Virtual Screening Results Research Articles

Malaria Box Compounds against Anopheles gambiae (Diptera: Culicidae) Carboxypeptidase B Activity to Block Malaria Transmission.

Carboxypeptidase B (CPB) plays an important role in blood digestion in mosquitos, aiding the release of free amino acids. Anopheles CPB is a target to block malaria transmission because it facilitates Plasmodium invasion of the mosquito midgut. Our study aimed to discover inhibitors of Anopheles CPB to prevent Plasmodium development in the mosquito. The Anopheles gambiae cpb (Agcpb) gene without a signal sequence was cloned into the pET28b expression vector. The recombinant AgCPB protein was expressed in E. coli BL21(DE3) within inclusion bodies after induction with 0.5 mM isopropyl β-D-1-thiogalactopyranoside at 37°C for 4 h. The protein pellet was dissolved in 6 M urea, purified by affinity chromatography, and dialyzed in reaction buffer. The refolded recombinant AgCPB could digest the hippuryl-arginine substrate similarly to that of the commercial porcine pancreas CPB. The 20 top-scoring malaria box compounds from the virtual-screening results were then chosen for an in vitro inhibition assay against AgCPB. Four of the 20 malaria box compounds could inhibit AgCPB activity. The compound MMV007591 was the most potent inhibitor with an IC50 at 0.066 µM. The results indicate that these candidate compounds may be utilized in drug development against mosquito CPB activity to curb malaria transmission.

Investigation of the Potential of Bile Acid Methyl Esters as Inhibitors of Aldo-keto Reductase 1C2: Insight from Molecular Docking, Virtual Screening, Experimental Assays and Molecular Dynamics.

Human aldo-keto reductase 1C isoforms (AKR1C1-C4) catalyze reduction of endogenous and exogenous compounds, including therapeutic drugs, and are associated with chemotherapy resistance. AKR1C2 is involved in metastatic processes and is a target for the treatment of various cancers. Here we used molecular docking to explore the potential of a series of eleven bile acid methyl esters as AKR1C2 inhibitors. Autodock 4.2 ranked 10 of the 11 test compounds above a decoy set generated based on ursodeoxycholic acid, a known AKR1C2 inhibitor, while 5 of these 10 ranked above 94 % of decoys in Autodock Vina. Seven inactives reported in the literature not to inhibit AKR1C2 ranked below the decoy threshold: 5 of these are specific inhibitors of AKR1C3, a related isoform. Using the same parameters, Autodock Vina identified steroidal analogs of AKR1C substrates, bile acids, and AKR1C inhibitors in the top 5 % of a virtual screen of a natural product library. In experimental assays, 6 out of 11 of the tested bile acid methyl esters inhibited >50 % of AKR1C2 activity, while 2 compounds were strong AKR1C3 inhibitors. Potential off-target interactions with the glucocorticoid receptor were measured using a yeast-based fluorescence assay, where results suggest that the methyl ester could interfere with binding. The top ranking compound based on docking and experimental results showed dose-dependent inhibition of AKR1C2 with an IC50 of ∼3.6 μM. Molecular dynamics simulations (20 ns) were used to explore potential interactions between a bile acid methyl ester and residues in the AKR1C2 active site. Our molecular docking results identify AKR1C2 as a target for bile acid methyl esters, which combined with virtual screening results could provide new directions for researchers interested in synthesis of AKR1C inhibitors.

Synthesis, quantum-chemical calculations and virtual screening of the alkaloid cytisine derivatives

The synthesis of some cytisine derivatives was carried out in the work. The article provides the data of quantum-chemical calculation and virtual screening of the alkaloid cytisine derivatives synthesized. At the same time, the reaction centers of the cytisine derivatives molecules were determined. In order to study the reactivity of the derivatives obtained (namely cinnamoylcytisine, lipoylcytisine, and cytisinylisoalantholactone) the quantum-chemical calculations were conducted to determine the energy and charge characteristics of the molecules. The results indicate a sufficient thermodynamic stability of the cinnamoylcytisine and lipoylcytisine molecules. The cytisinylisoalantholactone molecule is not stable according to the results of quantum chemical calculations. The data on the energy values of the frontier molecular orbitals show that, in general, all molecules exhibit electrophilic properties. A bioprediction was implemented using PASS (Prediction of Activity Spectra for Substances) as one of the most efficient and well-known computer program with the aim of detailed study and the probable establishment of the biological activity of the synthesized cytisine derivatives. Based on the results of virtual screening, promising types of alkaloid cytisine derivatives were identified, which are potential sources of original drugs

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure–activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R2pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC50 value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets.

