docking studies

Machine Learning Approaches to Investigate the Structure-Activity Relationship of Angiotensin-Converting Enzyme Inhibitors

Angiotensin-converting enzyme inhibitors (ACEIs) play a crucial role in treating conditions such as hypertension, heart failure, and kidney diseases. Nevertheless, the ACEIs currently available on the market are linked to a variety of adverse effects including renal insufficiency, which restricts their usage. There is thus an urgent need to optimize the currently available ACEIs. This study represents a structure-activity relationship investigation of ACEIs, employing machine learning to analyze data sets sourced from the ChEMBL database. Exploratory data analysis was performed to visualize the physicochemical properties of compounds by investigating the distributions, patterns, and statistical significance among the different bioactivity groups. Further scaffold analysis has identified 9 representative Murcko scaffolds with frequencies ≥10. Scaffold diversity has revealed that active ACEIs had more scaffold diversity than their intermediate and inactive counterparts, thereby indicating the significance of performing lead optimization on scaffolds of active ACEIs. Scaffolds 1, 3, 6, and 8 are unfavorable in comparison with scaffolds 2, 3, 5, 7, and 9. QSAR investigation of compiled data sets consisting of 549 compounds led to the selection of Mordred descriptor and Random Forest algorithm as the best model, which afforded robust model performance (accuracy: 0.981, 0.77, and 0.745; MCC: 0.972, 0.658, and 0.617 for the training set, 10-fold cross-validation set, and testing set, respectively). To enhance the model's robustness and predictability, we reduced the chemical diversity of the input compounds by using the 9 most prevalent Murcko scaffold-matched compounds (comprising a total of 168) followed by a subsequent QSAR model investigation using Mordred descriptor and extremely gradient boost algorithm (accuracy: 0.973, 0.849, and 0.823; MCC: 0.959, 0.786, and 0.742 for the training set, 10-fold cross-validation set, and testing set, respectively). Further illustration of the structure-activity relationship using SALI plots has enabled the identification of clusters of compounds that create activity cliffs. These findings, as presented in this study, contribute to the advancement of drug discovery and the optimization of ACEIs.

Proteochemometrics modeling for prediction of the interactions between caspase isoforms and their inhibitors

Caspases (cysteine-aspartic proteases) play critical roles in inflammation and the programming of cell death in the form of necroptosis, apoptosis, and pyroptosis. The name of these enzymes has been chosen in accordance with their cysteine protease activity. They act as cysteines in nucleophilically active sites to attack and cleave target proteins in the aspartic acid and amino acid C-terminal. Based on the substrate's structure and the specificity, the physiological activity of caspases is divided. However, in apoptosis, the division of caspases into initiating caspases (caspase 2, 8, 9, and 10) and executive caspases (caspase 3, 6, and 7) is essential. The present study aimed to perform Proteochemometrics Modeling to generalize the data on caspases, which could predict ligand and protein interactions. In this study, we employed protein and ligand descriptors. Moreover, protein descriptors were computed using the Protr R package, while PADEL-Descriptor was employed for the computation of ligand descriptors. In addition, NCA (Neighborhood Component Analyses) was used for descriptor selection, and SVR, decision tree, and ensemble methods were utilized for the proteochemometrics modeling. This study shows that the ensemble model demonstrates superior performance compared with other models in terms of R², Q², and RMSE criteria.

New Insights on Glutathione's Supramolecular Arrangement and Its In Silico Analysis as an Angiotensin-Converting Enzyme Inhibitor

Angiotensin-converting enzyme (ACE) inhibitors are one of the most active classes for cardiovascular diseases and hypertension treatment. In this regard, developing active and non-toxic ACE inhibitors is still a continuous challenge. Furthermore, the literature survey shows that oxidative stress plays a significant role in the development of hypertension. Herein, glutathione's molecular structure and supramolecular arrangements are evaluated as a potential ACE inhibitor. The tripeptide molecular modeling by density functional theory, the electronic structure by the frontier molecular orbitals, and the molecular electrostatic potential map to understand the biochemical processes inside the cell were analyzed. The supramolecular arrangements were studied by Hirshfeld surfaces, quantum theory of atoms in molecules, and natural bond orbital analyses. They showed distinct patterns of intermolecular interactions in each polymorph, as well as distinct stabilizations of these. Additionally, the molecular docking study presented the interactions between the active site residues of the ACE and glutathione via seven hydrogen bonds. The pharmacophore design indicated that the hydrogen bond acceptors are necessary for the interaction of this ligand with the binding site. The results provide useful information for the development of GSH analogs with higher ACE inhibitor activity.

Topomer CoMFA and HQSAR Study on Benzimidazole Derivative as NS5B Polymerase Inhibitor

Background:

In recent years, the number of people infected with the hepatitis C virus (HCV) is increasing rapidly. This has become a major threat to global health, therefore, new anti- HCV drugs are urgently needed. HCV NS5B polymerase is an RNA-dependent RNA polymerase (RdRp), which plays an important role in virus replication, and can effectively prevent the replication of HCV sub-genomic RNA in daughter cells. It is considered a very promising HCV therapeutic target for the design of anti-HCV drugs.

Methods:

In order to explore the relationship between the structure of benzimidazole derivative and its inhibitory activity on NS5B polymerase, holographic quantitative structure-activity relationship (HQSAR) and Topomer comparative molecular field analysis (CoMFA) were used to establish benzimidazole QSAR model of derivative inhibitors.

Results:

The results show that for the Topomer CoMFA model, the cross-validation coefficient q2 value is 0.883, and the non-cross-validation coefficient r2 value is 0.975. The model is reasonable, reliable, and has a good predictive ability. For the HQSAR model, the cross-validated q2 value is 0.922, and the uncross-validated r2 value is 0.971, indicating that the model data fit well and has a high predictive ability. Through the analysis of the contour map and color code diagram, 40 new benzimidazole inhibitor molecules were designed, and all of them have higher activity than template molecules, and the new molecules have significant interaction sites with protein 3SKE.

Conclusion:

The 3D-QSAR model established by Topomer CoMFA and HQSAR has good prediction results and the statistical verification is valid. The newly designed molecules and docking results provide theoretical guidance for the synthesis of new NS5B polymerase inhibitors and for the identification of key residues that the inhibitors bind to NS5B, which helps to better understand their inhibitory mechanism. These findings are helpful for the development of new anti-HCV drugs.

Investigation of angiotensin-I-converting enzyme (ACE) inhibitory tri-peptides: a combination of 3D-QSAR and molecular docking simulations

Angiotensin-I-converting enzyme (ACE) is a key enzyme in the regulation of peripheral blood pressure and electrolyte homeostasis. Therefore, ACE is considered as a promising target for treatment of hypertension. In the present work, in order to investigate the binding interactions between ACE and tri-peptides, three-dimensional quantitative structure-activity relationship (3D-QSAR) models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were developed. Three different alignment methods (template ligand-based, docking-based, and common scaffold-based) were employed to construct reliable 3D-QSAR models. Statistical parameters derived from the QSAR models indicated that the template ligand-based CoMFA (R cv 2 = 0.761, R pred 2 = 0.6257) and CoMSIA (R cv 2 = 0.757, R pred 2 = 0.6969) models were better than the other alignment-based models. In addition, molecular docking studies were carried out to predict the binding modes of the peptides to ACE. The peptide-enzyme interactions were consistent with the derived 3D contour maps. Overall, the insights gained from this study would offer theoretical references for understanding the mechanism of action of tri-peptides when binding to ACE and aid in the design of more potent tri-peptides.