Discovery of a novel DYRK1A inhibitor with neuroprotective activity by virtual screening and in vitro biological evaluation.

Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is implicated in accumulation of amyloid ß-protein (Aß) and phosphorylation of Tau proteins, and thus represents an important therapeutic target for neurodegenerative diseases. Though many DYRK1A inhibitors have been discovered, there is still no marketed drug targeting DYRK1A. This is partly due to the lack of effective and safe chemotypes. Therefore, it is still necessary to identify new classes of DYRK1A inhibitors. By performing virtual screening with the workflow mainly composed of pharmacophore modeling and molecular docking as well as the following DYRK1A inhibition assay, we identified compound L9, ((Z)-1-(((5-phenyl-1H-pyrazol-4-yl)methylene)-amino)-1H-tetrazol-5-amine), as a moderately active DYRK1A inhibitor (IC50: 1.67 µM). This compound was structurally different from the known DYRK1A inhibitors, showed a unique binding mode to DYRK1A. Furthermore, compound L9 showed neuroprotective activity against okadaic acid (OA)-induced injury in the human neuroblastoma cell line SH-SY5Y by regulating the expression of Aß and phosphorylation of Tau protein. This compound was neither toxic to the SH-SY5Y cells nor to the human normal liver cell line HL-7702 (IC50: >100 µM). In conclusion, we have identified a novel DYRK1A inhibitor with neuroprotective activity through virtual screening and in vitro biological evaluation, which holds the promise for further study.

Discovery of novel Akt1 inhibitors by an ensemble-based virtual screening method, molecular dynamics simulation, and in vitro biological activity testing.

Akt1, as an important member of the Akt family, plays a controlled role in cancer cell growth and survival. Inhibition of Akt1 activity can promote cancer cell apoptosis and inhibit tumor growth. Therefore, in this investigation, a multilayer virtual screening approach, including receptor-ligand interaction-based pharmacophore, 3D-QSAR, molecular docking, and deep learning methods, was utilized to construct a virtual screening platform for Akt1 inhibitors. 17 representative compounds with different scaffolds were identified as potential Akt1 inhibitors from three databases. Among these 17 compounds, the Hit9 exhibited the best inhibitory activity against Akt1 with inhibition rate of 33.08% at concentration of 1 µM. The molecular dynamics simulations revealed that Hit9 and Akt1 could form a compact and stable complex. Moreover, Hit9 interacted with some key residues by hydrophobic, electrostatic, and hydrogen bonding interactions and induced substantial conformation changes in the hinge region of the Akt1 active site. The average binding free energies for the Akt1-CQU, Akt1-Ipatasertib, and Akt1-Hit9 systems were - 34.44, - 63.37, and - 39.14 kJ mol-1, respectively. In summary, the results obtained in this investigation suggested that Hit9 with novel scaffold may be a promising lead compound for developing new Akt1 inhibitor for treatment of various cancers with Akt1 overexpressed.

Watermelon: setup and validation of an in silico fragment-based approach.

We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential hotspots for ligand interactions within a protein target's binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.

Integrated Computational Approaches for Drug Design Targeting Cruzipain.

Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. Trypanosoma cruzi is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of T. cruzi.

Ensemble-Based Virtual Screening Led to the Discovery of Novel Lead Molecules as Potential NMBAs.

Neuromuscular blocking agents (NMBAs) are routinely used during anesthesia to relax skeletal muscle. Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels; NMBAs can induce muscle paralysis by preventing the neurotransmitter acetylcholine (ACh) from binding to nAChRs situated on the postsynaptic membranes. Despite widespread efforts, it is still a great challenge to find new NMBAs since the introduction of cisatracurium in 1995. In this work, an effective ensemble-based virtual screening method, including molecular property filters, 3D pharmacophore model, and molecular docking, was applied to discover potential NMBAs from the ZINC15 database. The results showed that screened hit compounds had better docking scores than the reference compound d-tubocurarine. In order to further investigate the binding modes between the hit compounds and nAChRs at simulated physiological conditions, the molecular dynamics simulation was performed. Deep analysis of the simulation results revealed that ZINC257459695 can stably bind to nAChRs' active sites and interact with the key residue Asp165. The binding free energies were also calculated for the obtained hits using the MM/GBSA method. In silico ADMET calculations were performed to assess the pharmacokinetic properties of hit compounds in the human body. Overall, the identified ZINC257459695 may be a promising lead compound for developing new NMBAs as an adjunct to general anesthesia, necessitating further investigations.

