Structure-based Virtual Screening Approach Research Articles

High-throughput virtual screening of novel potent inhibitor(s) for Human Vanin-1 enzyme

Vanin-1 (VNN1) is a glycosylphosphatidylinositol (GPI)-anchored ectoenzyme which hydrolyzes pantetheine to pantothenic acid and cysteamine. It has emerged as a promising drug target for many human diseases associated with oxidative stress and inflammatory pathways. In the present study we used structure-based virtual screening approach for the identification of small molecule inhibitors of vanin-1. A chemical library consisting of natural compounds, synthetic compounds and RRV analogs were screened for drug-like molecules. The filtered molecules were subjected to molecular docking studies. Three potential hits—ZINC04073864 (Natural compound), CID227017 (synthetic compound) and CID129558381 (RRV analog)—were identified for the target enzyme. The molecules form good number of hydrogen bonds with the catalytic residues such as Glu79, Lys178 and Cys211. The apo-VNN1 and VNN1-ligand complexes were subjected to molecular dynamics (MD) simulation for 30 ns. The geometric properties such as root mean square deviation, radius of gyration, solvent accessible surface area, number of hydrogen bonds and the distance between the catalytic triad residues-Glu79, Lys178 and Cys211 were altered upon binding of the compounds. Essential dynamics and entropic studies further confirmed that the fluctuations in VNN1 decrease upon binding of the compounds. The lead molecules were stable throughout the simulation time period. Molecular Mechanics Poisson–Boltzmann Surface Area (MM/PBSA) studies showed that Van der Waals interaction energy contributes significantly to the total binding free energy. Thus, our study reveals three lead molecules—ZINC04073864, CID227017 and CID129558381 as potential inhibitors of Vanin-1 which can be validated through further studies. Communicated by Ramaswamy H. Sarma

Virtual screening of phytoconstituents from miracle herb nigella sativa targeting nucleocapsid protein and papain-like protease of SARS-CoV-2 for COVID-19 treatment

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel etiological agent of coronavirus disease 2019 (COVID-19). Nigella sativa, commonly known as black seed or black cumin, has been a historical and traditional plant since thousands of years. Based on their therapeutic efficacy, the chief components of terpenoids and flavonoids were selected from N. sativa seeds and seed oil. This study was designed to check the antiviral efficacy of N. sativa main phytoconstituents against five potential targets of SARS-CoV-2 using in silico structure-based virtual screening approach. Out of twenty five phytocomponents, ten components showed best binding affinity against two viral proteins viz. N-terminal RNA binding domain (NRBD; PDB ID: 6M3M) of nucleocapsid protein and papain-like protease (PL-PRO; PDB ID: 6W9C) of SARS-CoV-2 using AutoDock 4.2.6, AutoDock Vina and iGEMDOCK. PASS analyses of all ten phytocomponents using Lipinski’s Rule of five showed promising results. Further, druglikeness and toxicity assessment using OSIRIS Data Warrior v5.2.1 software exhibited the feasibility of phytocomponents as drug candidates with no predicted toxicity. Molecular dynamics simulation study of NRBD of SARS-CoV-2 nucleocapsid protein-alpha-spinasterol complex and PL-PRO-cycloeucalenol complex displayed strong stability at 300 K. Both these complexes exhibited constant root mean square deviation (RMSDs) of protein side chains and Cα atoms throughout the simulation run time. Interestingly, PL-PRO and NRBD are key proteins in viral replication, host cell immune evasion and viral assembly. Thus, NRBD and PL-PRO have the potential to serve as therapeutic targets for N. sativa phytoconstituents in drug discovery process against COVID-19.

Virtual screening and drug repurposing experiments to identify potential novel selective MAO-B inhibitors for Parkinson's disease treatment.

The main study's purpose is to detect novel natural products (NPs) that are potentially selective MAO-B inhibitors and, additionally, to computationally reposition the marketed drugs with a new therapeutic role for Parkinson's disease. To reach the goals, 3D similarity search, docking, ADMETox, and drug repurposing approaches were employed. Thus, an unbiased benchmarking dataset was built including selective and nonselective inhibitors for MAO-B compliant with both ligand- and structure-based virtual screening approaches. A retrospective and prospective mining scenario was applied to SPECS NP and DrugBank databases to detect novel scaffolds with potential benefits for Parkinson's disease patients. Out of the three best selected natural products, cardamomin showed excellently predicted drug-like properties, superior pharmacological profile, and specific interactions with MAO-B active site, indicating a potential selectivity over MAO-B. Two marketed drugs, fenamisal and monobenzone, were proposed as promising candidates repurposed for Parkinson's disease. The application of shape, physicochemical, and electrostatic similarity searches protocol emerged as a plausible solution to explore MAO-B inhibitors selectivity. This protocol might serve as a rewarding tool in early drug discovery and can be extended to other protein targets.

