Docking-based virtual screening of Brazilian natural compounds using the OOMT as the pharmacological target database

Exploring the potential mechanisms of Danshen against COVID-19 via network pharmacology analysis and molecular docking

Danshen, a prominent herb in traditional Chinese medicine (TCM), is known for its potential to enhance physiological functions such as blood circulation, immune response, and resolve blood stasis. Despite the effectiveness of COVID-19 vaccination efforts, some individuals still face severe complications post-infection, including pulmonary fibrosis, myocarditis arrhythmias and stroke. This study employs a network pharmacology and molecular docking approach to investigate the potential mechanisms underlying the therapeutic effects of candidate components and targets from Danshen in the treatment of complications in COVID-19. Candidate components and targets from Danshen were extracted from the TCMSP Database, while COVID-19-related targets were obtained from Genecards. Venn diagram analysis identified common targets. A Protein-Protein interaction (PPI) network and gene enrichment analysis elucidated potential therapeutic mechanisms. Molecular docking evaluated interactions between core targets and candidate components, followed by molecular dynamics simulations to assess stability. We identified 59 potential candidate components and 123 targets in Danshen for COVID-19 treatment. PPI analysis revealed 12 core targets, and gene enrichment analysis highlighted modulated pathways. Molecular docking showed favorable interactions, with molecular dynamics simulations indicating high stability of key complexes. Receiver operating characteristic (ROC) curves validated the docking protocol. Our study unveils candidate compounds, core targets, and molecular mechanisms of Danshen in COVID-19 treatment. These findings provide a scientific foundation for further research and potential development of therapeutic drugs.

Screening of potential phytomolecules against MurG as drug target in nosocomial pathogen Pseudomonas aeruginosa: perceptions from computational campaign

The nosocomial infection outbreak caused by Pseudomonas aeruginosa remains a public health concern. Multi-drug resistant (MDR) strains of P. aeruginosa are rapidly spreading leading to a huge mortality rate because of the unavailability of promising antimicrobials. MurG glycotransferase [UDP-N-acetylglucosamine-N-acetylmuramyl (pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase] is located at the plasma membrane and plays a key role in murein (peptidoglycan) biosynthesis in bacteria. Since MurG is required for bacterial cell wall synthesis and is non-homologous to Homo sapiens; it can be a potential target for the antagonist to treat P. aeruginosa infection. The discovery of high-resolution crystal structure of P. aeruginosa MurG offers an opportunity for the computational identification of its prospective inhibitors. Therefore, in the present study, the crystal structure of MurG (PDB ID: 3S2U) from P. aeruginosa was selected, and computational docking analyses were performed to search for functional inhibitors of MurG. IMPPAT (Indian medicinal plants, phytochemicals and therapeutic) phytomolecule database was screened by computational methods with MurG catalytic site. Docking results identified Theobromine (-8.881 kcal/mol), demethoxycurcumin (-8.850 kcal/mol), 2-alpha-hydroxycostic acid (-8.791 kcal/mol), aurantiamide (-8.779 kcal/mol) and petasiphenol (-8.685 kcal/mol) as a potential inhibitor of the MurG activity. Further, theobromine and demethoxycurcumin were subjected to MDS (molecular dynamics simulation) and free energy (MM/GBSA) analysis to comprehend the physiological state and structural stability of MurG-phytomolecules complexes. The outcomes suggested that these two phytomolecules could act as most favorable natural hit compounds for impeding the enzymatic action of MurG in P. aeruginosa, and thus it needs further validation by both in vitro and in vivo analysis. HIGHLIGHTSThe top phytomolecules such as theobromine, demethoxycurcumin, 2-alpha-hydroxycostic acid, aurantiamide and petasiphenol displayed promising binding with MurG catalytic domain.MurG complexed with theobromine and demethoxycurcumin showed the best interaction and stable by MD simulation at 100 ns.The outcome of MurG binding phytomolecules has expanded the possibility of hit phytomolecules validation.Communicated by Ramaswamy H. Sarma.