Exploring TEAD2 as a drug target for therapeutic intervention of cancer: A multi-computational case study.

Transcriptional enhanced associate domain (TEAD) is a family of transcription factors that plays a significant role during embryonic developmental processes, and its dysregulation is responsible for tumour progression. TEAD is considered as druggable targets in various diseases, namely cancer, cardiovascular diseases and neurodegenerative disorders. Previous structural studies revealed the importance of the central hydrophobic pocket of TEAD as a potential target for small-molecule inhibitors and demonstrated flufenamic acid (FLU) (a COX-2 enzyme inhibitor) to bind and inhibit TEAD2 functions. However, to date, no drug candidates that bind specifically to TEAD2 with high selectivity and efficacy have been developed or proposed. Within this framework, we present here a case study where we have identified potential TEAD2 inhibitor candidates by integrating multiple computational approaches. Among the candidates, the top two ranked compounds ZINC95969481 (LG1) which is a fused pyrazole derivative and ZINC05203789 (LG2), a fluorene derivative resulted in much favourable binding energy scores than the reference ligand, FLU. The drug likeliness of the best compounds was also evaluated in silico to ensure the bioavailability of these compounds particularly LG1 as compared to FLU thus providing a strong rationale for their development as leads against TEAD. Molecular dynamics simulations results highlighted the role of key residues contributing to favourable interactions in TEAD2-LG1 complex with much favourable interaction and binding free energy values with respect to the reference compound. Altogether, this study provides a starting platform to be more exploited by future experimental research towards the development of inhibitors against TEAD, a persuasive strategy for therapeutic intervention in cancer treatment.

Length and rigidity of the spacer impact on aldose reductase inhibition of the 5F-like ARIs in a dual-occupied mode.

The primary objective of this study was to investigate the structure-activity relationship of a new series of 5F-like Aldose Reductase Inhibitors (ARIs) using in silico docking method. In this perspective, 6 novel ARIs have been designed and synthesized. Evaluation of the inhibition of these compounds to ALR2 was carried on with epalrestat and 5F as the references. It was found that the spacer of 5F-like ARIs has a great influence on their inhibitory activity. Rigid spacer with length equal to 3 âˆ¼ 4 carbon alkyl chain brings about better inhibitory activity. Among them, compound 4b was verified as the most active ARIs, where its IC50 value was 16.8 ± 1.3 nM. Furthermore, in silico docking studies using AutoDock 4.2 as well as molecular simulation using GROMACS 2022.1 showed that 5F-like ARIs adopt a dual-occupation mode. The interaction energy (-25 to -74 kcal/mol), as well as MM-GBSA binding free energy (-37 to -65 kcal/mol) was positively correlated with their ALR2 inhibition constant (2000 to 16.8 nM). Docking interaction explained well the structure-activity relationship. A pharmacophore model has been set up for 5F-like ARIs thereafter. This model indicates that as an effective ARI, the entity should have four characteristics: an aromatic center, two hydrogen bond donors, and one hydrogen bond acceptor. By the way, all the 5F-like ARIs reported here are good to mild antioxidant with EC50 value between 13.6 ± 1.2 and 71.1 ± 3.2 µM. All our data direct the further development of more optimal ARIs for the treatment of diabetic complication in the future.

Ligand-based in silico identification and biological evaluation of potential inhibitors of nicotinamide N-methyltransferase.

