Virtual Ligand Research Articles

Discovery of a Novel Natural Allosteric Inhibitor That Targets NDM-1 Against Escherichia coli.

At present, the resistance of New Delhi metallo-β-lactamase-1 (NDM-1) to carbapenems and cephalosporins, one of the mechanisms of bacterial resistance against β-lactam antibiotics, poses a threat to human health. In this work, based on the virtual ligand screen method, we found that carnosic acid1 (CA), a natural compound, exhibited a significant inhibitory effect against NDM-1 (IC50 = 27.07 μM). Although carnosic acid did not display direct antibacterial activity, the combination of carnosic acid and meropenem still showed bactericidal activity after the loss of bactericidal effect of meropenem. The experimental results showed that carnosic acid can enhance the antibacterial activity of meropenem against Escherichia coli ZC-YN3. To explore the inhibitory mechanism of carnosic acid against NDM-1, we performed the molecular dynamics simulation and binding energy calculation for the NDM-1-CA complex system. Notably, the 3D structure of the complex obtained from molecular modeling indicates that the binding region of carnosic acid with NDM-1 was not situated in the active region of protein. Due to binding to the allosteric pocket of carnosic acid, the active region conformation of NDM-1 was observed to have been altered. The distance from the active center of the NDM-1-CA complex was larger than that of the free protein, leading to loss of activity. Then, the mutation experiments showed that carnosic acid had lower inhibitory activity against mutated protein than wild-type proteins. Fluorescence experiments verified the results reported above. Thus, our data indicate that carnosic acid is a potential NDM-1 inhibitor and is a promising drug for the treatment of NDM-1 producing pathogens.

Targeting the SARS-CoV-2 main protease using FDA-approved Isavuconazonium, a P2–P3 α-ketoamide derivative and Pentagastrin: An in-silico drug discovery approach

The SARS-CoV-2 main protease (Mpro) is an attractive target towards discovery of drugs to treat COVID-19 because of its key role in virus replication. The atomic structure of Mpro in complex with an α-ketoamide inhibitor (Lig13b) is available (PDB ID:6Y2G). Using 6Y2G and the prior knowledge that protease inhibitors could eradicate COVID-19, we designed a computational study aimed at identifying FDA-approved drugs that could interact with Mpro. We searched the DrugBank and PubChem for analogs and built a virtual library containing ∼33,000 conformers. Using high-throughput virtual screening and ligand docking, we identified Isavuconazonium, a ketoamide inhibitor (α-KI) and Pentagastrin as the top three molecules (Lig13b as the benchmark) based on docking energy. The ΔGbind of Lig13b, Isavuconazonium, α-KI, Pentagastrin was −28.1, −45.7, −44.7, −34.8 kcal/mol, respectively. Molecular dynamics simulation revealed that these ligands are stable within the Mpro active site. Binding of these ligands is driven by a variety of non-bonded interaction, including polar bonds, H-bonds, van der Waals and salt bridges. The overall conformational dynamics of the complexed-Mpro was slightly altered relative to apo-Mpro. This study demonstrates that three distinct classes molecules, Isavuconazonium (triazole), α-KI (ketoamide) and Pentagastrin (peptide) could serve as potential drugs to treat patients with COVID-19.

Differential Roles of Extracellular Histidine Residues of GPR68 for Proton-Sensing and Allosteric Modulation by Divalent Metal Ions.

