Evaluating anti-coronavirus activity of some phosphoramides and their influencing inhibitory factors using molecular docking, DFT, QSAR, and NCI-RDG studies

Interactions between DNA and the acridine intercalator: A computational study

Cancer is a global public health problem characterized by deviations in the mechanisms that control cell proliferation, resulting in mutations and variations in the structure of DNA. The mechanisms of action of chemotherapeutic drugs are related to their interactions and binding with DNA; consequently, the development of antineoplastic agents that target DNA has extensively focused on use of acridine, a heterocyclic molecule that binds to deoxyribonucleic acid via intercalation, a process that modifies DNA and makes replication impossible. In this context, this study aimed to computationally investigate how acridine intercalators interact with DNA by evaluating the mechanism of interactions, binding, and interaction energies using quantum mechanics calculations. Molecular electrostatic potential (MEP) analysis revealed that acridine has well- distributed negative charges in the center of the molecule, indicative of a dominant electron-rich region. Acridine exhibits well-defined π orbitals (HOMO and LUMO) on the aromatic rings, suggesting that charge transfer occurs within the molecule and may be responsible for the pharmacological activity of the compound. Structural analysis revealed that acridine interacts with DNA mainly through hydrogen bonds between HAcridine… ODNA with bond lengths ranging from 2.370 Å to 3.472 Å. The Binding energy (ΔEBind) showed that acridine interacts with DNA effectively for all complexes and the electronic energy results (E+ZPE) for complexes revealed that the complexes are more stable when the DNA-centered acridine molecule. The Laplacian-analysis topological QTAIM parameter (∇2ρ(r)) and total energy (H(r)) categorized the interactions as being non-covalent in nature. The RGD peak distribution in the NCI analysis reveals the presence of van der Waals interactions, predominantly between the intercalator and DNA. Accordingly, we confirm that acridine/DNA interactions are relevant for understanding how the intercalator acts within nucleic acids.

General structure-activity/selectivity relationship patterns for the inhibitors of the chemokine receptors (CCR1/CCR2/CCR4/CCR5) with application for virtual screening of PubChem database

CC chemokine receptors (CCRs) form a crucial subfamily of G protein-linked receptors that play a distinct role in the onset and progression of various life-threatening diseases. The main aim of this research is to derive general structure-activity relationship (SAR) patterns to describe the selectivity and activity of CCR inhibitors. To this end, a total of 7332 molecules related to the inhibition of CCR1, CCR2, CCR4, and CCR5 were collected from the Binding Database and analyzed using machine learning techniques. A diverse set of 450 molecular descriptors was calculated for each molecule, and the molecules were classified based on their therapeutic targets and activities. The variable importance in the projection (VIP) approach was used to select discriminatory molecular features, and classification models were developed using supervised Kohonen networks (SKN) and counter-propagation artificial neural networks (CPANN). The reliability and predictability of the models were estimated using 10-fold cross-validation, an external validation set, and an applicability domain approach. We were able to identify different sets of molecular descriptors for discriminating between active and inactive molecules and model the selectivity of inhibitors towards different CCRs. The sensitivities of the predictions for the external test set for the SKN models ranged from 0.827-0.873. Finally, the developed classification models were used to screen approximately 2 million random molecules from the PubChem database, with average values for areas under the receiver operating characteristic curves ranging from 0.78-0.96 for SKN models and 0.75-0.89 for CPANN models.Communicated by Ramaswamy H. Sarma.

Structural, Hirshfeld surface and molecular docking studies of a new organotin(IV)-phosphoric triamide complex and an amidophosphoric acid ester proposed as possible SARS-CoV-2 and Monkeypox inhibitors