Nicotinamide N-methyltransferase (NNMT) is a protein coding gene, which methylates the nicotinamide (NA) (vitamin B3) to produce 1-methylnicotinamide (MNA). Several studies have suggested that the overexpression of NNMT is associated with different metabolic disorders like obesity and type-2 diabetes thereby making it an important therapeutic target for development of anti-diabetic agents. Here we describe a workflow for identification of new inhibitors of NNMT from a library of small molecules. In this study, we have hypothesized a four-point pharmacophore model based on the pharmacophoric features of reported NNMT inhibitors in the literature. The statistically significant pharmacophore hypothesis was used to explore the Maybridge compound library that resulted in mapping of 1330 hit compounds on the proposed hypothesis. Subsequently, a total of eight high scoring compounds, showing good protein-ligand interactions in the molecular docking study, were selected for biological evaluation of NNMT activity. Eventually, four compounds were found to show significant inhibitory activity for NNMT and can be further explored to design new derivatives around the identified scaffolds with improved activities as NNMT inhibitors.

Design, synthesis, and pharmacological evaluation of indazole carboxamides of N-substituted pyrrole derivatives as soybean lipoxygenase inhibitors.

In this paper, we attempted to develop a novel class of compounds against lipoxygenase, a key enzyme in the biosynthesis of leukotrienes implicated in a series of inflammatory diseases. Given the absence of appropriate human 5-lipoxygenase crystallographic data, solved soybean lipoxygenase-1 and -3 structures were used as a template to generate an accurate pharmacophore model which was further used for virtual screening purposes. Eight compounds (1-8) have been derived from the in-house library consisting of N-substituted pyrroles conjugated with 5- or 6-indazole moieties through a carboxamide linker. This study led to the discovery of hit molecule 8 bearing a naphthyl group with the IC50 value of 22 µM according to soybean lipoxygenase in vitro assay. Isosteric replacement of naphthyl ring with quinoline moieties and reduction of carbonyl carboxamide group resulted in compounds 9-12 and 13, respectively. Compound 12 demonstrated the most promising enzyme inhibition. In addition, compounds 8 and 12 were found to reduce the carrageenan-induced paw edema in vivo by 52.6 and 49.8%, respectively. In view of the encouraging outcomes concerning their notable in vitro and in vivo anti-inflammatory activities, compounds 8 and 12 could be further optimized for the discovery of novel 5-lipoxygenase inhibitors in future. A structure-based 3D pharmacophore model was used in the virtual screening of in-house library to discover novel potential 5-lipoxygenase inhibitors.

Virtual screening for potential discoidin domain receptor 1 (DDR1) inhibitors based on structural assessment.

Discoidin domain receptor 1 (DDR1) (EC Number 2.7.10.1) has recently been considered as a promising therapeutic target for idiopathic pulmonary fibrosis (IPF). However, none of the currently discovered DDR1 inhibitors have been included in clinical studies due to low target specificity or druggability limitations, necessitating various approaches to develop novel DDR1 inhibitors. In this study, to assure target specificity, a docking assessment of the DDR1 crystal structures was undertaken to find the well-differentiated crystal structure, and 4CKR was identified among many crystal structures. Then, using the best pharmacophore model and molecular docking, virtual screening of the ChEMBL database was done, and five potential molecules were identified as promising inhibitors of DDR1. Subsequently, all hit compound complex systems were validated using molecular dynamics simulations and MM/PBSA methods to assess the stability of the system after ligand binding to DDR1. Based on molecular dynamics simulations and hydrogen-bonding occupancy analysis, the DDR1-Cpd2, DDR1-Cpd17, and DDR1-Cpd18 complex systems exhibited superior stability compared to the DDR1-Cpd1 and DDR-Cpd33 complex systems. Meanwhile, when targeting DDR1, the descending order of the five hit molecules' binding free energies was Cpd17 (- 145.820 kJ/mol) > Cpd2 (- 131.818 kJ/mol) > Cpd18 (- 130.692 kJ/mol) > Cpd33 (- 129.175 kJ/mol) > Cpd1 (- 126.103 kJ/mol). Among them, Cpd2, Cpd17, and Cpd18 showed improved binding characteristics, indicating that they may be potential DDR1 inhibitors. In this research, we developed a high-hit rate, effective screening method that serves as a theoretical guide for finding DDR1 inhibitors for the development of IPF therapeutics.

Structure-guided identification of a selective sulfonamide-based inhibitor targeting the human carbonic anhydrase VA isoform.