Identification of a dual TAOK1 and MAP4K5 inhibitor using a structure-based virtual screening approach

The STE20 kinase family is a complex signalling cascade that regulates cytoskeletal organisation and modulates the stress response. This signalling cascade includes various kinase mediators, such as TAOK1 and MAP4K5. The dysregulation of the STE20 kinase pathway is linked with cancer malignancy. A small-molecule inhibitor targeting the STE20 kinase pathway has therapeutic potential. In this study, a structure-based virtual screening (SBVS) approach was used to identify potential dual TAOK1 and MAP4K5 inhibitors. Enzymatic assays confirmed three potential dual inhibitors (>50% inhibition) from our virtual screening, and analysis of the TAOK1 and MAP4K5 binding sites indicated common interactions for dual inhibition. Compound 1 revealed potent inhibition of colorectal and lung cancer cell lines. Furthermore, compound 1 arrested cancer cells in the G0/G1 phase, which suggests the induction of apoptosis. Altogether, we show that the STE20 signalling mediators TAOK1 and MAP4K5 are promising targets for drug research.

Targeting Peptidyl-prolyl Cis-trans Isomerase NIMA-interacting 1: A Structure-based Virtual Screening Approach to Find Novel Inhibitors.

Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Pin1) is an enzyme that isomerizes phosphorylated serine or threonine motifs adjacent to proline residues. Pin1 has important roles in several cellular signaling pathways, consequently impacting the development of multiple types of cancers. Based on the previously reported inhibitory activity of pentacyclic triterpenoids isolated from the gum resin of Boswellia genus against Pin1, we designed a computational experiment using molecular docking, pharmacophore filtering, and structural clustering allied to molecular dynamics (MD) simulations and binding free energy calculations to explore the inhibitory activity of new triterpenoids against Pin1 structure. Here, we report different computational evidence that triterpenoids from neem (Azadirachta indica A. Juss), such as 6-deacetylnimbinene, 6-Oacetylnimbandiol, and nimbolide, replicate the binding mode of the Pin1 substrate peptide, interacting with high affinity with the binding site and thus destabilizing the Pin1 structure. Our results are supported by experimental data, and provide interesting structural insights into their molecular mechanism of action, indicating that their structural scaffolds could be used as a start point to develop new inhibitors against Pin1.

Identification of natural inhibitors against Mpro of SARS-CoV-2 by molecular docking, molecular dynamics simulation, and MM/PBSA methods

The recent outbreak of SARS-CoV-2 disease, also known as COVID-19, has emerged as a pandemic. The unavailability of specific therapeutic drugs and vaccines urgently demands sincere efforts for drug discovery against COVID-19. The main protease (Mpro) of SARS-CoV-2 is a critical drug target as it plays an essential role in virus replication. Therefore for the identification of potential inhibitors of SARS-CoV-2 Mpro, we applied a structure-based virtual screening approach followed by molecular dynamics (MD) study. A library of 686 phytochemicals was subjected to virtual screening which resulted in 28 phytochemicals based on binding energy. These phytochemicals were further subjected to drug-likeness and toxicity analysis, which resulted in seven drug-like hits. Out of seven, five phytochemicals viz., Mpro-Dehydrtectol (-10.3 kcal/mol), Epsilon-viniferin (-8.6 kcal/mol), Peimisine (-8.6 kcal/mol), Gmelanone (-8.4 kcal/mol), and Isocolumbin (-8.4 kcal/mol) were non-toxic. Consequently, these phytochemicals are subjected to MD, post MD analysis, and MM/PBSA calculations. The results of 100 ns MD simulation, RMSF, SASA, Rg, and MM/PBSA show that Epsilon-viniferin (-29.240 kJ/mol), Mpro-Peimisine (-43.031 kJ/mol) and Gmelanone (-13.093 kJ/mol) form a stable complex with Mpro and could be used as potential inhibitors of SARS-CoV-2 Mpro. However, further investigation of these inhibitors against Mpro receptor of COVID-19 is needed to validate their candidacy for clinical trials. Communicated by Ramaswamy H. Sarma

Biological Evaluation of Selected Flavonoids as Inhibitors of MNKs Targeting Acute Myeloid Leukemia.