Novel B, C-ring truncated deguelin derivatives reveals as potential inhibitors of cyclin D1 and cyclin E using molecular docking and molecular dynamic simulation

The overexpression of cyclin D1 and cyclin E due to their oncogenic potential and amplification has been associated with a higher mortality rate in many cancers. The deguelin is a natural compound, has shown promising anti-cancer activity by directly binding cyclin D1 and cyclin E and thus suppressing its function. The C7a atomic position of deguelin structure contains a proton that generates stabilized radical, as a result, decomposed deguelin reduces its structural stability and significantly decreases its biological activity. To design deguelin derivatives with the reduced potential side effect, series of B, C-ring truncated derivatives were investigated as cyclin D1 and cyclin E inhibitors. R-group-based enumeration was implemented in the deguelin scaffold using the R-group enumeration module of Schrödinger. Drug-Like filters like, REOS and PAINs series were applied to the enumerated compound library to remove compounds containing reactive functional groups. Further, screened compounds were docked within the ligand-binding cavity of cyclin D1 and cyclin E crystal structure, using Glide SP and XP protocol to obtain docking poses. Enrichment calculations were done using SchrÖdinger software, with 1000 decoy compounds (from DUD.E database) and 60 compounds (XP best poses) along with deguelin, to validate the docking protocol. The receiver operating characteristic (ROC) curve indicates R² = 0.94 for cyclin D1 and R² = 0.79 for cyclin E, suggesting that the docking protocol is valid. Besides, we explored molecular dynamics simulation to probe the binding stability of deguelin and its derivatives within the binding cavity of cyclin D1 and cyclin E structures which are associated with the cyclin D1 and cyclin E inhibitory mechanism.

Synthesis, docking, machine learning and antiproliferative activity of the 6-ferrocene/heterocycle-2-aminopyrimidine and 5-ferrocene-1H-Pyrazole derivatives obtained by microwave-assisted Atwal reaction as potential anticancer agents

A simple and fast methodology under microwave irradiation for the synthesis of 2-aminopyrimidine and pyrazole derivatives using Atwal reaction is reported. After the optimization of the reaction conditions, eight 2-aminolpyrimidines containing ferrocene and heterocycles and three ferrocene pyrazoles were synthesized from the respective chalcones in good yields. Eight compounds had their structure determined by X-ray diffraction. The molecular hybrid 6a-h and 9a-c were tested on four cancer cell lines - HCT116, PC3, HL60 and SNB19 - where four pyrimidine 6a, 6f-h and one pyrazole 9c derivatives show promising antiproliferative activity. In addition, docking simulation and machine learning methods were carried out to explain the biological activity achieved by the synthetized compounds.

Computational insights of phytochemical-driven disruption of RNA-dependent RNA polymerase-mediated replication of coronavirus: a strategic treatment plan against coronavirus disease 2019

The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised global health concerns. RNA-dependent RNA polymerase (RdRp) is the prime component of viral replication/proliferation machinery and is considered to be a potential drug target against SARS-CoV-2. The present study investigated the anti-RdRp activity of phytochemicals against SARS-CoV-2 infection. Virtual ligand screening was carried out to determine the potent compounds against RdRp. Molecular docking and an MD Simulation study were employed to evaluate the spatial affinity of selected phytochemicals for the active sites of RdRp. Structural stability of target compounds was determined using root mean square deviation computational analysis and drug-like abilities were investigated using ADMET. Bond distances between ligand and receptor were marked to predict the strength of interaction. Aloe, azadirachtin, columbin, cirsilineol, nimbiol, nimbocinol and sage exhibited the highest binding affinities and interacted with active sites of RdRp, surpassing the ability of chloroquine, lamivudine, favipiravir and remdesivir to target the same. All the natural metabolites exhibited stable conformation during MD Simulation of 101 ns at 310 K. Kinetic, potential and electrostatic energy were observed to be least in the case of natural metabolites in comparison with synthetic analogues. Deviations and fluctuations were observed to be structurally least in target phytochemicals. Physiochemical and biological properties of these compounds further validated their drug-like properties. Non-bonded distance was found to be short enough to form hydrogen bonding or hydrophobic interactions, which revealed that these target compounds can strongly bind with RdRp. The study found potential phytochemicals to disrupt the replication domain of SARS-CoV-2 by hindering RdRp. We therefore anticipate that the current findings could be considered as valuable for the development of an efficient preventive/therapeutic expedient against COVID-19.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Molecular Architect: A User-Friendly Workflow for Virtual Screening

Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.

