Hierarchical Virtual Screening Research Articles

A Systematic Hierarchical Virtual Screening Model for RhlR Inhibitors Based on PCA, Pharmacophore, Docking, and Molecular Dynamics.

RhlR plays a key role in the quorum sensing of Pseudomonas aeruginosa. The current structure-activity relationship (SAR) studies of RhlR inhibitors mainly focus on elucidating the functional groups. Based on a systematic review of previous research on RhlR inhibitors, this study aims to establish a systematic, hierarchical screening model for RhlR inhibitors. We initially established a database and utilized principal component analysis (PCA) to categorize the inhibitors into two classes. Based on the training set, pharmacophore models were established to elucidate the structural characteristics of ligands. Subsequently, molecular docking, molecular dynamics simulations, and the calculation of binding free energy and strain energy were performed to validate the crucial interactions between ligands and receptors. Then, the screening criteria for RhlR inhibitors were established hierarchically based on ligand structure characteristics, ligand-receptor interaction, and receptor affinity. Test sets were finally employed to validate the hierarchical virtual screening model by comparing it with the current SAR studies of RhlR inhibitors. The hierarchical screening model was confirmed to possess higher accuracy and a true positive rate, which holds promise for subsequent screening and the discovery of active RhlR inhibitors.

Towards development of new antimalarial compounds through in silico and in vitro assays

Malaria is one of most widespread infectious disease in world. The antimalarial therapy presents a series of limitations, such as toxicity and the emergence of resistance, which makes the search for new drugs urgent. Thus, it becomes necessary to explore essential and exclusive therapeutic targets of the parasite to achieve selective inhibition. Enoyl-ACP reductase is an enzyme of the type II fatty acid biosynthetic pathway and is responsible for the rate-limiting step in the fatty acid elongation cycle. In this work, we use hierarchical virtual screening and drug repositioning strategies to prioritize compounds for phenotypic assays and molecular dynamics studies. The molecules were tested against chloroquine-resistant W2 strain of Plasmodium falciparum (EC50 between 330.05 and 13.92 µM). Nitrofurantoin was the best antimalarial activity at low micromolar range (EC50 = 13.92 µM). However, a hit compound against malaria must have a biological activity value below 1 µM. A large number of molecules present problems with permeability in biological membranes and reaching an effective concentration in their target's microenvironment. Nitrofurantoin derivatives with inclusions of groups which confer increased lipid solubility (methyl groups, halogens and substituted and unsubstituted aromatic rings) have been proposed. These derivatives were pulled through the lipid bilayer in molecular dynamics simulations. Molecules 14, 18 and 21 presented lower free energy values than nitrofurantoin when crossing the lipid bilayer.

Discovery of Potential Neonicotinoid Insecticides by an Artificial Intelligence Generative Model and Structure-Based Virtual Screening.

The identification of neonicotinoid insecticides bearing novel scaffolds is of great importance for pesticide discovery. Here, artificial intelligence-based tools and virtual screening strategy were integrated to discover potential leads of neonicotinoid insecticides. A deep generative model was successfully constructed using a recurrent neural network combined with transfer learning. The model evaluation showed that the pretrained model could accurately grasp the SMILES grammar of drug-like molecules and generate potential neonicotinoid compounds after transfer learning. The generated molecules were evaluated by hierarchical virtual screening, hits were subjected to a similarity search, and the most similar structures were purchased for the bioassay. Compounds A2 and A5 displayed 52.5 and 50.3% mortality rates against Aphis craccivora at 100 mg/L, respectively. The docking study indicated that these two compounds have similar binding modes to neonicotinoids, which were verified by further molecular dynamics simulations.