In recent years, multistep hybrid computational protocols have attracted attention for their application in the drug discovery of enzyme inhibitors. So far, there are large collections of human carbonic anhydrase (hCA) inhibitors, but only a few of them selectively inhibit the mitochondrial isoforms hCA VA and VB as potential therapeutics in obesity treatment. Most sulfonamide-based inhibitors show poor selectivity for inhibiting isoforms of therapeutic interest over ubiquitous hCA I and hCA II. Herein, we propose a combination of ligand- and structure-based approaches to generate pharmacophore models for hCA VA inhibitors. Then, we performed a virtual screening (VS) campaign on a database of commercially available sulfonamides. Finally, the in silico screening followed by docking studies suggested several "hit compounds" that demonstrated to inhibit hCA VA at a low nanomolar concentration in a stopped-flow CO2 hydrase assay. Notably, the best candidate, 2-(3,4-dihydro-2H-quinolin-1-yl)-N-(4-sulfamoylphenyl)acetamide (code name VAME-28) proved to be a potent hCA VA inhibitor (Ki value of 54.8 nM) and a more selective agent over hCA II when compared to the reference compound topiramate.

LSD1-Based Reversible Inhibitors Virtual Screening and Binding Mechanism Computational Study.

As one of the crucial targets of epigenetics, histone lysine-specific demethylase 1 (LSD1) is significant in the occurrence and development of various tumors. Although several irreversible covalent LSD1 inhibitors have entered clinical trials, the large size and polarity of the FAD-binding pocket and undesired toxicity have focused interest on developing reversible LSD1 inhibitors. In this study, targeting the substrate-binding pocket of LSD1, structure-based and ligand-based virtual screenings were adopted to expand the potential novel structures with molecular docking and pharmacophore model strategies, respectively. Through drug-likeness evaluation, ADMET screening, molecular dynamics simulations, and binding free energy screening, we screened out one and four hit compounds from the databases of 2,029,554 compounds, respectively. Generally, these hit compounds can be divided into two categories, amide (Lig2 and Comp2) and 1,2,4-triazolo-4,3-α-quinazoline (Comp3, Comp4, Comp7). Among them, Comp4 exhibits the strongest binding affinity. Finally, the binding mechanisms of the hit compounds were further calculated in detail by the residue free energy decomposition. It was found that van der Waals interactions contribute most to the binding, and FAD is also helpful in stabilizing the binding and avoiding off-target effects. We believe this work not only provides a solid theoretical foundation for the design of LSD1 substrate reversible inhibitors, but also expands the diversity of parent nucleus, offering new insights for synthetic chemists.

Identification of potential inhibitors, conformational dynamics, and mechanistic insights into mutant Kirsten rat sarcoma virus (G13D) driven cancers.

The mutations at the hotspot region of K-Ras result in the progression of cancer types. Our study aimed to explore the small molecule inhibitors against the G13D mutant K-Ras model with anti-cancerous activity from food and drug administration (FDA)-approved drug compounds. We implemented several computational strategies such as pharmacophore-based virtual screening, molecular docking, absorption, distribution, metabolism and excretion features, and molecular simulation to ensure the identified hit compounds have potential efficacy against G13D K-Ras. We found that the FDA-approved compounds, namely, azelastine, dihydrocodeine, paroxetine, and tramadol, are potential candidates to inhibit the action of G13D mutant K-Ras. All four compounds exhibited similar binding patterns of sotorasib, and a structural binding mechanism with significant hydrophobic contacts. The descriptor features from the QikProp of all four compounds are within allowable limits compared to sotorasib drug. Consequently, a molecular simulation result emphasized that the dihydrocodeine and tramadol exhibited less fluctuation, minimal basin, significant h-bonds, and potent inhibition against G13D K-Ras. As a result, the current research identifies prospective K-Ras inhibitors that could be further improved with biochemical analysis for precision medicine against K-Ras-driven cancers.

Pharmacophore-Model-Based Virtual-Screening Approaches Identified Novel Natural Molecular Candidates for Treating Human Neuroblastoma.