South African Abietane Diterpenoids and Their Analogs as Potential Antimalarials: Novel Insights from Hybrid Computational Approaches

The hemoglobin degradation process in Plasmodium parasites is vital for nutrient acquisition required for their growth and proliferation. In P. falciparum, falcipains (FP-2 and FP-3) are the major hemoglobinases, and remain attractive antimalarial drug targets. Other Plasmodium species also possess highly homologous proteins to FP-2 and FP-3. Although several inhibitors have been designed against these proteins, none has been commercialized due to associated toxicity on human cathepsins (Cat-K, Cat-L and Cat-S). Despite the two enzyme groups sharing a common structural fold and catalytic mechanism, distinct active site variations have been identified, and can be exploited for drug development. Here, we utilize in silico approaches to screen 628 compounds from the South African natural sources to identify potential hits that can selectively inhibit the plasmodial proteases. Using docking studies, seven abietane diterpenoids, binding strongly to the plasmodial proteases, and three additional analogs from PubChem were identified. Important residues involved in ligand stabilization were identified for all potential hits through binding pose analysis and their energetic contribution determined by binding free energy calculations. The identified compounds present important scaffolds that could be further developed as plasmodial protease inhibitors. Previous laboratory assays showed the effect of the seven diterpenoids as antimalarials. Here, for the first time, we demonstrate that their possible mechanism of action could be by interacting with falcipains and their plasmodial homologs. Dynamic residue network (DRN) analysis on the plasmodial proteases identified functionally important residues, including a region with high betweenness centrality, which had previously been proposed as a potential allosteric site in FP-2.

MoleGear: A Java-Based Platform for Evolutionary De Novo Molecular Design

A Java-based platform, MoleGear, is developed for de novo molecular design based on the chemistry development kit (CDK) and other Java packages. MoleGear uses evolutionary algorithm (EA) to explore chemical space, and a suite of fragment-based operators of growing, crossover, and mutation for assembling novel molecules that can be scored by prediction of binding free energy or a weighted-sum multi-objective fitness function. The EA can be conducted in parallel over multiple nodes to support large-scale molecular optimizations. Some complementary utilities such as fragment library design, chemical space analysis, and graphical user interface are also integrated into MoleGear. The candidate molecules as inhibitors for the human immunodeficiency virus 1 (HIV-1) protease were designed by MoleGear, which validates the potential capability for de novo molecular design.

Structure investigation, enrichment analysis and structure-based repurposing of FDA-approved drugs as inhibitors of BET-BRD4

We report herein detailed structural insights into the ligand recognition modes guiding bromodomain selectivity, enrichment analysis and docking-based database screening for the identification of the FDA-approved drugs that have potential to be the human BRD4 inhibitors. Analysis of multiple X-ray structures prevailed that the lysine-recognition sites are highly conserved, and apparently, the dynamic ZA loop guides the specific ligand-recognition. The protein-ligand interaction profiling revealed that both BRD2 and BRD4 shared hydrophobic interaction of bound ligands with PRO-98/PRO-82, PHE-99/PHE-83, LEU-108/LEU-92 and direct H-bonding with ASN-156/ASN-140 (BRD2/BRD4), while on the other hand the water-mediated H-bonding of bound ligands with PRO-82, GLN-85, PRO-86, VAL-87, ASP-88, LEU-92, TYR-97 and MET-132, and aromatic π-π stacking with TRP-81 prevailed as unique interaction in BRD4, and were not observed in BRD2. Subsequently, through ROC curve analysis, the best enrichment was found with PDB-ID 4QZS of BRD4 structures. Finally, through docking-based database screening study, we found that several drugs have better binding affinity than the control candidate lead (+)-JQ1 (Binding affinity = -7.9 kcal/mol), a well-known BRD4 inhibitor. Among the top-ranked drugs, azelastine, a selective histamine H1 receptor antagonist, showed the best binding affinity of -9.3 kcal/mol and showed interactions with several key residues of the acetyl lysine binding pocket. Azelastine may serve as a promising template for further medicinal chemistry. These insights may serve as basis for structure-based drug design, drug repurposing and the discovery of novel BRD4 inhibitors. Communicated by Ramaswamy H. Sarma.