Phosphoramides and their complexes are attractive compounds due to their significant inhibiting functionality in biological medicine. In this paper, a novel organotin(IV)-phosphoramide complex (Sn(CH₃)₂Cl₂{[(3-Cl)C₆H₄NH]P(O)[NC₄H₈O]₂}₂, 1), derived from a reaction between phosphoric triamide ligand with dimethyltin dichloride, and a new amidophosphoric acid ester ([OCH₂C(CH₃)₂CH₂O]P(O)[N(CH₃)CH₂C₆H₅], 2), prepared from the condensation of a cyclic chlorophosphate reagent with N-methylbenzylamine, are structurally characterized and in silico investigated as potential SARS-CoV-2 and Monkeypox inhibitors by molecular docking simulation. Both compounds crystallize in the monoclinic crystal system with space group P2₁/c. The asymmetric unit of the complex 1 consists of one-half molecule, where Sn^IV is located on an inversion center, while the asymmetric part of 2 consists of one whole molecule. In the complex 1, the tin atom adopts a six-coordinate octahedral geometry with trans groups of (Cl)₂, (CH₃)₂ and (PO)₂ (PO = phosphoric triamide ligand). The molecular architecture consists of the N-H⋯Cl hydrogen bonds stretching as a 1D linear arrangement along the b axis with intermediate R22(12) ring motifs, whereas in the case of 2, the crystal packing is devoid of any classical hydrogen bond interaction. Furthermore, a graphical analysis by using Hirshfeld surface method identifies the most important intermolecular interactions being of the type H⋯Cl/Cl⋯H (for 1) and H⋯O/O⋯H (for 1 and 2), covering the hydrogen bond interactions N-H⋯Cl and C-H⋯O═P, respectively, which turn out to be favoured. A biological molecular docking simulation on the studied compounds provides evidence to suggest a significant inhibitory potential against SARS-COV-2 (6LU7) and Monkeypox (4QWO) especially for 6LU7 with a binding energy around -6 kcal/mol competing with current effective drugs against this virus (with a binding energy around -5 and -7 kcal/mol). It is worth noting that this report is the first case of an inhibitory potential evaluation of phosphoramide compounds on Monkeypox.

Density Functional Theory, Molecular Dynamics and AlteQ Studies Approaches of Baimantuoluoamide A and Baimantuoluoamide B to Identify Potential Inhibitors of Mpro Proteins: a Novel Target for the Treatment of SARS COVID-19

COVID-19 has resulted in epidemi conditions over the world. Despite efforts by scientists from all over the world to develop an effective va ine against this virus, there is presently no recognized cure for COVID-19. The most succeed treatments for various ailments come from natural components found in medicinal plants, which are also rucial for the development of new medications. This study intends to understand the role of the baimantuoluoamide A and baimantuoluoamide B molecules in the treatment of Covid19. Initially, density functional theory (DFT) used to explore their electronic potentials along with the Becke3-Lee-Yang-Parr (B3LYP) 6-311 + G(d, p) basis set. A number of characteristics, including the energy gap, hardness, local softness, electronegativity, and electrophilicity, have also been calculated to discuss the reactivity of mole ules. Using natural bond orbital, the title compound's bioactive nature and stability were investigated. Further, both compounds potential inhibitors with main protease (Mpro) proteins, molecular dynamics simulations and AlteQ investigations also studied.

Supplementary Information

The online version contains supplementary material available at 10.1134/S0021364023600039.

A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.

Investigation of Major Flavonoids from Artemisia argyi as a Potential COVID-19 Drug: Molecular Docking and DFT Calculations

Flavonoids from natural products are well-identified as potential antiviral agents in the treatment of SARS-CoV-2 (COVID-19) infection and related diseases. However, some major species of flavonoids from Chinese traditional folk medicine, such as of Artemisia argyi (A. argyi), have not been evaluated yet. Here, we choose five major flavonoids obtained from A. argyi, namely, Jaceosidin (1), Eupatilin (2), Apigenin (3), Eupafolin (4), and 5,6-Dihydroxy-7,3′,4′-trimethoxyflavone (5), compared to the well-studied Baicalein (6), as potential inhibitors analogs for COVID-19 by computational modeling strategies. The frontier molecular orbitals (FMOs), chemical reactivity descriptors, and electrostatic surface potential (ESP) were performed by density functional theory (DFT) calculations. Additionally, these flavonoids were docked on the main protease (PDB: 6LU7) of SARS-CoV-2 to evaluate the binding affinities. Computational analysis predicted that all of these compounds show a high affinity and might serve as potential inhibitors to SARS-CoV-2, among which compound (5) exhibits the least binding energy (−155.226 kcal/mol). The high binding affinity could be enhanced by increasing the electron repulsion due to the valence shell electron pair repulsion model (VSEPR). Consequently, the major flavonoids in Artemisia argyi have a significant ability to reduce the deterioration of COVID-19 in the terms of DFT calculations and molecular docking.