Hierarchical Virtual Screening of SARS-CoV-2 Main Protease Potential Inhibitors: Similarity Search, Pharmacophore Modeling, and Molecular Docking Study

Background: The outbreak of COVID-19 caused by severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) resulted in a widespread pandemic. Various approaches involved the repositioning of antiviral remedies and other medications. Several therapies, including oral antiviral treatments, represent some approaches to adapting to the long existence of the COVID-19 pandemic. In silico studies provide valuable insights throughout drug discovery and development in compliance with global efforts to overcome the pandemic. The main protease is an essential target in the viral cycle. Computer-aided drug design accelerates the identification of potential treatments, including oral therapy. Aim: This work aims to identify potential SARS-CoV-2 main protease inhibitors using different aspects of in silico approaches. Method: In this work, we conducted a hierarchical virtual screening of SARS-CoV-2 main protease inhibitors. A similarity search was conducted to screen molecules similar to the inhibitor PF-07321332. Concurrently, structure-based pharmacophores, besides ligand-based pharmacophores, were derived. A drug-likeness filter filtered the compounds retrieved from similarity search and pharmacophore modeling before being subjected to molecular docking. The candidate molecules that showed higher affinity to the main protease than the reference inhibitor were further filtered by absorption, distribution, metabolism, and excretion (ADME) parameters. Result: According to binding affinity and ADME analysis, four molecules (CHEMBL218022, PubChem163362029, PubChem166149100, and PubChem 162396459) were prioritized as promising hits. The compounds above were not reported before; no previous experimental studies and bioactive assays are available. Conclusion: Our time-saving approach represents a strategy for discovering novel SARS-CoV- 2 main protease inhibitors. The ultimate hits may be nominated as leads in discovering novel SARS-CoV-2 main protease inhibitors.

Discovery of Potential Prolyl-tRNA Synthetase Allosteric Inhibitor Through Virtual Screening and In Vitro Assay against Plasmodium falciparum

Objectives: This study aimed to identify novel antimalarial compounds based on allosteric inhibitor of prolyl-tRNA synthetase using hierarchical virtual screening. Materials and Methods: Pharmacophore model was designed initially, based on the structure-activity relationships data between several pyrazole-urea analogues and their IC50 enzymatic value. The model obtained was applied to screen ZINC15 database, after which followed by drug-likeness, toxicophore, and PAINS filter. The hit compounds were docked against P. falciparum prolyl-tRNA synthetase enzyme, using validated docking method. The resulting docking poses were ranked based on the docking score and re-evaluated based on the pharmacophore criteria. Top five compounds were obtained from this step and then evaluated using molecular dynamics simulation to verify its stability and hydrogen bond dynamics over 50 nanoseconds. MM-PBSA analysis was also performed to estimate their binding free energy. Ultimately, their potential bioactivity as antimalarial candidates have been verified against 3D7 strain. Results: The results showed that all five compounds obtained from virtual screening possess micromolar potency in vitro. Two compounds (ZINC 1029449 and ZINC1029453), yield high antimalarial activity (0.44 and 0.72 μM, respectively) Conclusions: Overall, the virtual screening approach has successfully produced lead compounds which can be further optimized to be antimalarial agents.

