Ligand-based Virtual Screening Approach Research Articles

Identification of selective 5-LOX and FLAP inhibitors as novel anti-inflammatory agents by ligand-based virtual screening

Inflammation is a multifaceted biological process in which the conversion of arachidonic acid to eicosanoids, including prostaglandins and leukotrienes (LTs), plays a crucial role. 5-Lipoxygenase (5-LOX) is a key enzyme in cellular LT biosynthesis, and it is supported by the accessory protein 5-lipoxygenase-activating protein (FLAP). Pharmacological interventions to modulate LTs aim at either decreasing their biosynthesis or at mitigating their biological effects. Therefore, inhibiting 5-LOX or FLAP represents a useful strategy to reduce inflammation. Herein we present the identification and pharmacological evaluation of novel inhibitors targeting 5-LOX or FLAP. By means of a ligand-based virtual screening approach, we selected 38 compounds for in vitro assays. Among them, ALR-38 exhibits direct 5-LOX inhibition, while ALR-6 and ALR-27 showed potential as FLAP inhibitors. These latter not only reduced LT production but also promoted the generation of specialized pro-resolving mediators in specific human macrophage phenotypes. Interestingly, the identified compounds turned out to be selective for their respective targets, as none of them displayed activity towards microsomal prostaglandin E2 synthase-1 and soluble epoxide hydrolase, which are other proteins involved in eicosanoid biosynthesis. Thus, these compounds are endowed with potential therapeutic utility in mitigating inflammatory responses and might offer a venue for tackling inflammation-based disorders.

Phenol Glucosides as Potential Inhibitors of SGLT1 for Enhanced Diabetes Mellitus Treatment in Patients with Declining Renal Function

Diabetes mellitus poses a significant global health challenge, necessitating the continual search for innovative therapeutic strategies. While sodium-glucose cotransporter 2 (SGLT2) inhibitors have shown promise in diabetes management, their efficacy diminishes in patients with declining renal function. This study aims to evaluate the potential of phenol glucosides as inhibitors for the sodium-glucose transport protein 1 (SGLT1), a key player in glucose uptake. We identified phlorizin as a representative phenol glucoside for experimental validation. The SGLT1 protein structure (PDB ID 7wmv) was analyzed through Ramachandran plot, ERRAT score, and ProSAweb Z-score, confirming its high-quality 3D conformation. A ligand-based virtual screening approach yielded 400 compounds that matched well with our pharmacophore models, including 10 compounds from virtual libraries. Notably, two compounds stood out for their high matching scores. Molecular docking simulations revealed strong binding affinities with SGLT1, especially for the compound CHEMBL2303983 with a binding energy of -11.2 kcal/mol. ADMET analysis was conducted to evaluate the drug-likeness & safety profile of such high-affinity compounds. The compounds exhibited variable water solubility and moderate lipophilicity but were generally compliant with most drug-likeness rules. However, certain challenges such as low GI absorption and inability to cross the blood-brain barrier were identified. No PAINS or Brenk alerts were raised, suggesting a low likelihood of assay interference or toxicity. In conclusion, our in-silico approach has identified promising candidates among phenol glucosides for inhibition of SGLT1, albeit with challenges in solubility and pharmacokinetics that require further optimization. The study opens new avenues for the synthesis and experimental verification of novel SGLT1 inhibitors.

Identification of Small Molecule Inhibitors of Human Cytomegalovirus pUL89 Endonuclease Using Integrated Computational Approaches.

