Docking Paradigm in Drug Design

Mechanism of AGT-Mediated Repair of dG-dC Cross-Links in the Drug Resistance to Chloroethylnitrosoureas: Molecular Docking, MD Simulation, and ONIOM (QM/MM) Investigation

Chloroethylnitrosoureas (CENUs) are important chemotherapies applied in the treatment of cancer. They exert anticancer activity by inducing DNA interstrand cross-links (ICLs) via the formation of two O6-alkylguanine intermediates, O6-chloroethylguanine (O6-ClEtG) and N1,O6-ethanoguanine (N1,O6-EtG). However, O6-alkylguanine-DNA alkyltransferase (AGT), a DNA-repair enzyme, can restore the O6-alkylguanine damages and thereby obstruct the formation of ICLs (dG-dC cross-link). In this study, the inhibitory mechanism of ICL formation was investigated to elucidate the drug resistance of CENUs mediated by AGT in detail. Based on the structures of the substrate-enzyme complexes obtained from docking and MD simulations, two ONIOM (QM/MM) models with different sizes of the QM region were constructed. The model with a larger QM region, which included the substrate (O6-ClEtG or N1,O6-EtG), a water molecule, and five residues (Tyr114, Cys145, His146, Lys165, and Glu172) in the active pocket of AGT, accurately described the repairing reaction and generated the results coinciding with the experimental outcomes. The repair process consists of two sequential steps: hydrogen transfer to form a thiolate anion on Cys145 and alkyl transfer from the O6 site of guanine (the rate-limiting step). The repair of N1,O6-EtG was more favorable than that of O6-ClEtG from both kinetics and thermodynamics aspects. Moreover, the comparison of the repairing process with the formation of dG-dC cross-link and the inhibition of AGT by O6-benzylguanine (O6-BG) showed that the presence of AGT could effectively interrupt the formation of ICLs leading to drug resistance, and the inhibition of AGT by O6-BG that was energetically more favorable than the repair of O6-ClEtG could not prevent the repair of N1,O6-EtG. Therefore, it is necessary to completely eliminate AGT activity before CENUs medication to enhance the chemotherapeutic effectiveness. This work provides reasonable explanations for the supposed mechanism of AGT-mediated drug resistance of CENUs and will assist in the development of novel CENU chemotherapies and their medication strategies.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Pharmacophore modeling, molecular docking, and molecular dynamics studies to identify new 5-HT2AR antagonists with the potential for design of new atypical antipsychotics

Some important atypical antipsychotic drugs target the serotonergic receptor 2A (5-HT2AR). Currently, new therapeutic strategies are needed to offer faster onset of action with fewer side effects and, therefore, greater efficacy in a substantial proportion of patients with neuropsychological disorders such as Autism and Parkinson. The main objective of this work was to use SBDD methods to identify new hit compounds potentially useful as precursors of novel and selective 5-HT2AR antagonists. A structure-based pharmacophore screening study based on a selective antagonist was carried out in ten databases. The set obtained was refined using molecular docking, and the five most promising compounds were subjected to molecular dynamics simulations. The most stable and promising hit occupied a side pocket present in the 5-HT2AR, a site that can be explored to obtain selective ligands. Simulations against 5-HT2CR and D2R showed that the best hit could not form stable complexes with these targets, strengthening the hypothesis that the hit presents selective binding by the receptor of interest. The selected hits showed some predicted toxicity risk or violated some drug-likeness property. However, it can be concluded that the identified hits are the most promising for performing in vitro assays. Once the presence of activity is confirmed, they could become precursors of optimized and selective antagonists of 5-HT2AR. An SBDD study was carried out to identify new selective 5-HT2AR ligands potentially useful for designing selective atypical antipsychotics.

Inhibition of SARS-CoV2 viral infection with natural antiviral plants constituents: An in-silico approach

Background and Objective

In 2019, a novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) was declared pandemic. Advancement in computational technology has provided rapid and cost-effective techniques to test the efficacy of newer therapeutic agents. This study evaluated some of the potent phytochemicals obtained from AYUSH (Ayurveda, Yoga, Naturopathy, Unani, Siddha, Sowa-Rigpa, and Homeopathy)-listed medicinal plants against SARS-CoV-2 proteins using computational techniques.

