Receptor-based Virtual Screening Research Articles

SMS121, a new inhibitor of CD36, impairs fatty acid uptake and viability of acute myeloid leukemia

Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and the second most common among children. AML is characterized by aberrant proliferation of myeloid blasts in the bone marrow and impaired normal hematopoiesis. Despite the introduction of new drugs and allogeneic bone marrow transplantation, patients have poor overall survival rate with relapse as the major challenge, driving the demand for new therapeutic strategies. AML patients with high expression of the very long/long chain fatty acid transporter CD36 have poorer survival and very long chain fatty acid metabolism is critical for AML cell survival. Here we show that fatty acids are transferred from human primary adipocytes to AML cells upon co-culturing. A drug-like small molecule (SMS121) was identified by receptor-based virtual screening and experimentally demonstrated to target the lipid uptake protein CD36. SMS121 reduced the uptake of fatty acid into AML cells that could be reversed by addition of free fatty acids and caused decreased cell viability. The data presented here serves as a framework for the development of CD36 inhibitors to be used as future therapeutics against AML.

Bioinformatics-driven discovery of novel EGFR kinase inhibitors as anti-cancer therapeutics: In silico screening and in vitro evaluation.

Epidermal growth factor receptor EGFR inhibitors are widely used as first line therapy for the treatment of non-small-cell lung cancer (NSCLC) in patients harboring EGFR mutation. However, the acquisition of a second-site mutation (T790 M) limited the efficacy and developed resistance. Therefore, discovery and development of specific drug target for this mutation is of urgent needs. In our study we used the ChemDiv diversity database for receptor-based virtual screening to secure EGFR-TK inhibitors chemotherapeutics. We identified four compounds that bind to the ATP-binding region of the EGFR-TK using AutoDock 4.0 and AutoDock Vina1.1.2 and post-docking investigations. The ligand showed hydrophobic interactions to the hydrophobic region of the binding site and engaged in hydrogen bonding with Met793. The ligands also explored π-cation interactions between the π-system of the ligand-phenyl ring and the positive amino group of Lys745. Molecular mechanics Poisson-Boltzmann surface area MM/PBSA per-residue energy decomposition analyses revealed that Val726, Leu792, Met793, Gly796, Cys797, Leu798, and Thr844 contributed the most to the binding energy. Biological evaluation of the retrieved hit compounds showed suppressing activity against EGFR auto phosphorylation and selective apoptosis-induced effects toward lung cancer cells harboring the EGFR L858R/T790M double mutation. Our work anticipated into novel and specific EGFR-TKIs and identified new compounds with therapeutic potential against lung cancer.

EPIC-1042 as a potent PTRF/Cavin1-caveolin-1 interaction inhibitor to induce PARP1 autophagic degradation and suppress temozolomide efflux for glioblastoma.

Temozolomide (TMZ) treatment efficacy in glioblastoma is determined by various mechanisms such as TMZ efflux, autophagy, base excision repair (BER) pathway, and the level of O6-methylguanine-DNA methyltransferase (MGMT). Here, we reported a novel small-molecular inhibitor (SMI) EPIC-1042 (C20H28N6) with the potential to decrease TMZ efflux and promote PARP1 degradation via autolysosomes in the early stage. EPIC-1042 was obtained from receptor-based virtual screening. Co-immunoprecipitation and pull-down assays were applied to verify the blocking effect of EPIC-1042. Western blotting, co-immunoprecipitation, and immunofluorescence were used to elucidate the underlying mechanisms of EPIC-1042. In vivo experiments were performed to verify the efficacy of EPIC-1042 in sensitizing glioblastoma cells to TMZ. EPIC-1042 physically interrupted the interaction of PTRF/Cavin1 and caveolin-1, leading to reduced secretion of small extracellular vesicles (sEVs) to decrease TMZ efflux. It also induced PARP1 autophagic degradation via increased p62 expression that more p62 bound to PARP1 and specially promoted PARP1 translocation into autolysosomes for degradation in the early stage. Moreover, EPIC-1042 inhibited autophagy flux at last. The application of EPIC-1042 enhanced TMZ efficacy in glioblastoma in vivo. EPIC-1042 reinforced the effect of TMZ by preventing TMZ efflux, inducing PARP1 degradation via autolysosomes to perturb the BER pathway and recruitment of MGMT, and inhibiting autophagy flux in the later stage. Therefore, this study provided a novel therapeutic strategy using the combination of TMZ with EPIC-1042 for glioblastoma treatment.