COVID-19 In Silico Drug with Zingiber officinale Natural Product Compound Library Targeting the Mpro Protein

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a worldwide pandemic. Ginger (Zingiber officinale) is a rhizome, which is commonly used for culinary and medicinal purposes. In Indonesia, ginger is taken as traditional medicine by processing it into a drink known as jamu. The present study aimed to assess and evaluate the bioactive compounds in ginger that can be used in drug design for treating COVID-19. The crystal structure of the SARS-CoV-2 main protease (Mpro) was generated from a protein sequence database, i.e., Protein Data Bank, and the bioactive compounds in ginger were derived from the existing compounds library. Mpro is involved in polyprotein synthesis, including viral maturation and nonstructural protein gluing, making it a potential antiviral target. Furthermore, the bioactive compounds in ginger were analyzed using Lipinski’s rule of five to determine their drug-like molecular properties. Moreover, molecular docking analysis was conducted using the Python Prescription 0.8 (Virtual Screening Tool) software, and the interaction between SARS-CoV-2 Mpro and the bioactive compounds in ginger was extensively examined using the PyMOL software. Out all of the 16 bioactive compounds that were docked successfully, 4-gingerol, which has the lowest binding energy against SARS-CoV-2 Mpro, as per the virtual screening results, was proven to have the most potential as a viral inhibitor of SARS-CoV-2

Structure-based virtual screening for new lead compounds targeted Plasmodium α-tubulin

Aim. Search for new dinitroaniline and phosphorothioamide compounds, capable of selective binding with Plasmodium α-tubulin, affecting its mitotic apparatus. Methods. Structural biology methods of computational prediction of protein-ligand interaction: molecular docking, molecular dynamics and pharmacophore analysis. Selection of compounds based on pharmacophore characteristics and virtual screening results. Results. The protocol and required structural conditions for target (α-tubulin of P. falciparum) preparation and correct modeling of the ligand-protein interaction (docking and virtual screening) were developed. The generalized pharmacophore model of ligand-protein interaction and key functional groups of ligands responsible for specific binding were identified. Conclusions. Based on results of virtual screening, 22 commercial compounds were selected. Identified compounds proposed as potential inhibitors of Plasmodium mitotic machinery and the base of new antimalarial drugs.
 Keywords: malaria, Plasmodium, intermolecular interaction, dinitroaniline derived, phosphorothioamidate derived.

EPharmer: An integrated and graphical software for pharmacophore-based virtual screening.

Pharmacophore-based virtual screening (VS) has emerged as an efficient computer-aided drug design technique when appraising multiple ligands with similar structures or targets with unknown crystal structures. Current pharmacophore modeling and analysis software suffers from inadequate integration of mainstream methods and insufficient user-friendly program interface. In this study, we propose a stand-alone, integrated, graphical software for pharmacophore-based VS, termed ePharmer. Both ligand-based and structure-based pharmacophore generation methods were integrated into a compact architecture. Fine-grained modules were carefully organized into the computing, integration, and visualization layers. Graphical design covered the global user interface and specific user operations including editing, evaluation, and task management. Metabolites prediction analysis with the chosen VS result is provided for preselection of wet experiments. Moreover, the underlying computing units largely adopted the preliminary work of our research team. The presented software is currently in client use and will be released for both professional and nonexpert users. Experimental results verified the favorable computing capability, user convenience, and case performance of the proposed software.

Computational Screening of FDA Approved Drugs from ZINC Database for Potential Inhibitors of Zika Virus NS2B/NS3 Protease: A Molecular Docking and Dynamics Simulation Study