In-silico analysis of potent Mosquirix vaccine adjuvant leads

BackgroundWorld Health Organization recommend the use of malaria vaccine, Mosquirix, as a malaria prevention strategy. However, Mosquirix has failed to reduce the global burden of malaria because of its inefficacy. The Mosquirix vaccine’s modest effectiveness against malaria, 36% among kids aged 5 to 17 months who need at least four doses, fails to aid malaria eradication. Therefore, highly effective and efficacious malaria vaccines are required. The well-characterized P. falciparum circumsporozoite surface protein can be used to discover adjuvants that can increase the efficacy of Mosquirix. Therefore, the study sought to undertake an in-silico discovery of Plasmodium falciparum circumsporozoite surface protein inhibitors with pharmacological properties on Mosquirix using hierarchical virtual screening and molecular dynamics simulation. ResultsMonoclonal antibody L9, an anti-Plasmodium falciparum circumsporozoite surface protein molecule, was used to identify Plasmodium falciparum circumsporozoite surface protein inhibitors with pharmacological properties on Mosquirix during a virtual screening process in ZINCPHARMER that yielded 23 hits. After drug-likeness and absorption, distribution, metabolism, excretion, and toxicity property analysis in the SwissADME web server, only 9 of the 23 hits satisfied the requirements. The 9 compounds were docked with Plasmodium falciparum circumsporozoite surface protein using the PyRx software to understand their interactions. ZINC25374360 (−8.1 kcal/mol), ZINC40144754 (−8.3 kcal/mol), and ZINC71996727 (−8.9 kcal/mol) bound strongly to Plasmodium falciparum circumsporozoite surface protein with binding affinities of less than −8.0 kcal/mol. The stability of these molecularly docked Plasmodium falciparum circumsporozoite surface protein-inhibitor complexes were assessed through molecular dynamics simulation using GROMACS 2022. ZINC25374360 and ZINC71996727 formed stable complexes with Plasmodium falciparum circumsporozoite surface protein. They were subjected to in vitro validation for their inhibitory potential. The IC50 values ranging between 250 and 350 ng/ml suggest inhibition of parasite development. ConclusionTherefore, the two Plasmodium falciparum circumsporozoite surface protein inhibitors can be used as vaccine adjuvants to increase the efficacy of the existing Mosquirix vaccine. Nevertheless, additional in vivo tests, structural optimization studies, and homogenization analysis are essential to determine the anti-plasmodial action of these adjuvants in humans.

Ligand and structure based hierarchical virtual screening cascade for finding novel epidermal growth factor receptor inhibitors.

The epidermal growth factor receptor (EGFR) tyrosine kinase plays an important role in tumor formation and growth by mediating cell growth and other physiological processes. Therefore, EGFR is a promising target for the treatment of cancer. In this work, we combined ligand-based and structure-based virtual screening methods to identify novel EGFR inhibitors from a library of more than 103 thousand compounds. We first obtained hundreds of compounds with similar physiochemical properties through 3D molecular shape and electrostatic similarity screening with potent inhibitors AEE788 and Afatinib as queries. Next, we identified compounds with strong binding affinities to the EGFR pocket through molecular docking, which makes good use of the structure information of the receptor. After molecular scaffold analysis, our bioassay confirmed 13 compounds with EGFR inhibitory activity and three compounds had IC50 values below 1000 nM. In addition, we collected 5371 EGFR inhibitors from online databases, and clustered them into 7 groups by K-means method using their ECFP4 fingerprints as input. Each cluster had typical molecular fragments and corresponding activity characteristics, which could guide the design of EGFR inhibitors, and we concluded that the fragments from some of the hits are indicated in the highly active scaffolds.

Hierarchical Virtual Screening of Potential New Antibiotics from Polyoxygenated Dibenzofurans against Staphylococcus aureus Strains.

Staphylococcus aureus is a microorganism with high morbidity and mortality due to antibiotic-resistant strains, making the search for new therapeutic options urgent. In this context, computational drug design can facilitate the drug discovery process, optimizing time and resources. In this work, computational methods involving ligand- and structure-based virtual screening were employed to identify potential antibacterial agents against the S. aureus MRSA and VRSA strains. To achieve this goal, tetrahydroxybenzofuran, a promising antibacterial agent according to in vitro tests described in the literature, was adopted as the pivotal molecule and derivative molecules were considered to generate a pharmacophore model, which was used to perform virtual screening on the Pharmit platform. Through this result, twenty-four molecules were selected from the MolPort® database. Using the Tanimoto Index on the BindingDB web server, it was possible to select eighteen molecules with greater structural similarity in relation to commercial antibiotics (methicillin and oxacillin). Predictions of toxicological and pharmacokinetic properties (ADME/Tox) using the eighteen most similar molecules, showed that only three exhibited desired properties (LB255, LB320 and LB415). In the molecular docking study, the promising molecules LB255, LB320 and LB415 showed significant values in both molecular targets. LB320 presented better binding affinity to MRSA (-8.18 kcal/mol) and VRSA (-8.01 kcal/mol) targets. Through PASS web server, the three molecules, specially LB320, showed potential for antibacterial activity. Synthetic accessibility (SA) analysis performed on AMBIT and SwissADME web servers showed that LB255 and LB415 can be considered difficult to synthesize and LB320 is considered easy. In conclusion, the results suggest that these ligands, particularly LB320, may bind strongly to the studied targets and may have appropriate ADME/Tox properties in experimental studies.