Materials and methods

The potential SARS-CoV-2 protein targets were utilized to study the ligand-protein binding characteristics. The bioactive agents were obtained from ashwagandha, liquorice, amla, neem, tinospora, pepper, and stevia. Ivermectin was utilized as a reference agent to compare its efficacy with phytochemicals.

Results

The computational analysis suggested that all the bioactive components from the selected plants possessed negative docking scores (ranging from -6.24 to -10.53). The phytoconstituents were well absorbed, distributed in the body except for the CNS, metabolized by liver enzymes, well cleared from the body, and well tolerated. The data suggest that AYUSH-recommended plants demonstrated therapeutic efficacy against SARS CoV-2 virus infection with significantly reduced toxicity.

Conclusion

The phytoconstituents were found to hinder the early stages of infection, such as absorption and penetration, while ivermectin prevented the passage of genetic material from the cytoplasm to the nucleus. Additional research involving living tissues and clinical trials are suggested to corroborate the computational findings.

Docking-based generative approaches in the search for new drug candidates

Despite the popularity of virtual screening (VS) of existing compound libraries, the search for new potential drug candidates also takes advantage of generative protocols, where new compound suggestions are enumerated using various algorithms. To increase the activity potency of generative approaches, they have recently been coupled with molecular docking, a leading methodology of structure-based drug design (SBDD). In this review, we summarize progress since docking-based generative models emerged. We propose a new taxonomy for these methods and discuss their importance for the field of computer-aided drug design (CADD). In addition, we discuss the most promising directions for the further development of generative protocols coupled with docking.

New Chemicals Suppressing SARS-CoV-2 Replication in Cell Culture

Candidates to being inhibitors of the main protease (Mpro) of SARS-CoV-2 were selected from the database of Voronezh State University using molecular modeling. The database contained approximately 19,000 compounds represented by more than 41,000 ligand conformers. These ligands were docked into Mpro using the SOL docking program. For one thousand ligands with best values of the SOL score, the protein–ligand binding enthalpy was calculated by the PM7 quantum-chemical method with the COSMO solvent model. Using the SOL score and the calculated protein–ligand binding enthalpies, eighteen compounds were selected for the experiments. Several of these inhibitors suppressed the replication of the coronavirus in cell culture, and we used the best three among them in the search for chemical analogs. Selection among analogs using the same procedure followed by experiments led to identification of seven inhibitors of the SARS-CoV-2 replication in cell culture with EC50 values at the micromolar level. The identified inhibitors belong to three chemical classes. The three inhibitors, 4,4-dimethyldithioquinoline derivatives, inhibit SARS-CoV-2 replication in Vero E6 cell culture just as effectively as the best published non-covalent inhibitors, and show low cytotoxicity. These results open up a possibility to develop antiviral drugs against the SARS-CoV-2 coronavirus.

fastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation

Predicting the native or near-native binding pose of a small molecule within a protein binding pocket is an extremely important task in structure-based drug design, especially in the hit-to-lead and lead optimization phases. In this study, fastDRH, a free and open accessed web server, was developed to predict and analyze protein-ligand complex structures. In fastDRH server, AutoDock Vina and AutoDock-GPU docking engines, structure-truncated MM/PB(GB)SA free energy calculation procedures and multiple poses based per-residue energy decomposition analysis were well integrated into a user-friendly and multifunctional online platform. Benefit from the modular architecture, users can flexibly use one or more of three features, including molecular docking, docking pose rescoring and hotspot residue prediction, to obtain the key information clearly based on a result analysis panel supported by 3Dmol.js and Apache ECharts. In terms of protein-ligand binding mode prediction, the integrated structure-truncated MM/PB(GB)SA rescoring procedures exhibit a success rate of >80% in benchmark, which is much better than the AutoDock Vina (~70%). For hotspot residue identification, our multiple poses based per-residue energy decomposition analysis strategy is a more reliable solution than the one using only a single pose, and the performance of our solution has been experimentally validated in several drug discovery projects. To summarize, the fastDRH server is a useful tool for predicting the ligand binding mode and the hotspot residue of protein for ligand binding. The fastDRH server is accessible free of charge at http://cadd.zju.edu.cn/fastdrh/.