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces.

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

DockingPie: a consensus docking plugin for PyMOL.

The primary strategy for predicting the binding mode of small molecules to their receptors and for performing receptor-based virtual screening studies is protein-ligand docking, which is undoubtedly the most popular and successful approach in computer-aided drug discovery. The increased popularity of docking has resulted in the development of different docking algorithms and scoring functions. Nonetheless, it is unlikely that a single approach outperforms the others in terms of reproducibility and precision. In this ground, consensus docking techniques are taking hold. We have developed DockingPie, an open source PyMOL plugin for individual, as well as consensus docking analyses. Smina, AutoDock Vina, ADFR and RxDock are the four docking engines that DockingPie currently supports in an easy and extremely intuitive way, thanks to its integrated docking environment and its GUI, fully integrated within PyMOL. https://github.com/paiardin/DockingPie. Supplementary data are available at Bioinformatics online.

Discovery of Novel Small Molecule HDAC1, 2, 3 Inhibitors -- Combined Receptor-Based and Ligand-Based Virtual Screening Strategy

Aims:This study aims to investigate and validate the potential drug target to HDAC1.Background:Human histone deacetylase 1 (HDAC1) can catalyze the deacetylation of histones belonging to the family of human histone deacetylases (HDACs). Amide hydrolase HDAC1 plays a key role in the development of many serious cancers such as prostate cancer, gastric cancer, lung cancer, esophageal cancer, colon cancer, and breast cancer. Therefore, HDAC1 inhibitors, promoting the transcription of a series of key genes such as the p53 gene and inhibiting the development of cancer through various downstream mechanisms, have great potential for the treatment of cancer.Objective:The objective of this study is to discover new skeleton HDAC1 inhibitors efficiently and conveniently with therapeutic potential for cancer.Method:Based on the crystal structure of HDAC1, through the combination of receptor-based and ligand- based virtual screening from the commercial compound library, the top-ranked compounds are selected for purchase through binding modes analysis, and their activities were verified through in vitro HDAC1 inhibitory biological experiments.Results:Based on LeDock, 5ICN showed good distinguishing ability and was used as the receptor. According to the results of the LeDock docking scoring from receptor-based virtual screening, 69 compounds with binding energy less than -7.5 kcal/mol were obtained and used for ligand-based virtual screening. A total of 21 novel compounds with high potential HDAC1 inhibitory activity were collected by combining the similarity searching (NN) and the multinomial Naive Bayes machine learning model (NB) methods. Through binding modes analysis, 10 compounds with different structures with potential HDAC1 inhibitory activity were selected and screened HDAC1 inhibitory in vitro. May267 showed moderate HDAC1 inhibitory activity, and the inhibition rate was 48% at a concentration of 20 μM.Conclusion:This study discovers novel small molecule HDAC1 inhibitors by combined receptor-based and ligand-based virtual screening strategy, which provides an efficient method for the discovery of other small molecule drugs. May267 shows moderate HDAC1 inhibitory activity, which can be further optimized as a lead compound. However, it still has the problem of poor kinase selectivity to be solved.

Identification of novel protein kinase C-βII inhibitors: virtual screening, molecular docking and molecular dynamics simulation studies.

Diabetic wounds (DWs) are the major end-stage manifestation encountered in diabetic patients. The two major pathways involved in the pathogenesis of DW are impaired angiogenesis and unnecessary NETosis, which are regulated by a common enzyme called protein kinase C (PKC)-βII. PKC-βII is a conventional isoform of PKC family that can be activated by calcium and diacylglycerol. PKC-βII possesses a specific expression profile and plays a distinct role in various cellular and molecular functions. The pathogenic role of PKC-βII and its involvement in the impairment of wound healing suggested that PKC-βII plays a potential role in DW progression. Hence, there is a renewed interest in developing specific inhibitors of PKC-βII. In the present study, receptor-based virtual screening was performed for the identification of potential PKC-βII inhibitors using TimTec, Enamine, Zinc and Specs databases. A total of 595 candidate compounds were evaluated based on absorption, distribution, metabolism, excretion and toxicity, standard precision docking. Further, extra-precision docking and binding free energy calculations were carried out for top-ranked compounds. Based on Glide score and protein-ligand interactions, we have identified compound 1 as a potential inhibitor. Finally, molecular dynamics (MD) simulation was performed for top compound 1 using the Desmond module (Schrödinger suite) to identify the structural stability of the protein-ligand complex. Gratifyingly, MD trajectory analysis demonstrated the stable binding conformation of compound 1 with PKC-βII enzyme. In silico approaches incorporated in this study provide a set of new putative PKC-βII inhibitors which could be potential leads to develop DW therapeutics.