Uni-Dock: GPU-Accelerated Docking Enables Ultralarge Virtual Screening.

Molecular docking, a structure-based virtual screening method, is a reliable tool to enrich potential bioactive molecules from molecular databases. With the rapid expansion of compound library sizes, the speed of existing molecular docking programs becomes less than adequate to meet the demand for screening ultralarge libraries containing tens of millions or billions of molecules. Here, we propose Uni-Dock, a GPU-accelerated molecular docking program that supports various scoring functions including vina, vinardo, and ad4. Uni-Dock achieves more than 1000-fold speedup with high accuracy compared with the AutoDock Vina running in single CPU core, outperforming reported GPU-accelerated docking programs including AutoDock-GPU and Vina-GPU based on head-to-head experiments. Uni-Dock docks molecules in batches simultaneously using concurrent threads of each molecule. The data flow between GPU and CPU is optimized to eliminate CPU hotspots and maximize GPU utility. Additionally, Uni-Dock also supports hydrogen bond biased docking for all scoring functions and can be migrated to multiple GPUs of different architectures and manufacturers. We analyzed the improved performance of Uni-Dock on the CASF-2016 and DUD-E datasets and recommend three combinations of hyperparameters corresponding to different docking scenarios. To demonstrate Uni-Dock's capability on routinely screening ultralarge libraries, we performed hierarchical virtual screening experiments with Uni-Dock on the Enamine Diverse REAL druglike set containing 38.2 million molecules to a popular target KRAS G12D in 12 h using 100 NVIDIA V100 GPUs. To the best of our knowledge, Uni-Dock should be the fastest GPU-accelerated docking program to date.

Identification of potentially high drug-like VEGFR2/c-Met dual-target type II kinase inhibitors with symmetric skeletons based on structural screening

Vascular endothelial growth factor receptor 2 (VEGFR2) and c-Mesenchymal epithelial transition factor (c-Met) are tyrosine kinase receptors associated with the occurrence of malignant tumors. Studies have shown that inhibition of VEGFR2 promotes a feedback increase in c-Met, a mechanism linked to the emergence of resistance to VEGFR2 inhibitors. Therefore, treatment targeting both VEGFR2 and c-Met will have better application prospects. In this study, hierarchical virtual screening was performed on ZINC15, Molport and Mcule-ULTIMATE databases to identify potential VEGFR2/c-Met dual inhibitors. Firstly, the best pharmacophore model for each target was used to cross-screen the three databases, and the compounds that could match the two pharmacophore models were then retained based on the Fit Value of the respective crystal ligands. Compounds ZINC, MOL, and MLB named after their database sources were retained by binding pattern analysis and docking assessment. ADMET predictions indicated that ZINC had significantly higher oral bioavailability compared to the approved drug cabozantinib. This is likely due to ZINC's unique symmetrical backbone with less structure complexity, which may reduce the occurrence of adverse effects. Molecular dynamics simulations and binding free energy analysis showed that all three hit compounds were able to stably bind at the active site, but only ZINC could form high occupancy of hydrogen bonds with both VEGFR2 and c-Met, and also only ZINC had a higher binding free energy than crystal ligands, suggesting that ZINC was the most likely potential VEGFR2/c-Met dual-target inhibitor. This finding provides a promising starting point for the development of VEGFR2/c-Met dual-target inhibitors. Communicated by Ramaswamy H. Sarma