GPR68, an orphan G-protein coupled receptor, senses protons, couples to multiple G-proteins, and is also activated or inhibited by divalent metal ions. It has seven extracellular histidine residues, although it is not clear how these histidine residues play a role in both proton-sensing and metal ion modulation. Here we demonstrate that divalent metal ions are allosteric modulators that can activate or inhibit proton activity in a concentration- and pH-dependent manner. We then show that single histidine mutants have differential and varying degrees of effects on proton-sensing and metal ion modulation. Some histidine residues play dual roles in proton-sensing and metal ion modulation, while others are important in one or the other but not both. Two extracellular disulfide bonds are predicted to constrain histidine residues to be spatially close to each other. Combining histidine mutations leads to reduced proton activity and resistance to metal ion modulation, while breaking the less conserved disulfide bond results in a more severe reduction in proton-sensing over metal modulation. The small-molecule positive allosteric modulators (PAMs) ogerin and lorazepam are not affected by these mutations and remain active at mutants with severely reduced proton activity or are resistant to metal ion modulation. These results suggest GPR68 possesses two independent allosteric modulation systems, one through interaction with divalent metal ions at the extracellular surface and another through small-molecule PAMs in the transmembrane domains. A new GPR68 model is developed to accommodate the findings which could serve as a template for further studies and ligand discovery by virtual ligand docking.

Abstract 662: A discovery of a small molecule mTORC1 allosteric inhibitor targeting cancer cells

Abstract The allosteric inhibition of the mechanistic target of rapamycin complex 1 (mTORC1), the central growth regulator that is highly activated in cancer cells, is unique for rapamycin and rapalogs. Upon binding to 12-kDa FK506-binding protein (FKBP12), the FKBP12-rapamycin specifically complexed with mTORC1 and inhibits the mediated downstream signals incorporated in metabolic processes. Since rapamycin has a large molecular weight and a poor solubility, it does not completely inhibit mTORC1 due to the pharmacokinetic limitations. It just shows a cytostatic effect in the treatment of a few benign cancers which results in tumors re-growth. In order to overcome these limitations, we have looked for a novel potential small molecule mTORC1 allosteric inhibitor by in silico and in vitro methods. Firstly, we carried out the in silico selection using Virtual Ligand Screening (VLS) for 105 compounds to select ligands with the high calculated scores toward FRB/FKBP12 interface as a receptor (PDB ID: 1fap). After further score calculations, we selected the top 5 compounds for the next selection step. We also prepared FKBP-rapamycin binding domain (FRB) of the mTORC1 and FKBP12, by using pET15b expression vector and BL21(DE3) E. coli. For the molecular-level protein: protein interaction (PPI) measurement, we used AlphaLISATM system (PerkinElmer, USA) using the prepared proteins. In addition, we checked the kinase activity using AlphaLISA® p-P70 S6K (T389) assay kit (PerkinElmer, USA), as well as Western blotting against p-S6 (S235/236) and p-4E-BP1 (T37/46). Finally, we performed the cell cytotoxicity assay of the selected compound against cancer and noncancer cell lines. The docking simulations showed that the residues involved in the interaction between FRB/FKBP12 binding site and rapamycin are partially overlapped with those of the screened compounds which stabilized the formation of FRB:Ligand:FKBP12 ternary complex. We also measured the inhibitory effect of the selected compounds against the ternary complex formation by rapamycin and two of them, ZNC1 and ZNC5, showed interactivity by IC50 of 30 nM and 41 nM, respectively. On the other hand, only ZNC5 promoted the formation of the ternary complex by a KD value of 1.7 nM. In addition, ZNC5 inhibited the kinase activity not only in the starvation condition by IC50 of 1.2 nM but also in the feeding-induced mTORC1 activation condition with IC50 of 3.5 nM. Moreover, it has been observed that the ZNC5 inhibited kinase activity of mTORC1 in the cancer cells while no effect on the normal cells. In this study, we discovered the ZNC5, a new allosteric inhibitor to the kinase of mTORC1, through the in silico and in vitro hybrid selection. The smaller size of ZNC5 comparing to rapamycin is probably advantageous for better pharmacokinetics in vivo. Most importantly, ZNC5 specifically inhibited the growth of cancer cells over normal cells, while no such specificity observed by rapamycin. Citation Format: Raef Shams, Yoshihiro Ito, Hideyuki Miyatake. A discovery of a small molecule mTORC1 allosteric inhibitor targeting cancer cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 662.

Computational Approaches for the Discovery of GPER Targeting Compounds.