Replication of Human Cytomegalovirus (HCMV) requires the presence of a metal-dependent endonuclease at the C-terminus of pUL89, in order to properly pack and cleave the viral genome. Therefore, pUL89 is an attractive target to design anti-CMV intervention. Herein, we used integrated structure-based and ligand-based virtual screening approaches in combination with MD simulation for the identification of potential metal binding small molecule antagonist of pUL89. In this regard, the essential chemical features needed for the inhibition of pUL89 endonuclease domain were defined and used as a 3D query to search chemical compounds from ZINC and ChEMBL database. Thereafter, the molecular docking and ligand-based shape screening were used to narrow down the compounds based on previously identified pUL89 antagonists. The selected virtual hits were further subjected to MD simulation to determine the intrinsic and ligand-induced flexibility of pUL89. The predicted binding modes showed that the compounds reside well in the binding site of endonuclease domain by chelating with the metal ions and crucial residues. Taken in concert, the in silico investigation led to the identification of potential pUL89 antagonists. This study provided promising starting point for further in vitro and in vivo studies.

Allosteric targeting resolves limitations of earlier LFA-1 directed modalities

Integrins are a family of cell surface receptors well-recognized for their therapeutic potential in a wide range of diseases. However, the development of integrin targeting medications has been impacted by unexpected downstream effects, reflecting originally unforeseen interference with the bidirectional signalling and cross-communication of integrins. We here selected one of the most severely affected target integrins, the integrin lymphocyte function-associated antigen-1 (LFA-1, αLβ2, CD11a/CD18), as a prototypic integrin to systematically assess and overcome these known shortcomings. We employed a two-tiered ligand-based virtual screening approach to identify a novel class of allosteric small molecule inhibitors targeting this integrin’s αI domain. The newly discovered chemical scaffold was derivatized, yielding potent bis-and tris-aryl-bicyclic-succinimides which inhibit LFA-1 in vitro at low nanomolar concentrations. The characterisation of these compounds in comparison to earlier LFA-1 targeting modalities established that the allosteric LFA-1 inhibitors (i) are devoid of partial agonism, (ii) selectively bind LFA-1 versus other integrins, (iii) do not trigger internalization of LFA-1 itself or other integrins and (iv) display oral availability. This profile differentiates the new generation of allosteric LFA-1 inhibitors from previous ligand mimetic-based LFA-1 inhibitors and anti-LFA-1 antibodies, and is projected to support novel immune regulatory regimens selectively targeting the integrin LFA-1. The rigorous computational and experimental assessment schedule described here is designed to be adaptable to the preclinical discovery and development of novel allosterically acting compounds targeting integrins other than LFA-1, providing an exemplary approach for the early characterisation of next generation integrin inhibitors.

Target-driven machine learning-enabled virtual screening (TAME-VS) platform for early-stage hit identification.

High-throughput screening (HTS) methods enable the empirical evaluation of a large scale of compounds and can be augmented by virtual screening (VS) techniques to save time and money by using potential active compounds for experimental testing. Structure-based and ligand-based virtual screening approaches have been extensively studied and applied in drug discovery practice with proven outcomes in advancing candidate molecules. However, the experimental data required for VS are expensive, and hit identification in an effective and efficient manner is particularly challenging during early-stage drug discovery for novel protein targets. Herein, we present our TArget-driven Machine learning-Enabled VS (TAME-VS) platform, which leverages existing chemical databases of bioactive molecules to modularly facilitate hit finding. Our methodology enables bespoke hit identification campaigns through a user-defined protein target. The input target ID is used to perform a homology-based target expansion, followed by compound retrieval from a large compilation of molecules with experimentally validated activity. Compounds are subsequently vectorized and adopted for machine learning (ML) model training. These machine learning models are deployed to perform model-based inferential virtual screening, and compounds are nominated based on predicted activity. Our platform was retrospectively validated across ten diverse protein targets and demonstrated clear predictive power. The implemented methodology provides a flexible and efficient approach that is accessible to a wide range of users. The TAME-VS platform is publicly available at https://github.com/bymgood/Target-driven-ML-enabled-VS to facilitate early-stage hit identification.