Exploiting the co-crystal ligands shape, features and structure-based approaches for identification of SARS-CoV-2 Mpro inhibitors

SARS-CoV-2 enters the host cell through the ACE2 receptor and replicates its genome using an RNA-Dependent RNA Polymerase (RDRP). The functional RDRP is released from pro-protein pp1ab by the proteolytic activity of Main protease (Mpro) which is encoded within the viral genome. Due to its vital role in proteolysis of viral polyprotein chains, it has become an attractive potential drug target. We employed a hierarchical virtual screening approach to identify small synthetic protease inhibitors. Statistically optimized molecular shape and color-based features (various functional groups) from co-crystal ligands were used to screen different databases through various scoring schemes. Then, the electrostatic complementarity of screened compounds was matched with the most active molecule to further reduce the hit molecules’ size. Finally, five hundred eighty-seven molecules were docked in Mpro catalytic binding site, out of which 29 common best hits were selected based on Glide and FRED scores. Five best-fitting compounds in complex with Mpro were subjected to MD simulations to analyze their structural stability and binding affinities with Mpro using MM/GB(PB)SA models. Modeling results suggest that identified hits can act as the lead compounds for designing better active Mpro inhibitors to enhance the chemical space to combat COVID-19.Communicated by Ramaswamy H. Sarma

In silico screening and computational evaluation of novel promising USP14 inhibitors targeting the palm-thumb pocket.

Protein degradation and synthesis are essential for regulating various biological activities within the body. As a member of deubiquitinating enzymes (DUBs), ubiquitin-specific protease 14 (USP14) plays a critical role in regulating protein degradation and maintaining cellular protein homeostasis. However, abnormal expression of USP14 has been associated with a variety of malignant tumors and other diseases. In this study, we conducted hierarchical virtual screening against the palm-thumb pocket of USP14, which resulted in the identification of two promising hits with novel scaffolds. We systematically evaluated the potential of these two hits in terms of their binding affinity and selectivity at the computational level. The results indicated that they had stronger binding affinities than previously reported molecules, as evidenced by lower docking scores and binding free energies. The binding stability analysis and hotspot residue prediction based on the MD simulations further revealed that they were capable of stably binding to the palm-thumb pocket of USP14 via crucial interactions with the residues GLN197, TYR476, ASP199, PHE331, TYR436 and HIS426. More importantly, both candidates exhibit higher selectivity for USP14 over several other USP family members (USP5, USP7 and USP15). Our findings are hoped to be a good starting point for the development of selective USP14 inhibitors.

Identification of novel BRD4 inhibitors by pharmacophore screening, molecular docking, and molecular dynamics simulation

Bromodomain-containing protein 4 (BRD4), as an epigenetic reader, can regulate chromatin structure and gene expression via recognizing and binding acetylated lysine in histones. BRD4 promotes the expression of tumor genes, such as c-Myc and Bcl-2, so it has been regarded as an attractive target for cancers. In this work, a hierarchical virtual screening approach was applied to identify potential BRD4 inhibitors from ZINC database. 3D-QSAR pharmacophore model was firstly constructed, validated and used for virtual screening. Molecular docking was then followed to evaluate the binding mode and interactions of screened compounds with BRD4. On this basis, the best three hits ZINC02126438, ZINC16645732, ZINC00856805 were selected for molecular dynamics simulation and binding free energy calculation. Among them, ZINC16645732 had a better binding free energy than the positive drug PF-1. Our results suggested that ZINC16645732 might be a potential BRD4 inhibitor that should undergo further experimental analysis and modification.

Hierarchical Virtual Screening and Binding Free Energy Prediction of Potential Modulators of Aedes Aegypti Odorant-Binding Protein 1.