Estrogens exert a panel of biological activities mainly through the estrogen receptors α and β, which belong to the nuclear receptor superfamily. Diverse studies have shown that the G protein-coupled estrogen receptor 1 (GPER, previously known as GPR30) also mediates the multifaceted effects of estrogens in numerous pathophysiological events, including neurodegenerative, immune, metabolic, and cardiovascular disorders and the progression of different types of cancer. In particular, GPER is implicated in hormone-sensitive tumors, albeit diverse issues remain to be deeply investigated. As such, this receptor may represent an appealing target for therapeutics in different diseases. The yet unavailable complete GPER crystallographic structure, and its relatively low sequence similarity with the other members of the G protein-coupled receptor (GPCR) family, hamper the possibility to discover compounds able to modulate GPER activity. Consequently, a reliable molecular model of this receptor is required for the design of suitable ligands. To date, convergent approaches involving structure-based drug design and virtual ligand screening have led to the identification of several GPER selective ligands, thus providing important information regarding its mode of action and function. In this survey, we summarize results obtained through computer-aided techniques devoted to the assessment of GPER ligands toward their usefulness in innovative treatments of different diseases.

Discovery of an Orally Active Small-Molecule Tumor Necrosis Factor-α Inhibitor.

Tumor necrosis factor α (TNF-α) is an important therapeutic target for rheumatoid arthritis, inflammatory bowel disease, and septic hepatitis. In this study, structure-based virtual ligand screening combined with in vitro and in vivo assays were applied. A lead compound, benpyrine, could directly bind to TNF-α and block TNF-α-trigged signaling activation. Furthermore, the endotoxemic murine model showed that benpyrine could attenuate TNF-α-induced inflammation, thereby reducing liver and lung injury. Meanwhile, administration of benpyrine by gavage significantly relieved the symptoms of collagen-induced arthritis and imiquimod-induced psoriasiform inflammation in mice. Thus, our study discovered a novel, highly specific, and orally active small-molecule TNF-α inhibitor that is potentially useful for treating TNF-α-mediated inflammatory and autoimmune disease.

Structure-Based Virtual Screening Accelerates GPCR Drug Discovery

Virtual ligand screening (VLS) against high-resolution structures of G-protein-coupled receptors (GPCRs) is likely to become the next-generation drug design approach of choice. Stein and colleagues recently demonstrated the feasibility of such an approach by discovering novel chemical scaffolds for the melatonin MT1 receptor and compounds with unique in vivo activities.

Identification and experimental confirmation of novel cGMP efflux inhibitors by virtual ligand screening of vardenafil-analogues

BackgroundClinical studies have reported overexpression of PDE5 and elevation of intracellular cyclic GMP in various types of cancer cells. ABCC5 transports cGMP out of the cells with high affinity. PDE5 inhibitors prevent both cellular metabolism and cGMP efflux by inhibiting ABCC5 as well as PDE5. Increasing intracellular cGMP is hypothesized to promote apoptosis and growth restriction in tumor cells and also has potential for clinical use in treatment of cardiovascular disease and erectile dysfunction. Vardenafil is a potent inhibitor of both PDE5 and ABCC5-mediated cGMP cellular efflux. Nineteen novel vardenafil analogs that have been predicted as potent inhibitors by VLS were chosen for tests of their ability to inhibit ATP- dependent transport of cGMP by measuring the accumulation of cyclic GMP in inside-out vesicles. AimIn this study, we investigated the ability of nineteen new compounds to inhibit ABCC5- mediated cGMP transport. We also determined the Ki values of the six most potent compounds. MethodsPreparation of human erythrocyte inside out vesicles and transport assay. ResultsKi values for six of nineteen compounds that showed more than 50 % inhibition of cGMP transport in the screening test were determined and ranged from 1.1 to 23.1 μM. One compound was significantly more potent than the positive control, sildenafil. ConclusionOur findings show that computational screening correctly identified vardenafil-analogues that potently inhibit cGMP efflux-pumps from cytosol and could have substantial clinical potential in treatment of patients with diverse disorders.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Natural chemical entities from Arisaema genus might be a promising break-through against Japanese encephalitis virus infection: a molecular docking and dynamics approach