Integrative Ligand-Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Simulation Approaches Identified Potential Lead Compounds against Pancreatic Cancer by Targeting FAK1

Pancreatic cancer is a very deadly disease with a 5-year survival rate, making it one of the leading causes of cancer-related deaths globally. Focal adhesion kinase 1 (FAK1) is a ubiquitously expressed protein in pancreatic cancer. FAK, a tyrosine kinase that is overexpressed in cancer cells, is crucial for the development of tumors into malignant phenotypes. FAK functions in response to extracellular signals by triggering transmembrane receptor signaling, which enhances focal adhesion turnover, cell adhesion, cell migration, and gene expression. The ligand-based drug design approach was used to identify potential compounds against the target protein, which included molecular docking: ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized born surface area (MM-GBSA). Following the retrieval of twenty hits, four compounds were selected for further evaluation based on a molecular docking approach. Three newly discovered compounds, including PubChem CID24601203, CID1893370, and CID16355541, with binding scores of -10.4, -10.1, and -9.7 kcal/mol, respectively, may serve as lead compounds for the treatment of pancreatic cancer associated with FAK1. The ADME (absorption, distribution, metabolism, and excretion) and toxicity analyses demonstrated that the compounds were effective and nontoxic. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the cancer.

N-Substituted 4-sulfamoylbenzoic acid derivatives as inhibitors of cytosolic phospholipase A2α

The N,N-disubstituted 4-sulfamoylbenzoic acid derivative 3, which was found to be an inhibitor of cytosolic phospholipase A2α (cPLA2α) with micromolar activity in a ligand-based virtual screening approach, was structurally modified to increase its enzyme inhibitory potency. Replacing the substituents on the sulfonamide nitrogen with other residues such as naphthyl, naphthylmethyl, indolylalkyl and differently substituted phenyl moieties did not lead to a significant increase in activity. Only strong structural convergence to the potent known benzhydrylindole-substituted benzoic acid derivatives that had served as templates in the virtual screening resulted in compounds with considerable potency. Thus, the sulfamoyl benzoic acid derivatives 85 and 88 showed submicromolar IC50 values against cPLA2α.Graphical abstract

A chemical probe inhibitor targeting STAT1 restricts cancer stem cell traits and angiogenesis in colorectal cancer

BackgroundColorectal cancer (CRC) is a worldwide cancer with rising annual incidence. New medications for patients with CRC are still needed. Recently, fluorescent chemical probes have been developed for cancer imaging and therapy. Signal transducer and activator of transcription 1 (STAT1) has complex functions in tumorigenesis and its role in CRC still needs further investigation.MethodsRNA sequencing datasets in the NCBI GEO repository were analyzed to investigate the expression of STAT1 in patients with CRC. Xenograft mouse models, tail vein injection mouse models, and azoxymethane/dextran sodium sulfate (AOM/DSS) mouse models were generated to study the roles of STAT1 in CRC. A ligand-based high-throughput virtual screening approach combined with SWEETLEAD chemical database analysis was used to discover new STAT1 inhibitors. A newly designed and synthesized fluorescently labeled 4’,5,7-trihydroxyisoflavone (THIF) probe (BODIPY-THIF) elucidated the mechanistic actions of STAT1 and THIF in vitro and in vivo. Colonosphere formation assay and chick chorioallantoic membrane assay were used to evaluate stemness and angiogenesis, respectively.ResultsUpregulation of STAT1 was observed in patients with CRC and in mouse models of AOM/DSS-induced CRC and metastatic CRC. Knockout of STAT1 in CRC cells reduced tumor growth in vivo. We then combined a high-throughput virtual screening approach and analysis of the SWEETLEAD chemical database and found that THIF, a flavonoid abundant in soybeans, was a novel STAT1 inhibitor. THIF inhibited STAT1 phosphorylation and might bind to the STAT1 SH2 domain, leading to blockade of STAT1-STAT1 dimerization. The results of in vitro and in vivo binding studies of THIF and STAT1 were validated. The pharmacological treatment with BODIPY-THIF or ablation of STAT1 via a CRISPR/Cas9-based strategy abolished stemness and angiogenesis in CRC. Oral administration of BODIPY-THIF attenuated colitis symptoms and tumor growth in the mouse model of AOM/DSS-induced CRC.ConclusionsThis study demonstrates that STAT1 plays an oncogenic role in CRC. BODIPY-THIF is a new chemical probe inhibitor of STAT1 that reduces stemness and angiogenesis in CRC. BODIPY-THIF can be a potential tool for CRC therapy as well as cancer cell imaging.