Quantum-Chemical Quasi-Docking for Molecular Dynamics Calculations

The quantum quasi-docking procedure is used to compare the docking accuracies of two quantum-chemical semiempirical methods, namely, PM6-D3H4X and PM7. Quantum quasi-docking is an approximation to quantum docking. In quantum docking, it is necessary to search directly for the global minimum of the energy of the protein-ligand complex calculated by the quantum-chemical method. In quantum quasi-docking, firstly, we look for a wide spectrum of low-energy minima, calculated using the MMFF94 force field, and secondly, we recalculate the energies of all these minima using the quantum-chemical method, and among these recalculated energies we determine the lowest energy and the corresponding ligand position. Both PM6-D3H4X and PM7 are novel methods that describe well-dispersion interactions, hydrogen and halogen bonds. The PM6-D3H4X and PM7 methods are used with the COSMO implicit solvent model as it is implemented in the MOPAC program. The comparison is made for 25 high quality protein-ligand complexes. Firstly, the docking positioning accuracies have been compared, and we demonstrated that PM7+COSMO provides better positioning accuracy than PM6-D3H4X. Secondly, we found that PM7+COSMO demonstrates a much higher correlation between the calculated and measured protein-ligand binding enthalpies than PM6-D3H4X. For future quantum docking PM7+COSMO is preferable, but the COSMO model must be improved.

[Development of antiviral drugs based on inhibitors of the SARS-COV-2 main protease]

Docking and quantum-chemical methods have been used for screening of drug-like compounds from the own database of the Voronezh State University to find inhibitors the SARS-CoV-2 main protease, an important enzyme of the coronavirus responsible for the COVID-19 pandemic. Using the SOL program more than 42000 3D molecular structures were docked into the active site of the main protease, and more than 1000 ligands with most negative values of the SOL score were selected for further processing. For all these top ligands, the protein-ligand binding enthalpy has been calculated using the PM7 semiempirical quantum-chemical method with the COSMO implicit solvent model. 20 ligands with the most negative SOL scores and the most negative binding enthalpies have been selected for further experimental testing. The latter has been made by measurements of the inhibitory activity against the main protease and suppression of SARS-CoV-2 replication in a cell culture. The inhibitory activity \of the compounds was determined using a synthetic fluorescently labeled peptide substrate including the proteolysis site of the main protease. The antiviral activity was tested against SARS-CoV-2 virus in the Vero cell culture. Eight compounds showed inhibitory activity against the main protease of SARS-CoV-2 in the submicromolar and micromolar ranges of the IC50 values. Three compounds suppressed coronavirus replication in the cell culture at the micromolar range of EC50 values and had low cytotoxicity. The found chemically diverse inhibitors can be used for optimization in order to obtain a leader compound, the basis of new direct-acting antiviral drugs against the SARS-CoV-2 coronavirus.

Supercomputing, Docking and Quantum Mechanics in Quest for Inhibitors of Papain-like Protease of SARS-CoV-2

Lomonosov-2 supercomputer is used to search for new organic compounds that can suppress the replication of the SARS-CoV-2 coronavirus. The latter is responsible for the COVID-19 pandemic. Docking and a quantum-chemical semiempirical atomistic modeling method are used to find inhibitors of the SARS-CoV-2 papain-like protease, which is one of the key coronavirus enzymes responsible for its replication. The atomistic model of the papain-like protease of this coronavirus is based on the high-resolution structure deposited in the Protein Data Bank. The SOL docking program has been used for virtual screening of more than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$40000$$\end{document} low molecular weight molecules (ligands). Ligands with the highest protein-ligand binding energy, selected using the docking results, were subjected to quantum-chemical calculations. The latters are performed by the PM7 semiempirical method with the COSMO implicit solvent model using the MOPAC program. The enthalpy of protein-ligand binding is calculated for the best position of the ligand in the protein. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$19$$\end{document} ligands were selected for experimental in vitro testing as candidates for papain-like protease inhibitors base on docking and quantum-chemical results. In case of experimental confirmation, these compounds may become the basis for direct-acting antiviral drugs for the SARS-CoV-2 coronavirus.