Japanese encephalitis virus (JEV) infection affects millions of population worldwide whose incidence is increasing year by year and currently, no specific drugs are available for its treatment. However, vaccines are available for its prevention but not effective against all the clinical isolates. Thus, there is an urgent need for new chemical entities or exploration of existing molecules for its treatment. In the current study, we have undertaken virtual ligand screening (VLS) method to screen out selected phytoconstituents of Genus Arisaema against various targets (NS5, NS3 helicase, and NS2B-NS3 protease) of JEVs which exhibits vital role in replication, infection cycle and host interaction by using molecular docking followed by molecular dynamics (MD) simulations. Screened natural chemical entities displayed good binding affinity as well as optimum stability toward NS5 and NS3 helicase. Further, the drug likeliness evaluated by Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) analysis was found to be in the acceptable range. In conclusion, these natural chemical entities could be considered as promising candidates for the development of anti-JEV drugs. However, further investigation is required to confirm their exact role in JEV infection through in vitro and in vivo experiments. Communicated by Ramaswamy H. Sarma

Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods

SARS-CoV-2 has caused tens of thousands of infections and more than one thousand deaths. There are currently no registered therapies for treating coronavirus infections. Because of time consuming process of new drug development, drug repositioning may be the only solution to the epidemic of sudden infectious diseases. We systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses, predicted their structures, and built 19 structures that could be done by homology modeling. By performing target-based virtual ligand screening, a total of 21 targets (including two human targets) were screened against compound libraries including ZINC drug database and our own database of natural products. Structure and screening results of important targets such as 3-chymotrypsin-like protease (3CLpro), Spike, RNA-dependent RNA polymerase (RdRp), and papain like protease (PLpro) were discussed in detail. In addition, a database of 78 commonly used anti-viral drugs including those currently on the market and undergoing clinical trials for SARS-CoV-2 was constructed. Possible targets of these compounds and potential drugs acting on a certain target were predicted. This study will provide new lead compounds and targets for further in vitro and in vivo studies of SARS-CoV-2, new insights for those drugs currently ongoing clinical studies, and also possible new strategies for drug repositioning to treat SARS-CoV-2 infections.

Molecular modeling of the interaction of the dihydroquercetin and its metabolites with cyclooxygenase-2

Background.Dihydroquercetin (DHQ) is a natural flavonoid. It has a wide range of pharmacological effects, which includes anti-inflammatory activity. There is a gap in our knowledge about the biochemical mechanisms of the therapeutic potency implementation of this compound. This fact slows down the process of the drug development using DHQ. Molecular modeling is designed to further translate the research from the fundamental experimentation to the real clinical practice. Purpose. The study objective was to estimate DHQ as a cyclooxygenase-2 (COX-2) inhibitor by using in silico analysis.Materials and methods. The information about the COX-2 structure was obtained from the Protein Data Bank (code 5KIR). The 3D-models of DHQ were generated by using the ChemBioDraw Ultra software. Docking was carried out in the GOLD program after the corresponding validation of molecular modeling algorithms based on experimental data of X-ray diffraction analysis.Results. The design of this study is based on the rational selecting of the virtual ligand structures. It gives an opportunity to optimize the quantum-mechanical calculation. By using in silico analysis, it was shown that DHQ and some of its metabolites demonstrate ability of binding to SER353, SER530, and ARG513 of COX-2 at the catalytic site.Conclusion. Important α-amino acids for intermolecular interaction of DHQ and its metabolites with COX-2 were determined during this study. Our data can be used for the development of new antiinflammatory drugs on the base of DHQ.

Virtual Screening Techniques in Drug Discovery: Review and Recent Applications.

The discovery of bioactive molecules is an expensive and time-consuming process and new strategies are continuously searched for in order to optimize this process. Virtual Screening (VS) is one of the recent strategies that has been explored for the identification of candidate bioactive molecules. The number of new techniques and software that can be applied in this strategy has grown considerably in recent years, so, before their use, it is necessary to understand the basics an also the limitations behind each one to get the most out of them. It is also necessary to assess the real contributions of this strategy so that more significant progress can be made in the future. In this context, this review aims to discuss some important points related to VS, including the use of virtual ligand and biotarget libraries, structurebased and ligand-based VS techniques, as well as to present recent cases where this strategy was successfully applied.