Distributed heterogeneous ensemble learning on Apache Spark for ligand-based virtual screening

Virtual screening is one of the most common computer-aided drug design techniques that apply computational tools and methods on large libraries of molecules to extract the drugs. Ensemble learning is a recent paradigm launched to improve machine learning results in terms of predictive performance and robustness. It has been successfully applied in ligand-based virtual screening (LBVS) approaches. Applying ensemble learning on huge molecular libraries is computationally expensive. Hence, the distribution and parallelisation of the task have become a significant step by using sophisticated frameworks such as Apache Spark. In this paper, we propose a new approach HEnsL_DLBVS, for heterogeneous ensemble learning, distributed on Spark to improve the large-scale LBVS results. To handle the problem of imbalanced big training datasets, we propose a novel hybrid technique. We generate new training datasets to evaluate the approach. Experimental results confirm the effectiveness of our approach with satisfactory accuracy and its superiority over homogeneous models.

Ligand-based Discovery of Novel Small Molecule Inhibitors of RON Receptor Tyrosine Kinase.

RON (Recepteur d'Origine Nantais) receptor tyrosine kinase is a promising target for anti-cancer therapeutics. The aim of this study was to identify new RON inhibitors using virtual screening methods. To this end, a ligand-based virtual screening approach was employed for screening of ZINC database on the homology model of RON receptor. All the selected hits were inspected in terms of drug-likeness, ADME properties, and toxicity profiles. Ligand-based similarity searches along with further filtering criteria led to the identification of two compounds, TKI1 and TKI2 that were evaluated using in vitro cell-based RON inhibition assays. The results showed that TKI1 and TKI2 could reduce phosphorylation of RON. Both compounds showed inhibitory activity of the downstream mTOR pathway with no apparent effects on other signaling mediators in a dose-dependent manner. These compounds can provide a basis for developing novel anti-RON inhibitors applicable to cancer therapy using medicinal chemistry-oriented optimization strategies.

Combined ligand and structure-based virtual screening approaches for identification of novel AChE inhibitors.

The excessive activity of acetylcholinesterase enzyme (AChE) causes different neuronal problems, especially dementia and neuronal cell deaths. Food and Drug Administration (FDA) approved drugs donepezil, rivastigmine, tacrine and galantamine are AChE inhibitors and in the treatment of Alzheimer’s disease (AD) these drugs are currently prescribed. However, these inhibitors have various adverse side effects. Therefore, there is a great need for the novel selective AChE inhibitors with fewer adverse side effects for the effective treatment. In this study, combined ligand-based and structure-based virtual screening approaches were used to identify new hit compounds from small molecules library of National Cancer Institute (NCI) containing approximately 265,000 small molecules. In the present study, we developed a computational pipeline method to predict the binding affinities of the studied compounds at the specific target sites. For this purpose, a text mining study was carried out initially and compounds containing the keyword “indol” were considered. The therapeutic activity values against AD were screened using the binary quantitative structure activity relationship (QSAR) models. We then performed docking, molecular dynamics (MD) simulations and free energy analysis to clarify the interactions between selected ligands and enzyme. Thus, in this study we identified new promising hit compounds from a large database that may be used to inhibit the enzyme activity of AChE.