Zika virus is a mosquito borne pathogen with a single strand RNA genome. Human infection with this virus is usually asymptomatic, however outbreaks reported in both Pacific region and Latin America have been associated with increase in frequency of microcephaly in newborns and fetuses of infected mothers. Also, the incidence of Guillain-Barré syndrome had also increased among adults with Zika virus infection. Currently, neither vaccine nor antiviral drug has been developed against Zika virus. Structure based virtual screening can be employed, through drug repurposing strategy, to accelerate the identification of potential anti-Zika virus candidates. As such, virtual screening of approved drugs against Zika virus NS2B/NS3 protease can help to recognize new hits capable of hindering viral ability to replicate and evade immune system of the host. In this computational study, we have screened 1615 FDA approved drugs against NS2B/NS3 protease enzyme of Zika virus by using both molecular docking and dynamics simulation. Our virtual screening results indicate that the anti-muscarinic agent Darifenacin and the anti-diarrheal agent Loperamide may have a promising capacity to inhibit Zika virus NS2B/NS3 protease. According to molecular docking and dynamics simulation, these two approved drugs have good binding capacity to NS2B/NS3 as reported by docking energy of binding and MM-PBSA binding energy. In addition, both Darifenacin and Loperamide were able to maintain close proximity to protease crystal throughout simulation period. However, invitro evaluation of these two drugs against Zika virus NS2B/NS3 protease is required to confirm these computational results.

Clove Constituents as new leads for the Design and development of Multi-targeted Anti-Alzheimer activity

Objective: To use virtual screening analysis to screen out the phytoconstituents of Syzygium aromaticum against multiple targets of AD and determine its anti-oxidant and inflammatory inhibitory property. Methods: The compounds listed out from Syzygium aromaticum were subjected to virtual screening based on their drug likeness property and bioactivity scores. The molecular docking simulation such as HEX 8.0, PyRx, MVD along with Auto Dock 4.2 were employed to determine the potential candidate for providing activity against multiple targets of AD. The toxicity estimation was also carried out using TEST software. The potential candidate was further evaluated using DPPH, FRAT, Albumin denaturation and Proteinase inhibition method. Results: Only eight phytoconstituents were selected for virtual screening as they possessed drug likeness property and better bioactivity score for inhibition of kinases, proteases and enzymes. The docking results from various tools predicted that Oleanolic acid can be considered as potential constituents for multi-target action against AD. Toxicity estimation was in range. It also exhibited anti-oxidant and inflammatory inhibition providing its evidence for anti-AD activity. Conclusion: Taken together, these virtual screening results and in-vitro assays suggest that Oleanolic acid has multi target action against AD, which can be proved further with in-vivo studies.

Identification of Multi-Target Anti-AD Chemical Constituents From Traditional Chinese Medicine Formulae by Integrating Virtual Screening and In Vitro Validation.

Alzheimer’s disease (AD) is a neurodegenerative disease that seriously threatens the health of the elderly. At present, no drugs have been proven to cure or delay the progression of the disease. Due to the multifactorial aetiology of this disease, the multi-target-directed ligand (MTDL) approach provides an innovative and promising idea in search for new drugs against AD. In order to find potential multi-target anti-AD drugs from traditional Chinese medicine (TCM) formulae, a compound database derived from anti-AD Chinese herbal formulae was constructed and predicted by the anti-AD multi-target drug prediction platform established in our laboratory. By analyzing the results of virtual screening, 226 chemical constituents with 3 or more potential AD-related targets were collected, from which 16 compounds that were predicted to combat AD through various mechanisms were chosen for biological validation. Several cell models were established to validate the anti-AD effects of these compounds, including KCl, Aβ, okadaic acid (OA), SNP and H2O2 induced SH-SY5Y cell model and LPS induced BV2 microglia model. The experimental results showed that 12 compounds including Nonivamide, Bavachromene and 3,4-Dimethoxycinnamic acid could protect model cells from AD-related damages and showed potential anti-AD activity. Furthermore, the potential targets of Nonivamide were investigated by molecular docking study and analysis with CDOCKER revealed the possible binding mode of Nonivamide with its predicted targets. In summary, 12 potential multi-target anti-AD compounds have been found from anti-AD TCM formulae by comprehensive application of computational prediction, molecular docking method and biological validation, which laid a theoretical and experimental foundation for in-depth study, also providing important information and new research ideas for the discovery of anti-AD compounds from traditional Chinese medicine.