The mortality of cancer patients with neuroblastoma is increasing due to the limited availability of specific treatment options. Few drug candidates for combating neuroblastoma have been developed, and identifying novel therapeutic candidates against the disease is an urgent issue. It has been found that muc-N protein is amplified in one-third of human neuroblastomas and expressed as an attractive drug target against the disease. The myc-N protein interferes with the bromodomain and extraterminal (BET) family proteins. Pharmacologically inhibition of the protein potently depletes MYCN in neuroblastoma cells. BET inhibitors target MYCN transcription and show therapeutic efficacy against neuroblastoma. Therefore, the study aimed to identify potential inhibitors against the BET family protein, specifically Brd4 (brodamine-containing protein 4), to hinder the activity of neuroblastoma cells. To identify effective molecular candidates against the disease, a structure-based pharmacophore model was created for the binding site of the Brd4 protein. The pharmacophore model generated from the protein Brd4 was validated to screen potential natural active compounds. The compounds identified through the pharmacophore-model-based virtual-screening process were further screened through molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, and molecular dynamics (MD) simulation approach. The pharmacophore-model-based screening process initially identified 136 compounds, further evaluated based on molecular docking, ADME analysis, and toxicity approaches, identifying four compounds with good binding affinity and lower side effects. The stability of the selected compounds was also confirmed by dynamic simulation and molecular mechanics with generalized Born and surface area solvation (MM-GBSA) methods. Finally, the study identified four natural lead compounds, ZINC2509501, ZINC2566088, ZINC1615112, and ZINC4104882, that will potentially inhibit the activity of the desired protein and help to fight against neuroblastoma and related diseases. However, further evaluations through in vitro and in vivo assays are suggested to identify their efficacy against the desired protein and disease.

Virtual screening based on pharmacophore model for developing novel HPPD inhibitors.

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is an important target for herbicide design. A multilayered virtual screening workflow was constructed by combining two pharmacophore models based on ligand and crystal complexes, molecular docking, molecular dynamics (MD), and biological activity determination to identify novel small-molecule inhibitors of HPPD. About 110, 000 compounds of Bailingwei and traditional Chinese medicine databases were screened. Of these, 333 were analyzed through docking experiments. Five compounds were selected by analyzing the binding pattern of inhibitors with amino acid residues in the active pocket. All five compounds could produce stable coordination with cobalt ion, and form favorable π-π interactions. MD simulation demonstrated that Phe381 and Phe424 made large contributions to the strength of binding. The enzyme activity experiment verified that compound-139 displayed excellent potency against AtHPPD (IC50 = 0.742 µM), however, compound-5222 had inhibitory effect on human HPPD (IC50 = 6 nM). Compound-139 exhibited herbicidal activity to some extent on different gramineous weeds. This work provided a strong insight into the design and development of novel HPPD inhibitor using in silico techniques.

Molecular docking and dynamics studies of Nicotinamide Riboside as a potential multi-target nutraceutical against SARS-CoV-2 entry, replication, and transcription: A new insight.

The highly contagious Coronavirus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is a newborn infectious member of the dangerous beta-coronaviruses (ß-CoVs) following SARS and MERS-CoVs, can be regarded as the most significant issue afflicting the whole world shortly after December 2019. Considering CoVs as RNA viruses with a single-stranded RNA genome (+ssRNA), the critical viral enzyme RNA dependent RNA polymerase (RdRp) is a promising therapeutic target for the potentially fatal infection COVID-19. Nicotinamide riboside (NR), which is a naturally occurring analogue of Niacin (vitamin B3), is expected to have therapeutic effects on COVID-19 due to its super close structural similarity to the proven RdRp inhibitors. Thus, at the first phase of the current molecular docking and dynamics simulation studies, we targeted SARS-CoV-2 RdRp. On the next phase, SARS-CoV RdRp, human Angiotensin-converting enzyme 2, Inosine-5'-monophosphate dehydrogenase, and the SARS-CoV-2 Structural Glycoproteins Spike, Nonstructural viral protein 3-Chymotrypsin-like protease, and Papain-like protease were targeted using the docking simulation to find other possible antiviral effects of NR serendipitously. In the current study, the resulted scores from molecular docking and dynamics simulations as the primary determinative factor as well as the observed reliable binding modes have demonstrated that Nicotinamide Riboside and its active metabolite NMN can target human ACE2 and IMPDH, along with the viral Spro, Mpro, PLpro, and on top of all, RdRp as a potential competitive inhibitor.