Excessive eIF4E phosphorylation by mitogen-activated protein kinase (MAPK)-interacting kinases 1 and 2 (MNK1 and MNK2; collectively, MNKs) has been associated with oncogenesis. The overexpression of eIF4E in acute myeloid leukemia (AML) is related to cancer cell growth and survival. Thus, the inhibition of MNKs and eIF4E phosphorylation are potential therapeutic strategies for AML. Herein, a structure-based virtual screening approach was performed to identify potential MNK inhibitors from natural products. Three flavonoids, apigenin, hispidulin, and luteolin, showed MNK2 inhibitory activity with IC50 values of 308, 252, and 579 nM, respectively. A structure-activity relationship analysis was performed to disclose the molecular interactions. Furthermore, luteolin exhibited substantial inhibitory efficacy against MNK1 (IC50 = 179 nM). Experimental results from cellular assays showed that hispidulin and luteolin inhibited the growth of MOLM-13 and MV4-11 AML cells by downregulating eIF4E phosphorylation and arresting the cell cycle at the G0/G1 phase. Therefore, hispidulin and luteolin showed promising results as lead compounds for the potential treatment for AML.

Selection of antileishmanial sesquiterpene lactones from SistematX database using a combined ligand-/structure-based virtual screening approach.

Leishmaniasis refers to a complex of diseases, caused by the intracellular parasitic protozoans belonging to the genus Leishmania. Among the three types of disease manifestations, the most severe type is visceral leishmaniasis, which is caused by Leishmania donovani, and is diagnosed in more than 20,000 cases annually, worldwide. Because the current therapeutic options for disease treatment are associated with several limitations, the identification of new potential leads/drugs remains necessary. In this study, a combined approach was used, based on two different virtual screening (VS) methods, which were designed to select promising antileishmanial agents from among the entire sesquiterpene lactone (SL) dataset registered in SistematX, a web interface for managing a secondary metabolite database that is accessible by multiple platforms on the Internet. Thus, a ChEMBL dataset, including 3159 and 1569 structures that were previously tested against L. donovani amastigotes and promastigotes in vitro, respectively, was used to develop two random forest models, which performed with greater than 74% accuracy in both the cross-validation and test sets. Subsequently, a ligand-based VS assay was performed against the 1306 SistematX-registered SLs. In parallel, the crystal structures of three L. donovani target proteins, N-myristoyltransferase, ornithine decarboxylase, and mitogen-activated protein kinase 3, and a homology model of pteridine reductase 1 were used to perform a structure-based VS, using molecular docking, of the entire SistematX SL dataset. The consensus analysis of these two VS approaches resulted in the normalization of probability scores and identified 13 promising, enzyme-targeting, antileishmanial SLs from SistematX that may act against L. donovani. A combined approach based on two different virtual screening methods (structure-based and ligand-based) was performed using an in-house dataset composed of 1306 sesquiterpene lactones to identify potential antileishmanial (Leishmania donovani) structures.

Accounting of Receptor Flexibility in Ultra-Large Virtual Screens with VirtualFlow Using a Grey Wolf Optimization Method.

Structure-based virtual screening approaches have the ability to dramatically reduce the time and costs associated to the discovery of new drug candidates. Studies have shown that the true hit rate of virtual screenings improves with the scale of the screened ligand libraries. Therefore, we have recently developed an open source drug discovery platform (VirtualFlow), which is able to routinely carry out ultra-large virtual screenings. One of the primary challenges of molecular docking is the circumstance when the protein is highly dynamic or when the structure of the protein cannot be captured by a static pose. To accommodate protein dynamics, we report the extension of VirtualFlow to allow the docking of ligands using a grey wolf optimization algorithm using the docking program GWOVina, which substantially improves the quality and efficiency of flexible receptor docking compared to AutoDock Vina. We demonstrate the linear scaling behavior of VirtualFlow utilizing GWOVina up to 128 000 CPUs. The newly supported docking method will be valuable for drug discovery projects in which protein dynamics and flexibility play a significant role.