In silico discovery of novel inhibitors from Northern African natural products database against main protease (Mpro) of SARS-CoV-2

The recent outbreak of COVID-19 (Coronavirus Disease 2019), caused by a novel SARS-CoV-2 virus, has led to public health emergencies worldwide where time is as important as equipment to save lives. Antimalarial drugs such as hydroxychloroquine and chloroquine derivatives are used in emergencies but they are not suitable for patients with high blood pressure, diabetes and heart problems. Since there are no approved drugs for this disease, science is challenged to find vaccines and new drugs. Therefore, as part of our Silico drug design strategy, we identified drug-like compounds that inhibit replication of the main protease (Mpro) of SARS-CoV-2 based on receptor-based virtual database screening, molecular docking, molecular dynamics, and drug-similarity profiling from the NANPDB natural products database available at North African. The two resulting hit compounds named 5- Chloro-Omega-hydroxy-1-O-methylemodin and cystodion E showed the highest binding energy with Mpro of SARS-CoV-2 and strong inhibitory activity compared with the previously published N3 inhibitor. The complexes of these two compounds were validated by molecular dynamics analysis (RMSD, RMSF, Rg, total number of hydrogen bonds and secondary structure fractions of the protein in the complex) as the best method to evaluate the biological stability of the system. Therefore, these molecules deserve more attention in drug development compared to COVID-19. Highlights A large database of natural compounds was screened against nCoV-2's Mpro. Molecular docking and Molecular dynamics were used as powerful methods. Two compounds were found are very attractive to inhibit Mpro of nCoV-2. ADME-Tox profiling is evaluated the active compounds are not cancerogenic. Communicated by Ramaswamy H. Sarma

Targeting Epigenetic Regulators Using Machine Learning: Potential Sirtuin 2 Inhibitors

Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the [Formula: see text][Formula: see text]kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.

In silico identification of natural product inhibitors against Octamer-binding transcription factor 4 (Oct4) to impede the mechanism of glioma stem cells.

Octamer-binding transcription factor 4 (Oct4) is a core regulator in the retention of stemness, invasive, and self-renewal properties in glioma initiating cells (GSCs) and its overexpression inhibits the differentiation of glioma cells promoting tumor cell proliferation. The Pit-Oct-Unc (POU) domain comprising POU-specific domain (POUS) and POU-type homeodomain (POUHD) subdomains is the most critical part of the Oct4 for the generation of induced pluripotent stem cells from somatic cells that lead to tumor initiation, invasion, posttreatment relapse, and therapeutic resistance. Therefore, the present investigation hunts for natural product inhibitors (NPIs) against the POUHD domain of Oct4 by employing receptor-based virtual screening (RBVS) followed by binding free energy calculation and molecular dynamics simulation (MDS). RBVS provided 13 compounds with acceptable ranges of pharmacokinetic properties and good docking scores having key interactions with the POUHD domain. More Specifically, conformational and interaction stability analysis of 13 compounds through MDS unveiled two compounds ZINC02145000 and ZINC32124203 which stabilized the backbone of protein even in the presence of linker and POUS domain. Additionally, ZINC02145000 and ZINC32124203 exhibited stable and strong interactions with key residues W277, R242, and R234 of the POUHD domain even in dynamic conditions. Interestingly, ZINC02145000 and ZINC32124203 established communication not only with the POUHD domain but also with the POUS domain indicating their incredible potency toward thwarting the function of Oct4. ZINC02145000 and ZINC32124203 also reduced the flexibility and escalated the correlations between the amino acid residues of Oct4 evidenced by PCA and DCCM analysis. Finally, our examination proposed two NPIs that can impede the Oct4 function and may help to improve overall survival, diminish tumor relapse, and achieve a cure not only in deadly disease GBM but also in other cancers with minimal side effects.

Identification of New Potential Inhibitors of Quorum Sensing through a Specialized Multi-Level Computational Approach.

Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self-produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum-sensing (QS), an important process of cell-to-cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum-sensing, using CviR—the quorum-sensing receptor from Chromobacterium violaceum—as a model target. This in silico approach combines protein-ligand docking (with 7 different docking programs/scoring functions), receptor-based virtual screening, molecular dynamic simulations, and free energy calculations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target-specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA-Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repurposing towards QS inhibition.

Discovery of a new series of PI3K-δ inhibitors from Virtual Screening

PI3K-δ mediates key immune cell signaling pathways and is a target of interest for treatment of oncological and immunological disorders. Here we describe the discovery and optimization of a novel series of PI3K-δ selective inhibitors. We first identified hits containing an isoindolinone scaffold using a combined ligand- and receptor-based virtual screening workflow, and then improved potency and selectivity guided by structural data and modeling. Careful optimization of molecular properties led to compounds with improved permeability and pharmacokinetic profile, and high potency in a whole blood assay.

Potential natural inhibitors of xanthine oxidase and HMG-CoA reductase in cholesterol regulation: in silico analysis

BackgroundHypercholesterolemia has posed a serious threat of heart diseases and stroke worldwide. Xanthine oxidase (XO), the rate-limiting enzyme in uric acid biosynthesis, is regarded as the root of reactive oxygen species (ROS) that generate atherosclerosis and cholesterol crystals. β-Hydroxy β-methylglutaryl-coenzyme A reductase (HMGR) is a rate-limiting enzyme in cholesterol biosynthesis. Although some commercially available enzyme inhibiting drugs have effectively reduced cholesterol levels, most of them have failed to meet potential drug candidates’ requirements. Here, we have carried out an in-silico analysis of secondary metabolites that have already shown good inhibitory activity against XO and HMGR in a wet lab setup.MethodsOut of 118 secondary metabolites reviewed, sixteen molecules inhibiting XO and HMGR were selected based on the IC50 values reported in in vitro assays. Further, receptor-based virtual screening was carried out against secondary metabolites using GOLD Protein-Ligand Docking Software, combined with subsequent post-docking, to study the binding affinities of ligands to the enzymes. In-silico ADMET analysis was carried out to explore their pharmacokinetic properties, followed by toxicity prediction through ProTox-II.ResultsThe molecular docking of amentoflavone (GOLD score 70.54, ∆G calc. = − 10.4 Kcal/mol) and ganomycin I (GOLD score 59.61, ∆G calc. = − 6.8 Kcal/mol) displayed that the drug has effectively bound at the competitive site of XO and HMGR, respectively. Besides, 6-paradol and selgin could be potential drug candidates inhibiting XO. Likewise, n-octadecanyl-O-α-D-glucopyranosyl (6′ → 1″)-O-α-D-glucopyranoside could be potential drug candidates to maintain serum cholesterol. In-silico ADMET analysis has shown that these sixteen metabolites were optimal within the categorical range compared to commercially available XO and HMGR inhibitors, respectively. Toxicity analysis through ProTox-II revealed that 6-gingerol, ganoleucoin K, and ganoleucoin Z are toxic for human use.ConclusionThis computational analysis supports earlier experimental evidence towards the inhibition of XO and HMGR by natural products. Further study is necessary to explore the clinical efficacy of these secondary molecules, which might be alternatives for the treatment of hypercholesterolemia.

Protein reliability analysis and virtual screening of natural inhibitors for SARS-CoV-2 main protease (Mpro) through docking, molecular mechanic & dynamic, and ADMET profiling

Due to an outbreak of COVID-19, the number of research papers devoted to in-silico drug discovery of potential antiviral drugs is increasing every day exponentially. Still, there is no specific drug to prevent or treat this novel coronavirus (SARS-CoV-2) disease. Thus, the screening for a potential remedy presents a global challenge for scientists. Up to date over a hundred crystallographic structures of SARS-CoV-2 Mpro have been deposited to Protein Data Bank. With many known proteins, the demand for a reliable target has become higher than ever, so as the choice of an efficient computational methods. Therefore, in this study comparative methods have been used for receptor-based virtual screening, targeting 9 selected structures of viral Mpro. Reliability analyses followed by re-docking of the specific co-crystallized ligand provided the best reproductivity for structures with PDB ID 6LU7, 6Y2G and 6Y2F. The influence of crystallographic water on an outcome of a virtual screening against selected targets was also investigated. Once the most reliable targets were selected, the library of easy purchasable natural compounds were retrieved from the MolPort database (10,305 compounds) and docked against the selected Mpro proteins. To ensure the efficiency of the selected compounds, binding energies for top-15 hit ligands were calculated using Molecular Mechanics as well as their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were predicted. Based on predicted binding energies and toxicities, top-5 compounds were selected and subjected to Molecular Dynamics simulation and found to be stable in complex to act as possible inhibitors for SARS-CoV-2. Communicated by Ramaswamy H. Sarma