Correlation between Virtual Screening Performance and Binding Site Descriptors of Protein Targets

Rescoring is a simple approach that theoretically could improve the original docking results. In this study AutoDock Vina was used as a docked engine and three other scoring functions besides the original scoring function, Vina, as well as their combinations as consensus scoring functions were employed to explore the effect of rescoring on virtual screenings that had been done on diverse targets. Rescoring by DrugScore produces the most number of cases with significant changes in screening power. Thus, the DrugScore results were used to build a simple model based on two binding site descriptors that could predict possible improvement by DrugScore rescoring. Furthermore, generally the screening power of all rescoring approach as well as original AutoDock Vina docking results correlated with the Maximum Theoretical Shape Complementarity (MTSC) and Maximum Distance from Center of Mass and all Alpha spheres (MDCMA). Therefore, it was suggested that, with a more complete set of binding site descriptors, it could be possible to find robust relationship between binding site descriptors and response to certain molecular docking programs and scoring functions. The results could be helpful for future researches aiming to do a virtual screening using AutoDock Vina and/or rescoring using DrugScore.

Synthesis, SAR, and Docking Studies Disclose 2-Arylfuran-1,4-naphthoquinones as In Vitro Antiplasmodial Hits

A total of 28 lapachol-related naphthoquinones with four different scaffolds were synthesized and spectroscopically characterized. In vitro antiplasmodial activity was assayed against the chloroquine-resistant Plasmodium falciparum W2 strain by the parasite lactate dehydrogenase (pLDH) method. Cytotoxicity against Hep G2A16 cell was determined by the MTT assay. All compounds disclosed higher in vitro antiplasmodial activity than lapachol. Ortho- and para-naphthoquinones with a furan ring fused to the quinonoid moiety were more potent than 2-hydroxy-3-(1′-alkenyl)-1,4-naphthoquinones, while ortho-furanonaphthoquinones were more cytotoxic. Molecular docking to Plasmodium targets Pfcyt bc1 complex and PfDHOD enzyme showed that five out of the 28 naphthoquinones disclosed favorable binding energies. Furanonaphthoquinones endowed with an aryl moiety linked to the furan ring are highlighted as new in vitro antiplasmodial lead compounds and warrant further investigation.

Octopus: a platform for the virtual high-throughput screening of a pool of compounds against a set of molecular targets

Octopus is an automated workflow management tool that is scalable for virtual high-throughput screening (vHTS). It integrates MOPAC2016, MGLTools, PyMOL, and AutoDock Vina. In contrast to other platforms, Octopus can perform docking simulations of an unlimited number of compounds into a set of molecular targets. After generating the ligands in a drawing package in the Protein Data Bank (PDB) format, Octopus can carry out geometry refinement using the semi-empirical method PM7 implemented in MOPAC2016. Docking simulations can be performed using AutoDock Vina and can utilize the Our Own Molecular Targets (OOMT) databank. Finally, the proposed software compiles the best binding energies into a standard table. Here, we describe two successful case studies that were verified by biological assay. In the first case study, the vHTS process was carried out for 22 (phenylamino)urea derivatives. The vHTS process identified a metalloprotease with the PDB code 1GKC as a molecular target for derivative LE&007. In a biological assay, compound LE&007 was found to inhibit 80% of the activity of this enzyme. In the second case study, compound Tx001 was submitted to the Octopus routine, and the results suggested that Plasmodium falciparum ATP6 (PfATP6) as a molecular target for this compound. Following an antimalarial assay, Tx001 was found to have an inhibitory concentration (IC₅₀) of 8.2 μM against PfATP6. These successful examples illustrate the utility of this software for finding appropriate molecular targets for compounds. Hits can then be identified and optimized as new antineoplastic and antimalarial drugs. Finally, Octopus has a friendly Linux-based user interface, and is available at www.drugdiscovery.com.br . Graphical Abstract Octopus: A platform for inverse virtual screening (IVS) to search new molecular targets for drugs.