Case studies on computer-based identification of natural products as lead molecules

AbstractThe development and application of computer-aided drug design/discovery (CADD) techniques (such as structured-base virtual screening, ligand-based virtual screening and neural networks approaches) are on the point of disintermediation in the pharmaceutical drug discovery processes. The application of these CADD methods are standing out positively as compared to other experimental approaches in the identification of hits. In order to venture into new chemical spaces, research groups are exploring natural products (NPs) for the search and identification of new hits and more efficient leads as well as the repurposing of approved NPs. The chemical space of NPs is continuously increasing as a result of millions of years of evolution of species and these data are mainly stored in the form of databases providing access to scientists around the world to conduct studies using them. Investigation of these NP databases with the help of CADD methodologies in combination with experimental validation techniques is essential to identify and propose new drug molecules. In this chapter, we highlight the importance of the chemical diversity of NPs as a source for potential drugs as well as some of the success stories of NP-derived candidates against important therapeutic targets. The focus is on studies that applied a healthy dose of the emerging CADD methodologies (structure-based, ligand-based and machine learning).

Design of Dual Inhibitors of Histone Deacetylase 6 and Heat Shock Protein 90.

Histone deacetylase 6 (HDAC6) and heat shock protein 90 (Hsp90) are widely investigated anticancer drug targets. Importantly, several lines of evidence indicate that their regulation and activity are intimately linked, and that their combined inhibition may lead to impressive therapeutic benefits. In this study, we developed and applied an integrated computational strategy to design dual inhibitors of HDAC6 and Hsp90. Although the two targets share very little homology, an integrated ligand-based and structure-based virtual screening approach indicated a subset of compounds possessing the key structural requirements for binding at both targets. In vitro tests demonstrated that some of the selected candidates are able to selectively inhibit HDAC6 over HDAC1, to increase the acetylation levels of tubulin on cell assays and to reduce cell proliferation. The discovered compounds represent valuable starting points for further hit optimization.

Computer aided ligand based screening for identification of promising molecules against enzymes involved in peptidoglycan biosynthetic pathway from Acinetobacter baumannii

A. baumannii has been considered as Priority-I as suggested by the World Health Organization (WHO) and the most critical pathogenic microorganism for causing nosocomial infection in imunno-compromised hospital-acquired patients due to multi-drug resistance (MDR). In the current study, we utilized “Computer-aided ligand-based virtual screening approach” for identification of promising molecules against Mur family proteins based on the known inhibitor (Naphthyl Tetronic Acids ((5Z)-3-(4-chlorophenyl)-4-hydroxy-5-(1-naphthylmethylene) furan-2(5H)-one)) of MurB from E. coli. The in-house library was prepared using a similarity search of a known inhibitor (Drug Bank ID: DB07296) against several relevant chemical databases. The molecules obtained from virtual screening of Naphthyl Tetronic Acids in-house library were successively subjected to physicochemical and ADMET screening. After this, the molecules which passed all the filters, subsequently subjected into interaction analysis with the drug target proteins (MurB, MurD, MurE and MurG) of A. baumanni and the results explained that four molecules were promising (CHEMBL468144, DB07296, Enamine_T5956969 and 54723243) for further molecular dynamics simulations. The free and ligand bounded proteins that undergone MD simulation are listed as follows: MurB, MurB-CHEMBL468144, MurB-DB07296, MurE, MurE-54723243, MurE-DB07296, MurD, MurD-Enamine_T5956969, MurD-DB07296, MurG, MurG-CHEMBL468144, and MurG-DB07296. Based on global and essential dynamics analysis, the stability order of molecules towards MurB (CHEMBL468144 > DB07296); MurD (Enamine_T5956969 > DB07296); MurE (54723243 > DB07296) and MurG (CHEMBL468144 > DB07296) indicates that the newly identified molecules are more promising one in comparison with the existing inhibitor. Based on all the docking and MD simulation results, the stability order of the free and ligand bounded protein are as follows; MurB and MurB-ligand complexes > MurD and MurD-ligand complexes > MurG and MurG-ligand complexes > MurE and MurE-ligand complexes. Finally, the selected compounds would be recommended for further experimental investigations and used as promising inhibitors of the infection caused by A. baumannii.