Physapubescin I from husk tomato suppresses SW1990 cancer cell growth by targeting kidney-type glutaminase

Kidney-type glutaminase (KGA), catalyzing the hydrolysis of glutamine to glutamate for energy supply, is over-expressed in many cancers and has been regarded as a new therapeutic target for cancers. Physapubescin I was isolated from the fruits of the edible herb Physalis pubescens L., commonly named as “husk tomato or hairy groundcherry”, and was predicted to be a potential KGA inhibitor through structure-based virtual ligand screening. Enzyme inhibition assays, microscale thermophoresis (MST) and cellular thermal shift assay (CETSA) experiments have demonstrated the high efficiency and specificity of physapubescin I targeting KGA. EdU proliferation, Hoechst 33258 staining and cytotoxicity assays indicated that physapubescin I could inhibit cancer cell proliferation and promote apoptosis more effectively than the known KGA inhibitor, BPTES. Knockdown of KGA by siRNA reduced the inhibition of physapubescin I to SW1990 cells. Meanwhile, physapubescin I impaired glutamine metabolism in SW1990 cells with increasing intracellular level of glutamine, and correspondingly decreasing glutamate and its downstream metabolites, which may account for its inhibition of cancer cell proliferation and proapoptosis. Physapubescin I also showed significant tumor growth inhibition and low toxicity in a SW1990 xenograft mouse model. Collectively, physapubescin I may serve as a potential drug candidate or lead compound for cancer therapy by targeting KGA.

Abstract 17: Novel STAT3 inhibitors identified by Structure-Based Virtual Ligand Screening incorporating SH2 domain flexibility

Abstract Efforts to develop STAT3 inhibitors have focused on its SH2 domain, starting with short phosphotyrosylated peptides with STAT3 binding motifs, e.g. pY905LPQTV within gp130. Despite binding to STAT3 with high affinity, issues regarding stability, bioavailability, and membrane permeability of these peptides, as well as peptidomimetics such as CJ-887, have limited their further clinical development and led to increased interest in small-molecule inhibitors. Some small molecule STAT3 inhibitors have been identified using structure-based virtual ligand screening (SB-VLS). While having favorable drug-like properties, most suffer from weak binding affinities, possibly due to the high flexibility of the target domain, especially within the region involved in pY-peptide binding. We conducted molecular dynamic (MD) simulations of the SH2 domain in a complex with CJ-887, with a focus on ligand-induced protein conformation changes that increase binding affinity. We used an averaged structure from this MD trajectory as “induced-active site” receptor model for SB-VLS of 110,000 compounds in the SPEC database. Screening was followed by re-docking, re-scoring of the top 30% of hits and selection for compounds that directly interact with pY+0 binding pocket residues R609-S613, resulting in 110 initial hits, which were then tested for STAT3 targeting using a panel of cellular and biochemical assays. 24 out of 110 compounds could inhibit G-CSF-stimulated pY-STAT3 by > 50% at 10 µM with 9 compounds inhibiting by > 99%; 8/24 compounds (SPEC-29, 8, 93, 98, 106, 57, 101 and 85) inhibited STAT3-binding to EGFR pY-peptide (SPR) by 29-71% at 10 µM; 5 of these (SPEC-29, 8, 93, 98, and 106) had pY-STAT3 IC50s ranging from 2.7-19.0 µM; 3 compounds (SPEC-29, 8, and 93) potently inhibited growth of pSTAT3-high MDA-MB-468 (IC50 = 2.4-12.1 µM) and MDA-MB-231 (IC50s = 3.0-12.2 µM) cells and the growth-inhibitory abilities correlated to abilities to inhibit pY-STAT3. Thus, we used a dynamic ligand-STAT3, bound-fit docking model to screen libraries that successfully identified two hits with potent activity against STAT3 both of which comply with all of Lipinski’s “rules of five” for drug-likeness. Importantly, while most STAT3 inhibitors previously identified have negatively charged moieties, which mediate their binding to the pY+0 binding pocket but limit cell penetration, the two hits identified are uncharged and likely will serve as superior candidates for future hit-to-lead development leading to an effective direct small-molecule STAT3 inhibitor. Furthermore, the ability of the technique employed here for identifying neutral hits may also be useful in designing inhibitors targeting SH2 domains within other oncogenic targets. Citation Format: Ren Kong, Uddalak Bharadwaj, Thomas Kris Eckols, Moses Kasembeli, Mikhail Kolosov, Anh Tran, Oluwatomilona I. Ifelayo, Hong Zhao, Stephen T. Wong, David J. Tweardy. Novel STAT3 inhibitors identified by Structure-Based Virtual Ligand Screening incorporating SH2 domain flexibility [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 17.