Hybrid Pharmacophore- and Structure-Based Virtual Screening Pipeline to Identify Novel EGFR Inhibitors That Suppress Non-Small Cell Lung Cancer Cell Growth.

Dysregulated epidermal growth factor receptor (EGFR) expression is frequently observed in non-small cell lung cancer (NSCLC) growth and metastasis. Despite recent successes in the development of tyrosine kinase inhibitors (TKIs), inevitable resistance to TKIs has led to urgent calls for novel EGFR inhibitors. Herein, we report a rational workflow used to identify novel EGFR-TKIs by combining hybrid ligand- and structure-based pharmacophore models. Three types of models were developed in this workflow, including 3D QSAR-, common feature-, and structure-based EGFR-TK domain-containing pharmacophores. A National Cancer Institute (NCI) compound dataset was adopted for multiple-stage pharmacophore-based virtual screening (PBVS) of various pharmacophore models. The six top-scoring compounds were identified through the PBVS pipeline coupled with molecular docking. Among these compounds, NSC609077 exerted a significant inhibitory effect on EGFR activity in gefitinib-resistant H1975 cells, as determined by an enzyme-linked immunosorbent assay (ELISA). Further investigations showed that NSC609077 inhibited the anchorage-dependent growth and migration of lung cancer cells. Furthermore, NSC609077 exerted a suppressive effect on the EGFR/PI3K/AKT pathway in H1975 cells. In conclusion, these findings suggest that hybrid virtual screening may accelerate the development of targeted drugs for lung cancer treatment.

Binding Thermodynamics and Dissociation Kinetics Analysis Uncover the Key Structural Motifs of Phenoxyphenol Derivatives as the Direct InhA Inhibitors and the Hotspot Residues of InhA.

Given the current epidemic of multidrug-resistant tuberculosis, there is an urgent need to develop new drugs to combat drug-resistant tuberculosis. Direct inhibitors of the InhA target do not require activation and thus can overcome drug resistance caused by mutations in drug-activating enzymes. In this work, the binding thermodynamic and kinetic information of InhA to its direct inhibitors, phenoxyphenol derivatives, were explored through multiple computer-aided drug design (CADD) strategies. The results show that the van der Waals interactions were the main driving force for protein-ligand binding, among which hydrophobic residues such as Tyr158, Phe149, Met199 and Ile202 have high energy contribution. The AHRR pharmacophore model generated by multiple ligands demonstrated that phenoxyphenol derivatives inhibitors can form pi-pi stacking and hydrophobic interactions with InhA target. In addition, the order of residence time predicted by random acceleration molecular dynamics was consistent with the experimental values. The intermediate states of these inhibitors could form hydrogen bonds and van der Waals interactions with surrounding residues during dissociation. Overall, the binding and dissociation mechanisms at the atomic level obtained in this work can provide important theoretical guidance for the development of InhA direct inhibitors with higher activity and proper residence time.

Identification of Human Dihydroorotate Dehydrogenase Inhibitor by a Pharmacophore-Based Virtual Screening Study.

Human dihydroorotate dehydrogenase (hDHODH) is an enzyme belonging to a flavin mononucleotide (FMN)-dependent family involved in de novo pyrimidine biosynthesis, a key biological pathway for highly proliferating cancer cells and pathogens. In fact, hDHODH proved to be a promising therapeutic target for the treatment of acute myelogenous leukemia, multiple myeloma, and viral and bacterial infections; therefore, the identification of novel hDHODH ligands represents a hot topic in medicinal chemistry. In this work, we reported a virtual screening study for the identification of new promising hDHODH inhibitors. A pharmacophore-based approach combined with a consensus docking analysis and molecular dynamics simulations was applied to screen a large database of commercial compounds. The whole virtual screening protocol allowed for the identification of a novel compound that is endowed with promising inhibitory activity against hDHODH and is structurally different from known ligands. These results validated the reliability of the in silico workflow and provided a valuable starting point for hit-to-lead and future lead optimization studies aimed at the development of new potent hDHODH inhibitors.

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).