Exploring the Potentiality of Natural Products from Essential Oils as Inhibitors of Odorant-Binding Proteins: A Structure- and Ligand-Based Virtual Screening Approach To Find Novel Mosquito Repellents.

Odorant-binding proteins (OBPs) are the main olfactory proteins of mosquitoes, and their structures have been widely explored to develop new repellents. In the present study, we combined ligand- and structure-based virtual screening approaches using as a starting point 1633 compounds from 71 botanical families obtained from the Essential Oil Database (EssOilDB). Using as reference the crystallographic structure of N,N-diethyl-meta-toluamide interacting with the OBP1 homodimer of Anopheles gambiae (AgamOBP1), we performed a structural and pharmacophoric similarity search to select potential natural products from the library. Thymol acetate, 4-(4-methyl phenyl)-pentanal, thymyl isovalerate, and p-cymen-8-yl demonstrated a favorable chemical correlation with DEET and also had high-affinity interactions with the OBP binding pocket that molecular dynamics simulations showed to be stable. To the best of our knowledge, this is the first study to evaluate on a large scale the potentiality of NPs from essential oils as inhibitors of the mosquito OBP1 using in silico approaches. Our results could facilitate the design of novel repellents with improved selectivity and affinity to the protein binding pocket and can shed light on the mechanism of action of these compounds against insect olfactory recognition.

Combined Ligand-Based and Structure-Based Virtual Screening Approach for Identification of New Dipeptidyl Peptidase 4 Inhibitors.

Dipeptidyl Peptidase 4 (DPP 4) enzyme cleaves an incretin-based glucoregulatory hormone Glucagon Like Peptide -1 from N-terminal where penultimate amino acid is either alanine or proline. Several DPP 4 inhibitors, "gliptins", are approved for the management of Type 2 Diabetes or are under clinical trial. In the present study, combined pharmacophore and docking-based virtual screening protocol were used for the identification of new hits from the Specs Database, which would inhibit DPP 4. The entire computational studies were performed using the Discovery Studio v. 4.1 software package, Pipeline Pilot v. 9.2 (Accelrys Inc.) and FRED v. 2.2.5 (OpenEye Scientific Software). Common feature pharmacophore model was generated from known DPP 4 inhibitors and validated by Receiver Operating curve analysis and GH-scoring method. Database search of Specs commercial database was performed using validated pharmacophore. Hits obtained from pharmacophore search were further docked into the binding site of DPP 4. Based on the analysis of docked poses of hits, 10 compounds were selected for in- vitro DPP 4 enzyme inhibition assay. Based on docking studies, virtual hits were predicted to form interaction with essential amino acid residues of DPP 4 and have an almost similar binding orientation as that of the reference molecule. Three compounds having Specs database ID- AN-465/42837213, AP-064/42049348 and AN- 465/43369427 were found to inhibit DPP 4 enzyme moderately. The present study demonstrates a successful utilization of in-silico tools in the identification of new DPP 4 inhibitor, which can serve as a starting point for the development of novel DPP 4 inhibitors.

In silico-guided identification of potential inhibitors against β2m aggregation in dialysis-related amyloidosis