The Aedes aegypti mosquito is the main hematophagous vector responsible for arbovirus transmission in Brazil. The disruption of A. aegypti hematophagy remains one of the most efficient and least toxic methods against these diseases and, therefore, efforts in the research of new chemical entities with repellent activity have advanced due to the elucidation of the functionality of the olfactory receptors and the behavior of mosquitoes. With the growing interest of the pharmaceutical and cosmetic industries in the development of chemical entities with repellent activity, computational studies (e.g., virtual screening and molecular modeling) are a way to prioritize potential modulators with stereoelectronic characteristics (e.g., pharmacophore models) and binding affinity to the AaegOBP1 binding site (e.g., molecular docking) at a lower computational cost. Thus, pharmacophore- and docking-based virtual screening was employed to prioritize compounds from Sigma-Aldrich® (n = 126,851) and biogenic databases (n = 8766). In addition, molecular dynamics (MD) was performed to prioritize the most potential potent compounds compared to DEET according to free binding energy calculations. Two compounds showed adequate stereoelectronic requirements (QFIT > 81.53), AaegOBP1 binding site score (Score > 42.0), volatility and non-toxic properties and better binding free energy value (∆G < −24.13 kcal/mol) compared to DEET ((N,N-diethyl-meta-toluamide)) (∆G = −24.13 kcal/mol).

In Silico Identification of New Anti-SARS-CoV-2 Main Protease (Mpro) Molecules with Pharmacokinetic Properties from Natural Sources Using Molecular Dynamics (MD) Simulations and Hierarchical Virtual Screening.

Infectious agents such as SARS-CoV, MERS-CoV, and SARS-CoV-2 have emerged in recent years causing epidemics with high mortality rates. The quick development of novel therapeutic compounds is required in the fight against such pathogenic agents. Unfortunately, the traditional drug development methods are time-consuming and expensive. In this study, computational algorithms were utilized for virtual screening of a library of natural compounds in the ZINC database for their affinity towards SARS-CoV-2 Mpro. Compounds such as cinanserin, nelfinavir, baicalin, baicalein, candesartan cilexetil, chloroquine, dipyridamole, and hydroxychloroquine have the ability to prevent SARS-CoV-2 Mpro from facilitating COVID 19 infection; thus, they treat COVID 19. However, these drugs majorly act to reduce the symptoms of the disease. No anti-viral drug against COVID 19 virus infection has been discovered and approved. Therefore, this study sought to explore natural inhibitors of SARS-CoV-2 Mpro to develop a pharmacophore model for virtual screening of natural compounds in the ZINC database as potential candidates for SARS-CoV-2 Mpro inhibitors and as therapeutic molecules against COVID 19. This study undertook in silico methods to identify the best anti-viral candidates targeting SAR-CoV-2 Mpro from natural sources in the ZINC database. Initially, reported anti-SARS-CoV-2 Mpro molecules were integrated into designing a pharmacophore model utilizing PharmaGist. Later, the pharmacophore model was loaded into ZINCPHARMER and screened against the ZINC database to identify new probable drug candidates. The root means square deviation (RMSD) values of the potential drug candidates informed the selection of some of them, which were docked with SARS-CoV-2 Mpro to comprehend their interactions. From the molecular docking results, the top four candidates (ZINC000254823011, ZINC000072307130, ZINC000013627512, and ZINC000009418994) against SARS-CoV-2 Mpro, with binding energies ranging from –8.2 kcal/mol to –8.6 kcal/mol, were examined for their oral bioavailability and other pharmacokinetic properties. Consequently, ZINC000072307130 emerged as the only orally bioavailable drug candidate with desirable pharmacokinetic properties. This candidate drug was used to perform MD simulations, and the outcomes revealed that ZINC000072307130 formed a stable complex with the viral main protease. Consequently, ZINC000072307130 emerges as a potential anti-SARS-CoV-2 Mpro inhibitor for the production of new COVID 19 drugs.