Consensus Docking in Drug Discovery

Background: Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets. Methods: In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose. Results: The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus. Conclusion: In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.

Application of MM-PBSA Methods in Virtual Screening.

Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS.

Identification of novel inhibitors of p-hydroxyphenylpyruvate dioxygenase using receptor-based virtual screening

Abstract p-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) is a potential target for herbicide discovery, which catalyzes the conversion of p-hydroxyphenylpyruvate to homogentisate. Triketone HPPD inhibitors have been widely applied to weed control for many years. To discover potential HPPD inhibitors with novel scaffold, a virtual screening procedure combining pharmacophore model, molecular docking and molecular dynamics (MD) simulation was carried out. The crystal complex (PDB: 6ISD ) with sulcotrione was selected to establish the receptor-ligand pharmacophore model (CBP). The verified CBP model with key features and interactions was employed to seek structures with potential inhibitory from ZINC database. The best-fit compounds from pharmacophore screening (2050 compounds) were subsequently subjected to molecular docking with CDOCKER method to identify the binding affinity of inhibitors with amino acid residues in active pocket. Six compounds were obtained as the final hits with the Fe(Ш) coordination and π-π interaction with Phe381 and Phe424. MD results demonstrated that Phe424 made great contributions to binding affinities of all the systems. This work provided powerful insights into the design and development of novel HPPD inhibitor in silico techniques. ZINC000255329531 was identified as the most promising candidate due to its high HPPD inhibition effect (ΔGbind = 25.66 kcal mol−1), in comparison with the commercial triketone HPPD herbicide sulcotrione (ΔGbind = 18.94 kcal mol−1).

Hit identification of FGFR1 inhibitors using receptor-based virtual screening

Hit identification of FGFR1 inhibitors using receptor-based virtual screening

Discovery of novel acylhydrazone neuraminidase inhibitors

Neuraminidase (NA) plays a crucial role in the replication and transmission of influenza virus. NA inhibitors have been developed as effective treatments for influenza A and B infections. In this paper, a new lead neuraminidase inhibitor 6a (IC50 = 7.10 ± 0.2 μM) was discovered by ligand-based virtual screening, receptor-based virtual screening, molecular dynamics simulation (MD), and bioassay validation. MD simulation indicates that the morpholinyl group of 6a could be embedded in 430-loop of NA. To exploit the 430-loop in the active site, a series of novel acylhydrazone NA inhibitors 6b-6g were designed and synthesized based on the lead compound 6a. Compound 6e exerts the most potency, with IC50 value of 2.37 ± 0.5 μM against NA, which is lower than that of oseltamivir carboxylate (OC) (IC50 = 3.84 μM). Overall, this work provided unique insights in the discovery of potent inhibitors against NA.

Combined Ligand- and Receptor-Based Virtual Screening Methodology to Identify Structurally Diverse Protein Tyrosine Phosphatase 1B Inhibitors.

Protein tyrosine phosphatase 1B (PTP1B) is a potential drug target for diabetes and obesity. However, the design of PTP1B inhibitors that combine potency and bioavailability is a great challenge, and new leads are needed to circumvent this problem. Virtual screening (VS) workflows can be used to find new PTP1B inhibitors with little chemical similarity to existing inhibitors. Unfortunately, previous VS workflows for the identification of PTP1B inhibitors have several limitations, such as a small number of experimentally tested compounds and the low bioactivity of those compounds. We developed a VS workflow capable of identifying 15 structurally diverse PTP1B inhibitors from 20 compounds, the bioactivity of which was tested in vitro. Moreover, we identified two PTP1B inhibitors with the highest bioactivity reported by any VS campaign (i.e., IC50 values of 1.4 and 2.1 μm), which could be used as new lead compounds.