In dialysis-related amyloidosis (DRA), misfolding of β2-microglobulin (β2m) leads to amyloid fibril deposition mainly in the skeletal joints seriously affecting their functionality. The identification and characterization of small-molecules that bind β2m and possibly inhibit its aggregation remain unexplored. In the present study, a ligand-based virtual screening approach and molecular dynamics (MD) simulations were employed to explore potent small-molecule inhibitors against β2m aggregation. The compounds were screened from various small-molecule databases by applying ligand-based virtual screening with rifamycin SV (RSV) as a reference compound. The molecular docking analysis was performed to filter out lead compounds with a higher binding affinity than RSV from a library of ∼800 compounds. Three compounds, ChEBI68321 (C1), ChEMBL360190 (C2) and ZINC3091144 (C3), displaying excellent binding free energies of –51.29, –36.51 and –34.36 kcal/mol, respectively, with β2m were subjected to MD simulations to get insights into the binding locations, key interactions and structural stability of the β2m-ligand complexes. The hydrogen bond analysis highlight higher structural stability and reduced flexibility of the loop regions of β2m in presence of C1, C2 and C3. The integrated computational approach employed in the present study identify promising lead compounds against β2m aggregation in DRA.Abbreviationsβ2mβ2-microglobulin3Dthree dimensionalADAlzheimer’s diseaseADTAutoDock ToolsDRADialysis-related amyloidosisDSSPdictionary of secondary structure of proteinsFELfree energy landscapeGROMACSGROningen MAchine for Chemical SimulationsLGALamarckian Genetic AlgorithmLINCSLINear Constraint SolverMCmain chainMDmolecular dynamicsMHC–Imajor histocompatibility complex class IMM–PBSAmolecular mechanics Poisson–Boltzmann surface areaPMEnanometer (nm); particle mesh ewaldPCAprincipal component analysisPDBprotein data bankRgradius–of–gyrationRSVrifamycin SVRMSDroot–mean–square deviationRMSFroot–mean–square fluctuationSCside chainSPCsimple point chargeSASASolvent accessible surface areaVMDvisual molecular dynamicsCommunicated by Ramaswamy H. Sarma

Scaffold hopping computational approach for searching novel β-lactamase inhibitors

We present a novel computational ligand-based virtual screening approach with scaffold hopping capabilities for the identification of novel inhibitors of β-lactamases which confer bacterial resistance to β-lactam antibiotics. The structures of known β-lactamase inhibitors were used as query ligands, and a virtual in silico screening a database of 8 million drug-like compounds was performed in order to select the ligands with similar shape and charge distribution. A set of numerical descriptors was used such as chirality, eigen spectrum of matrices of interatomic distances and connectivity together with higher order moment invariants that showed their efficiency in the field of pattern recognition but have not yet been employed in drug discovery. The developed scaffold-hopping approach was applied for the discovery of analogues of four allosteric inhibitors of serine β-lactamases. After a virtual in silico screening, the effect of two selected ligands on the activity of TEM type β-lactamase was studied experimentally. New non-β-lactam inhibitors were found that showed more effective inhibition of β-lactamases compared to query ligands.

The identification of new ATAD2 bromodomain inhibitors: the application of combined ligand and structure-based virtual screening

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches were used to identify new inhibitors for ATAD2 bromodomain. The LBVS approach was used to search 23,129,083 clean compounds to identify compounds similar to an active compound with reported pIC50 equal to 7.2. Based on LBVS results, 19 compounds were selected. To perform SBVS, by applying nine filters on 23,129,083 clean compounds, 1,057,060 compounds were selected. After performing SBVS on these selected compounds with idock software, 16 compounds with the lowest binding energies were selected. More accurate molecular docking analysis was performed on these 35 selected compounds by using iGEMDOCK software and six of them with the lowest binding energies were selected as hit compounds. These compounds were zinc36647229, zinc77969074, zinc13637358, zinc77971540, zinc12991296 and zinc19374204.

Exploring the readthrough of nonsense mutations by non-acidic Ataluren analogues selected by ligand-based virtual screening

Ataluren, also known as PTC124, is a 5-(fluorophenyl)-1,2,4-oxadiazolyl-benzoic acid suggested to suppress nonsense mutations by readthrough of premature stop codons in the mRNA. Potential interaction of PTC124 with mRNA has been recently studied by molecular dynamics simulations highlighting the importance of H-bonding and stacking π−π interactions. A series of non-acidic analogues of PTC124 were selected from a large database via a ligand-based virtual screening approach. Eight of them were synthesized and tested for their readthrough activity using the Fluc reporter harboring the UGA premature stop codon. The most active compound was further tested for suppression of the UGA nonsense mutation in the bronchial epithelial IB3.1 cell line carrying the W1282X mutation in the CFTR gene.