An in-silico analysis reveals 7,7′-bializarin as a promising DNA gyrase B inhibitor on Gram-positive and Gram-negative bacteria

Bacterial diseases are considered by the World Health Organization to be one of the greatest threats to public health worldwide, mainly due to the increasingly frequent resistance to traditional antibiotics. Estimates from the World Bank indicate that the annual global economic impacts of antibiotic resistance will reach US$1.0–3.4 trillion by 2030. With this, the demand for studies aiming at the discovery of new antibiotics or molecules that may play a synergistic role within the spectrum of drug-resistant bacteria is of fundamental importance. In this in silico study, ligands generated from anthraquinones with established antibacterial activity were evaluated as potential inhibitors of the DNA gyrase subunit B of two species of Gram-positive and two Gram-negative bacteria. The main result of molecular docking-based virtual screening reveals several anthraquinones with remarkable binding energies, of which 7,7′-bializarin (ZINC000004783172) exhibited the highest value for all DNA gyrases subunit B studied and formed stable complexes, as evidenced by molecular dynamics simulations. Collectively, the results presented here reveal the potential of this molecule to bind tightly to the active site of DNA gyrases subunit B of Escherichia coli, Salmonella enterica (subtype typhi), Enterococcus faecalis, and Staphylococcus aureus, and therefore represents a promising candidate for further in vitro testing aimed at evaluating its antibacterial effect.

Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors

The inconsistencies in the performance of the virtual screening (VS) process, depending on the used software and structural conformation of the protein, is a challenging issue in the drug design and discovery field. Varying performance, especially in terms of early recognition of the potential hit compounds, negatively affects the whole process and leads to unnecessary waste of the time and resources. Appropriate application of the ensemble docking and consensus-scoring approaches can significantly increase reliability of the VS results. Dihydroorotate dehydrogenase (DHODH) is a key enzyme in the pyrimidine biosynthesis pathway. It is considered as a valuable therapeutic target in cancer, autoimmune and viral diseases. Based on the conducted benchmark study and analysis of the effect of different combinations of the applied methods and approaches, here we suggested a structure-based virtual screening (SBVS) workflow that can be used to increase the reliability of VS.

Stereoselectivity Predictions for the Pd-Catalyzed 1,4-Conjugate Addition Using Quantum-Guided Molecular Mechanics.

The conjugate addition of aryl boronic acids to enones is a powerful synthetic tool to introduce quaternary chiral centers, but the experimentally observed stereoselectivities vary widely, and the identification of suitable substrate-ligand combinations requires significant effort. We describe the development and application of a transition-state force field (TSFF) by the quantum-guided molecular mechanics (Q2MM) method that is validated using an automated screen of 9 ligands, 38 aryl boronic acids, and 22 enones, leading to a MUE of 1.8 kJ/mol and a R2 value of 0.877 over 82 examples. A detailed error analysis identified the structural origin for the deviations in the small group of outliers. The TSFF was then used to predict the stereoselectivity for 27 ligands and 59 enones. The vast majority of the virtual screening results are in line with the expected results. Selected results for 6-substituted pyrox ligands, which were not part of the training set, were followed up by density functional theory and experimental studies.

In silico virtual screening-based study of nutraceuticals predicts the therapeutic potentials of folic acid and its derivatives against COVID-19

The recent outbreak of the novel coronavirus (SARS-CoV-2) in the Wuhan province of China has taken millions of lives worldwide. In this pandemic situation and absence of known drugs and vaccines against novel coronavirus disease (COVID-19), there is an urgent need for the repurposing of the existing drugs against it. So, here we have examined a safe and cheap alternative against this virus by screening hundreds of nutraceuticals compounds against known therapeutic targets of SARS-COV-2 by molecular docking. The virtual screening results were then analyzed for binding energy and interactive residues and compared with some already known hits in the best binding pose. All these analyses of this study strongly predicted the potential of Folic acid and its derivates like Tetrahydrofolic acid and 5-methyl tetrahydrofolic acid against SARS-COV-2. The strong and stable binding affinity of this water-soluble vitamin and its derivatives against the SARS-COV-2, indicating that they could be valuable drugs against the management of this COVID-19 pandemic. This study could serve as the starting point for further investigation of these molecules through in vitro and in vivo assays.Electronic supplementary materialThe online version of this article (10.1007/s13337-020-00643-6) contains supplementary material, which is available to authorized users.

Anti-Alzheimer chemical constituents of Morus macroura Miq.: chemical profiling, in silico and in vitro investigations.