Structure-based Virtual Screening Approach for the Discovery of Potent Inhibitors of Aminoglycoside 6'-N-Acetyltransferase Type Ib [AAC(6')-Ib] against K. pneumoniae Infections

Background: Aminoglycoside 6'-N-acetyltransferase type Ib (AAC(6')-Ib) from Klebsiella pneumoniae is an established drug target and has conferred insensitivity to aminoglycosides. Aminoglycosides are often inactivated by aminoglycoside modifying enzymes encoded by genes present in the chromosome, plasmids, and other genetic elements. The AAC(6′)- Ib is an enzyme of clinical importance found in a wide variety of gram-negative pathogens. The AAC(6′)-Ib enzyme is of interest not only because of its ubiquity but also because of other characteristics e.g., it presents significant microheterogeneity at the N-termini and the aac(6′)-Ib gene is often present in integrons, transposons, plasmids, genomic islands, and other genetic structures. The majority of the reported potent inhibitors against the target are substrate analogs. Therefore, there is a need to develop or discover new scaffolds other than substrate analogs as AAC(6')-Ib inhibitor. Objective: The objective of this study is to set optimum parameters for the structure-based virtual screening by multiple docking and scoring methods. The multiple scoring of each ligand also incorporates the ‘Induced Fit’ docking effect that helps to build further confidence in the shortlisted compounds. The method eventually is able to predict the potential inhibitors that bind to the active site and can potentially inhibit the activity of the Aminoglycoside 6′-N-acetyltransferase type Ib [AAC(6’)-Ib] from Klebsiella pneumoniae. Methods: Using the available three-dimensional structure of enzyme AAC(6')-Ib inhibitor complex, a structure-based virtual screening was performed with the hope of prioritizing the promising leads. In order to set up the protocol, 30,000 drug-like molecules were selected from the ChemBridge library. Multiple docking programs, i.e. UCSF DOCK6 and AutoDock Vina have been applied in the current study so that a consensus is developed to the predicted binding modes and thus the docking accuracy. The Amber scores of the Dock6 – a secondary scoring function was also used to perform the ‘Induced Fit’ effect and correspondingly re-rank the compounds. Results: The top 30 ranked compounds of the most frequent scored were selected from the histogram. The 2D interactions of those 30 compounds were drawn from the Ligplot+ tool. Six of the compounds were prioritized as potential inhibitors as they are representing the maximum number of interactions from the rest of the compounds and also possess the drug-likeness as predicted by the estimated ADMET properties. Conclusion: This study provided useful insight that the proposed compounds have the potential to bind to the aminoglycoside binding site of AAC(6′)-Ib that may eventually inhibit the Klebsiella pneumoniae. This study has the potential to propose putative new and novel inhibitors against a resistant drug target of Klebsiella pneumoniae.

Structure-Based Virtual Screening to Discover Potential Lead Molecules for the SARS-CoV-2 Main Protease.

The COVID-19 disease is caused by a new strain of the coronavirus family (SARS-CoV-2), and it has affected at present millions of people all over the world. The indispensable role of the main protease (Mpro) in viral replication and gene expression makes this enzyme an attractive drug target. Therefore, inhibition of SARS-CoV-2 Mpro as a proposition to halt virus ingression is being pursued by scientists globally. Here we carried out a study with two objectives: the first being to perform comparative protein sequence and 3D structural analysis to understand the effect of 12 point mutations on the active site. Among these, two mutations, viz., Ser46 and Phe134, were found to cause a significant change at the active sites of SARS-CoV-2. The Ser46 mutation present at the entrance of the S5 subpocket of SARS-CoV-2 increases the contribution of other two hydrophilic residues, while the Phe134 mutation, present in the catalytic cysteine loop, can cause an increase in catalytic efficiency of Mpro by facilitating fast proton transfer from the Cys145 to His41 residue. It was observed that active site remained conserved among Mpro of both SARS-CoVs, except at the entrance of the S5 subpocket, suggesting sustenance of substrate specificity. The second objective was to screen the inhibitory effects of three different data sets (natural products, coronaviruses main protease inhibitors, and FDA-approved drugs) using a structure-based virtual screening approach. A total of 73 hits had a combo score >2.0. Eight different structural scaffold classes were identified, such as one/two tetrahydropyran ring(s), dipeptide/tripeptide/oligopeptide, large (approximately 20 atoms) cyclic peptide, and miscellaneous. The screened hits showed key interactions with subpockets of the active site. Further, molecular dynamics studies of selected screened compounds confirmed their perfect fitting into the subpockets of the active site. This study suggests promising structures that can fit into the SARS-CoV-2 Mpro active site and also offers direction for further lead optimization and rational drug design.