In silico study to identify novel potential thiadiazole-based molecules as anti-Covid-19 candidates by hierarchical virtual screening and molecular dynamics simulations

In the present study, a new category of 1,3,4-thiadiazoles was developed by submitting methyl 2-(4-hydroxy-3-methoxybenzylidene) hydrazine-1-carbodithioate to react with the appropriate hydrazonoyl halides in presence of few drops of diisopropyl ethyl amine. The chemical structures of the newly synthesized derivatives were inferred by means of their micro-analytical and spectral data. Utilizing combined molecular docking and molecular dynamics techniques, the binding affinities and features of the synthesized compounds were evaluated against four SARS-CoV-2 target enzymes, namely, main protease (Mpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), and receptor-binding domain (RBD) of the spike protein. Compound 7 demonstrated promising binding affinities with the target enzymes Mpro, PLpro, RdRp, and RBD with docking scores of −11.4, −9.4, −8.2, and −6.8 kcal/mol, respectively. In addition, compound 7 exhibited MM-GBSA//100 ns MD docking score of −35.9 kcal/mol against Mpro. Structural and energetic analyses revealed the stability of the 7-Mpro complex over 100 ns MD simulations. In addition, compound 7 obeyed Lipinski’s rule of five, as it has acceptable absorption, distribution, and oral bioavailability inside the body. Therefore, compound 7 is considered as a promising starting point for designing potential therapeutic agents against Covid-19.

Hierarchical Virtual Screening Based on Rocaglamide Derivatives to Discover New Potential Anti-Skin Cancer Agents.

Skin Cancer (SC) is among the most common type of cancers worldwide. The search for SC therapeutics using molecular modeling strategies as well as considering natural plant-derived products seems to be a promising strategy. The phytochemical Rocaglamide A (Roc-A) and its derivatives rise as an interesting set of reference compounds due to their in vitro cytotoxic activity with SC cell lines. In view of this, we performed a hierarchical virtual screening study considering Roc-A and its derivatives, with the aim to find new chemical entities with potential activity against SC. For this, we selected 15 molecules (Roc-A and 14 derivatives) and initially used them in docking studies to predict their interactions with Checkpoint kinase 1 (Chk1) as a target for SC. This allowed us to compile and use them as a training set to build robust pharmacophore models, validated by Pearson’s correlation (p) values and hierarchical cluster analysis (HCA), subsequentially submitted to prospective virtual screening using the Molport® database. Outputted compounds were then selected considering their similarities to Roc-A, followed by analyses of predicted toxicity and pharmacokinetic properties as well as of consensus molecular docking using three software. 10 promising compounds were selected and analyzed in terms of their properties and structural features and, also, considering their previous reports in literature. In this way, the 10 promising virtual hits found in this work may represent potential anti-SC agents and further investigations concerning their biological tests shall be conducted.

Integrated virtual screening and molecular dynamics simulation revealed promising drug candidates of p53-MDM2 interaction.

In the vast majority of malignancies, the p53 tumor suppressor pathway is compromised. In some cancer cells, high levels of MDM2 polyubiquitinate p53 and mark it for destruction, thereby leading to a corresponding downregulation of the protein. MDM2 interacts with p53 via its hydrophobic pocket, and chemical entities that block the dimerization of the protein-protein complex can restore p53 activity. Thus far, only a few chemical compounds have been reported as potent arsenals against p53-MDM2. The Protein Data Bank has crystallogaphic structures of MDM2 in complex with certain compounds. Herein, we have exploited one of the complexes in the identification of new p53-MDM2 antagonists using a hierarchical virtual screening technique. The initial stage was to compile a targeted library of structurally appropriate compounds related to a known effective inhibitor, Nutlin 2, from the PubChem database. The identified 57 compounds were subjected to virtual screening using molecular docking to discover inhibitors with high binding affinity for MDM2. Consequently, five compounds with higher binding affinity than the standard emerged as the most promising therapeutic candidates. When compared to Nutlin 2, four of the drug candidates (CID_140017825, CID_69844501, CID_22721108, and CID_22720965) demonstrated satisfactory pharmacokinetic and pharmacodynamic profiles. Finally, MD simulation of the dynamic behavior of lead-protein complexes reveals the stability of the complexes after a 100,000ps simulation period. In particular, when compared to the other three leads, overall computational modeling found CID_140017825 to be the best pharmacological candidate. Following thorough experimental trials, it may emerge as a promising chemical entity for cancer therapy.