Herein, we investigated both fruits and leaves of Morus macroura Miq. as a potential source of bioactive compounds against Alzheimer's disease (AD). LC-HRMS-assisted chemical profiling of its extracts showed that they are a rich source of diverse phytochemicals. Among the 29 identified compounds in both the fruit and leaf extracts, moracin D, chrysin, resveratrol, and ferulic acid were predicted to pass the human blood-brain barrier (BBB), and hence, reach their therapeutic targets in the brain. Subsequently, these compounds were subjected to a comprehensive pharmacophore-based screening for their protein targets relevant to AD using two independent software programs (i.e. Swiss Target Prediction and PharmMapper). The results of this initial virtual screening were further refined by a number of docking and molecular dynamic simulation experiments to suggest a number of crucial AD-related proteins (e.g. acetylcholine esterase, β-secretase, and monoamine oxidase) as potential targets for these compounds. Finally, in vitro testing was performed to validate the in silico investigation's results, where chrysin, resveratrol, and ferulic acid were found to inhibit the predicted AD-related enzymes with IC50 values comparable with those of the reference inhibitors. Additionally, they were able to inhibit the aggregation of amyloid-beta, one of the hallmarks in AD pathogenesis, and to exhibit considerable antioxidant capacity. Our results highlighted Morus macroura compounds as future anti-Alzheimer chemical leads.

Hierarchical Graph Representation of Pharmacophore Models.

For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic information on the binding interactions in protein-ligand complexes. Chemical feature interaction based pharmacophore models extracted from these simulations, were recently used with consensus scoring approaches to identify potentially active molecules. While this approach is rapid and can be fully automated for virtual screening, additional relevant information from such simulations is still opaque and so far the full potential has not been entirely exploited. To address these aspects, we developed the hierarchical graph representation of pharmacophore models (HGPM). This single graph representation enables an intuitive observation of numerous pharmacophore models from long MD trajectories and further emphasizes their relationship and feature hierarchy. The resulting interactive depiction provides an easy-to-apprehend tool for the selection of sets of pharmacophores as well as visual support for analysis of pharmacophore feature composition and virtual screening results. Furthermore, the representation can be adapted to include information involving interactions between the same protein and multiple different ligands. Herein, we describe the generation, visualization and use of HGPMs generated from MD simulations of two x-ray crystallographic derived structures of the human glucokinase protein in complex with allosteric activators. The results demonstrate that a large number of pharmacophores and their relationships can be visualized in an interactive, efficient manner, unique binding modes identified and a combination of models derived from long MD simulations can be strategically prioritized for VS campaigns.

Exploring the Interaction Mechanism of Desmethyl-broflanilide in Insect GABA Receptors and Screening Potential Antagonists by In Silico Simulations.

Broflanilide, a novel insecticide, is classified as a negative allosteric modulator (NAM) of insect γ-aminobutyric acid (GABA) receptors (GABARs) as desmethyl-broflanilide (DMBF) allosterically inhibits the GABA-induced responses. The G277M mutation of the Drosophila melanogaster GABAR subunit has been reported to abolish the inhibitory activity of DMBF. The binding mode of DMBF in insect GABARs needs to be clarified to understand the underlying mechanism of this mutation and to develop novel, efficient NAMs of insect GABARs. Here, we found that a hydrogen bond formed between DMBF and G277 of the D. melanogaster GABAR model might be the key interaction for the antagonism of DMBF by in silico simulations. The volume increase induced by the G277M mutation blocks the entrance of the binding pocket, making it difficult for DMBF to enter the binding pocket and thereby decreasing its activity. The following virtual screening and bioassay results identified a novel NAM candidate of insect GABARs. Overall, we reported a possible binding mode of DMBF in insect GABARs and proposed the insensitivity mechanism of the G277M mutant GABAR to DMBF using molecular simulations. The identified NAM candidates might provide more alternatives or potentials for the design of GABAR-targeting insecticides.