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.

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.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs.

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

Structure of the enterovirus D68 RNA-dependent RNA polymerase in complex with NADPH implicates an inhibitor binding site in the RNA template tunnel

Enterovirus D68 (EV-D68) is an emerging viral pathogen belonging to the Enterovirus genus of the Picornaviridae family, which is a serious threat to human health and has resulted in significant economic losses. The EV-D68 genome encodes an RNA-dependent RNA polymerase (RdRp) 3Dpol, which is central for viral genome replication and considered as a promising target for specific antiviral therapeutics. In this study, we report the crystal structures of human EV-D68 RdRp in the apo state and in complex with the inhibitor NADPH, which was selected by using a structure-based virtual screening approach. The EV-D68-RdRp-NADPH complex is the first RdRp-inhibitor structure identified in the species Enterovirus D. The inhibitor NADPH occupies the RNA template binding channel of EV-D68 RdRp with a novel binding pocket. Additionally, residues involved in the NADPH binding pocket of EV-D68 RdRp are highly conserved in RdRps of enteroviruses. Therefore, the enzyme activity of three RdRps from EV-D68, poliovirus, and enterovirus A71 is shown to decrease when titrated with NADPH separately in vitro. Furthermore, we identified that NADPH plays a pivotal role as an RdRp inhibitor instead of a chain terminator during restriction of RNA-dependent RNA replication. In the future, derivatives of NADPH may pave the way for novel inhibitors of RdRp through compound modification, providing potential antiviral agents for treating enteroviral infection and related diseases.

Comprehensive investigation of selectivity landscape of glycogen synthase kinase-3 inhibitors

Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today’s retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC–AUC) leave-one-out cross-validation (LOO CV). and additional 2 D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2 D similarity coefficients and distances, 3 D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3 D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2 D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2 D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors. Communicated by Ramaswamy H. Sarma

Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach

Recent work has gradually been clarifying the binding site of non-electrophilic agonists on the transient receptor potential A1 (TRPA1). This study searched for non-electrophilic TRPA1 agonists by means of in silico drug discovery techniques based on three-dimensional (3-D) protein structure. First, agonist-bound pocket structures were explored using an advanced molecular dynamics simulation starting from the cryo-electron microscopic structure of TRPA1, and several pocket structures suitable for virtual screening were extracted by structure evaluation using known non-electrophilic TRPA1 agonists. Next, 49 compounds were selected as new non-electrophilic agonist candidates from a library of natural products comprising 10,555 compounds by molecular docking toward these pocket structures. Measurement of the TRPA1 agonist activity of these compounds showed notable TRPA1 activation with three compounds (decanol, 2-ethyl-1-hexanol, phenethyl butanoate). Decanol and 2-ethyl-1-hexanol, which are categorized as fatty alcohols, in particular have a novel chemical scaffold for TRPA1 activation. The results of this study are expected to be of considerable use in understanding the molecular mechanism of TRPA1 recognition by non-electrophilic agonists.

Pose Filter-Based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists.

Farnesoid X receptor (FXR) agonists can reverse dysregulated bile acid metabolism, and thus, they are potential therapeutics to prevent and treat nonalcoholic fatty liver disease. The low success rate of FXR agonists' R&D and the side effects of clinical candidates such as obeticholic acid make it urgent to discover new chemotypes. Unfortunately, structure-based virtual screening (SBVS) that can speed up drug discovery has rarely been reported with success for FXR, which was likely hindered by the failure in addressing protein flexibility. To address this issue, we devised human FXR (hFXR)-specific ensemble learning models based on pose filters from 24 agonist-bound hFXR crystal structures and coupled them to traditional SBVS approaches of the FRED docking plus Chemgauss4 scoring function. It turned out that the hFXR-specific pose filter ensemble (PFE) was able to improve ligand enrichment significantly, which rendered 3RUT-based SBVS with its PFE the ideal approach for FXR agonist discovery. By screening of the Specs chemical library and in vitro FXR transactivation bioassay, we identified a new class of FXR agonists with compound XJ034 as the representative, which would have been missed if the PFE was not coupled. Following that, we performed in-depth biological studies which demonstrated that XJ034 resulted in a downtrend of intracellular triglyceride in vitro, significantly decreased the serum/liver TG in high fat diet-induced C57BL/6J obese mice, and more importantly, showed metabolic stabilities in both plasma and liver microsomes. To provide insight into further structure-based lead optimization, we solved the crystal structure of hFXR complexed with compound XJ034, uncovering a unique hydrogen bond between compound XJ034 and residue Y375. The current work highlights the power of our pose filter-based ensemble learning approach in terms of scaffold hopping and provides a promising lead compound for further development.