Identification of potential human beta-secretase 1 inhibitors by hierarchical virtual screening and molecular dynamics

Alzheimer's disease (AD) is a neurodegenerative pathology responsible for 70% of dementia cases worldwide. Despite its relevance, the few drugs available for the treatment of this disease offer only symptomatic relief, with limited efficacy and serious adverse effects. The most accepted hypothesis about the pathogenesis involves the aggregation and deposition of β-amyloid peptides, mainly in the cerebral cortex and hippocampus, through the catalytic action of beta-secretase 1 (BACE-1), making this enzyme a promising target for the development of new drugs. In order to prioritize candidates for BACE-1 inhibitors, a hierarchical virtual screening by pharmacophore model and molecular docking was performed against the 216,833 molecules contained in several databases. Our previously built pharmacophore model was used for the first filtering step, which resulted in the selection of 399 molecules. The remaining molecules were filtered through molecular docking with GOLD 5.4.0. In this step, molecules with scoring values ​​greater than the mean plus standard deviation were evaluated for commercial availability and absence of asymmetric centers. Four molecules were selected and evaluated for mutagenic potential by the AMES test with the help of the pkCSM server. Finally, they were tested against the descriptors on Lipinski and Veber rules, and ZINC01589617 (QFIT = 56.52/Score = 44.95) satisfied all requirements, being subjected to molecular dynamics simulations (t = 100 ns) in order to obtain robust data on the mode of bonding and profile of intermolecular interactions. Those in silico strategies demonstrated that ZINC01589617 is a potential candidate for biological tests. Communicated by Ramaswamy H. Sarma

Putative dual inhibitors of mTOR and RET kinase from natural products: Pharmacophore-based hierarchical virtual screening

Protein kinases play quintessential roles in regulating various cellular processes. Aberrant activation of their signal transduction modules contribute to diverse ailments including cancer. Kinases, therefore, have become attractive drug targets for therapeutic intervention. Majority of kinases are classified into the two most widely acknowledged families encompassing serine/threonine and tyrosine kinases. The mTOR (mammalian Target of Rapamycin) is a serine/threonine kinase that regulates fundamental processes including protein synthesis, growth, metabolism, aging, regeneration, and autophagy. RET (REarranged during Transfection) is a transmembrane receptor tyrosine kinase required to maintain the neuronal populations of peripheral, sympathetic, parasympathetic, and enteric nervous systems. The molecules targeting both serine/threonine and tyrosine kinases possess potent antitumor activity and have demonstrated clinical efficacy. The clinical success of PP121, a dual inhibitor of mTOR and RET kinase, therefore, was explored in the present investigation. Correspondingly, an auto-pharmacophore model was generated and employed to screen four databases of natural molecules, which map the features observed for PP121. Within this framework, multiple computational approaches were integrated including homology modeling, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations. The hierarchical strategy led to identification of two compounds- STOCK1N-98911 and STOCK1N-84953, which bind to ATP-binding sites as well as allosteric sites, of mTOR and RET kinase. Free energy calculations highlighted the role of vital residues contributing to favorable interactions in binding of hits with mTOR and RET kinase. The pharmacokinetic properties of hits were also evaluated in silico to ensure their bioavailability, providing a rationale for their development as leads against both kinases. Altogether, this investigation provides a preliminary platform for future experimental research, towards the development of dual inhibitors of serine/threonine and tyrosine kinases, a persuasive strategy for intersecting two families of oncogenes.