The Molecular Simulation Study of nNOS Activation Induced by the Interaction Between Its Calmodulin-Binding Domain and SUMO1

Neuronal nitric oxide synthase (nNOS), an enzyme required for learning and memory, catalyzes L-arginine decomposition during nitric oxide production in mammalian neurons. Over-activation of nNOS leads to oxidative/nitrosative stress, which is part of the pathophysiological process of various neuropsychiatric disorders. Previous experimental studies suggest that nNOS is a target for small ubiquitin-like modifier 1 (SUMO1), and that SUMO1-ylation upregulates nNOS catalytic activity in hippocampal neurons. To date, a comprehensive structural model has not been proposed for nNOS SUMO1-ylation. In this study, our aim was to build in silico models to identify the non-bonded interactions between SUMO1 and the calmodulin binding domain (CaMBD) of nNOS. Using molecular docking and molecular dynamics simulation, we found that SUMO1 modification stabilizes the conformation of nNOS CaMBD, and helps maintain a conformation beneficial for nNOS catalysis. Analysis of the polar contacts and hydrogen bonds, and the root mean square derivation results showed that R726 and R727 of CaMBD formed polar contacts or high occupancy hydrogen bonds with SUMO1. Correlation factor analysis and free energy calculations showed that the W716, L734, F740, M745, and F781 residues were also involved in the SUMO1/CaMBD interaction in an orientation-dependent manner. The potential inhibitor binding pocket of SUMO1, aimed at disrupting SUMO1/CaMBD binding, was detected from the virtual screening results. Our in silico studies revealed that interfering with the non-bonded interactions of SUMO1/CaMBD would blocked nNOS SUMO-ylation and subsequent hyperactivation. This work provides novel structural insight into the functional regulation of nNOS by post-translational SUMO1 modification, and provides suggestions for the design of drugs targeting nNOS hyperactivation.

A systematic pharmacological investigation of pharmacologically active ingredients in Toujie Quwen granules for treatment of COVID-19

To explore the pharmacologically active ingredients in Toujie Quwen granules (TJQW) for treatment of coronavirus disease 2019 (COVID-19) in light of systemic pharmacology. We performed database search, literature mining and drug-like index screening to identify the bioactive components in TJQW, the positive drugs for disease treatment and their therapeutic targets. The core disease target was investigated based on the cross-linking interaction of the bioactive components, positive drug and potential disease target, and the target proteins at the key nodes were analyzed by GO and KEGG analyses. Based on the therapeutic targets for COVID-19, virtual screening was conducted to screen the compounds in TJQW and construct the network cross-linking the key bioactive molecules in TJQW, key node targets of the disease, and the related biological pathways. We identified 159 compounds in TJQW and obtained 18 core proteins based on the cross-linking of the bioactive components, positive drugs and disease targets. The key node targets consisted of 22 targets including the latest 4 COVID-19 proteins. Virtual screening results showed that at least 14 compounds could bind with the core disease target proteins. The material basis of TJQW for COVID-19 treatment was explained in multi-pathway, multi-component and multi-target perspectives. In terms of the structural characteristics of the compounds, we screened the top 30 molecules with strong binding with the target proteins, among which flavonoids were the predominant components. This investigation reveals the therapeutic mechanism of TJQW for COVID-19 involving multiple components, targets and pathways from the perspective of key bioactive molecules, disease key node targets and related biological pathways. We screened 30 active precursors from TJQW, which provides reference for the clinical application and further development of TJQW.

Interdimeric Curvature in Tubulin-Tubulin Complexes Delineates the Microtubule-Destabilizing Properties of Plocabulin.

Plocabulin is a novel microtubule (MT) destabilizer agent with potent antineoplastic activity. This compound binds to the maytansine site at the longitudinal interface between tubulin dimers and exerts a hinge-like effect that disrupts normal microtubule assembly. Plocabulin has emerged as a valuable model for the rational design of novel MT destabilizers because of its unique structural and mechanistic features. To make progress on this matter, detailed molecular-level understanding of the ligand-protein interactions responsible for plocabulin association and the conformation and energetic effects arising from plocabulin binding on the longitudinal interaction between tubulin dimers must be provided. In this work, fully atomistic MD simulations and MM/GBSA binding free-energy calculations were used to examine the association of plocabulin to one or two tubulin dimers in longitudinal arrangement. Our results revealed that plocabulin binding is favored by the addition of a second tubulin dimer and that this ligand promotes the assembly of curved tetrameric arrangements with strengthened longitudinal interdimeric interactions compared to ligand-free systems. The applicability of these findings to the rational discovery of novel MT destabilizers was tested using MD and MM/GBSA calculations as filtering tools to narrow the results of virtual screening among an FDA-approved drug database. Our results confirmed that tight-binding ligands do not necessarily exert the expected conformational and energetic effects on longitudinal tubulin-tubulin interactions, which is a matter to consider in future design strategies.