Identification of potential inhibitors against pathogenic missense mutations of PMM2 using a structure-based virtual screening approach

The autosomal recessive phosphomannomutase 2-congenital disorder of glycosylation (PMM2-CDG) is characterized by defective functioning of the PMM2 enzyme, which is necessary for the conversion of mannose-6-phosphate into mannose-1-phosphate. Here, a computational pipeline was drawn to identify the most significant mutations, and further, we used a virtual screening approach to identify a new lead compound to treat the identified significant mutations. We searched for missense mutation data related to PMM2-CDG in HGMD®, UniProt, and ClinVar. Our search yielded a total of 103 mutations, of which 91 are missense mutations. The D65Y, I132N, I132T, and F183S mutations were classified as deleterious, destabilizing, and altering the biophysical properties using the PredictSNP, iStable, and Align GVGD in silico prediction tools. Additionally, we applied a multistep protocol to screen for an alternative lead compound to the existing CID2876053 (1-(3-chlorophenyl)-3,3-bis(pyridine-2-yl)urea) with affinity to these identified significant mutants. Two compounds, CHEMBL1491007 (6-chloro-4-phenyl-3-(4-pyridin-2-ylpiperazin-1-yl)-1H-quinolin-2-one) and CHEMBL3653029 (5-chloro-4-[6-[(3-fluorophenyl)methylamino]pyridin-2-yl]-N-(piperidin-4-ylmethyl)pyridin-2-amine), exhibited the highest binding affinity with the selected mutants and were chosen for further analysis. Through molecular docking, molecular dynamics simulation, and MMPBSA analysis, we found that the known compound, i.e. CID2876053, has stronger interaction with the D65Y mutant. The newly identified lead compound CHEMBL1491007 showed stronger interaction with the I132N and I132T mutants, whereas the most deleterious mutant, F183S, showed stronger interaction with CHEMBL3653029. This study is expected to aid in the field of precision medicine, and further to in vivo and in vitro analysis of these lead compounds might shed light on the treatment of PMM2-CDG. Communicated by Ramaswamy H. Sarma

Novel Small Molecules against Two Binding Sites of Wnt2 Protein as potential Drug Candidates for Colorectal Cancer: A Structure Based Virtual Screening Approach.

Wnts are the major ligands responsible for activating Wnt signaling pathway through binding to Frizzled proteins (Fzd) as the receptors. Among these ligands, Wnt2 plays the main role in the tumorigenesis of several human cancers especially colorectal cancer (CRC). Therefore, it can be considered as a potential drug target. The aim of this study was to identify potential drug candidates against two binding sites of Wnt2. Structure-based virtual screening approaches were applied to identify compounds against binding sites of Wnt2 for inhibiting the interaction Wnt2 and Fzd receptors. The best hit compounds from molecular docking of National Cancer Institute diversity set II database were used for structural similarity search on ZINC database, obtaining large hit compounds query to perform a virtual screening and retrieving potential lead compounds. Eight lead compounds were selected while their binding affinity, binding modes interactions, and molecular dynamics simulations studies were assessed. Molecular docking studies showed that eight selected lead compounds can bind to the desired binding sites of Wnt2 in a high affinity manner. Bioavailability analysis of the selected lead compounds indicated that they possessed significant drug like properties. Thus, these lead compounds were considered as potential drug candidates for inhibiting Wnt signaling pathway through combining with the binding sites of Wnt2 and hindering the interaction of Wnt2 and Fzd receptors. Our findings suggest that Wnt2 binding sites may be a useful target for treatment for CRC fueling the future efforts for developing new compounds against Wnt signaling pathway.