Abstract 1169: Characterization of a small molecule inhibitor for GD3 synthase (ST8SIA1), a novel target in breast cancer stem-like cells

Abstract We reported that GD2 selectively identifies breast cancer stem-like cells (BCSCs) in TNBCs and that the enzyme ST8SIA1 regulates GD2 biosynthesis. We have shown that knockout of ST8SIA1 expression in TNBC cell lines inhibits tumor growth and metastasis in vivo, whereas ST8SIA1 overexpression induces epithelial-to-mesenchymal transition, leading to metastasis of TNBC cells. Very recently we have shown that ST8SIA1 regulates focal adhesion kinase (FAK)/AKT/mammalian target of rapamycin (mTOR) signaling in GD2+ BCSCs. Here we hypothesize that inhibition of ST8SIA1 activity using small molecule inhibitors targets BCSC function. Because of a lack of a crystal structure for developing small-molecule inhibitors of ST8SIA1, we developed a homolog of ST8SIA1 using the crystal structure of ST8SIA3 (PDB ID: 5B09) as a template. ST8SIA3 shares 32% identity and 54% similarity with this crystal structure. To this model we docked GM3, the substrate for the ST8SIA1 enzyme and analyzed their interactions. Next, we used this model to dock the compound library to identify potential inhibitors of ST8SIA1. To that end, we performed structure-based virtual screening or virtual ligand screening of 10,000 compounds based on the structure of GM3 (>70% identity), which we retrieved from the ChemSpider database. Similarly, we retrieved 1500 compounds based on the parent structure of flavone (>70% identity) from the ZINC database. After further filtration and selection of these structures, we ranked these compounds on the basis of 11 different scoring functions in DS 4.5. We selected the top candidate (ZINC02886919) for further functional characterization. ZINC02886919 had a predicted binding affinity (-logKd) of 6.17, with which we calculated the predicted Kd as 0.812 μM. Visualization analysis demonstrated that ZINC02886919 is involved in both polar and hydrophobic interactions in the binding pocket. We then synthesized ZINC02886919 and pursued pharmacological and in vitro assays. Treatment of SUM159 cells with ZINC02886919 decreased the percentage of GD2+ BCSCs by 5- to 10-fold within 72h in a concentration-dependent manner (from 31% to 6%). In addition, ZINC02886919 inhibited soft agar colony and mammosphere formation for SUM159 cells by 10- to 20-fold. These data indicated that the ST8SIA1 homolog model that we developed shares structural similarity with native human ST8SIA1 protein and that ZINC02886919 is a potential inhibitor of ST8SIA1 activity and thus BCSC function. In conclusion, inhibition of ST8SIA1 expression or activity inhibits BCSC function, TNBC tumor growth, and metastasis. The ST8SIA1 inhibitor ZINC02886919 inhibits GD2 expression and mammosphere and soft agar colony formation in vitro. The effect of ZINC02886919 on tumor growth and metastasis is currently being tested in vivo. Citation Format: Appalaraju Jappupilli, Khoa Nguyen, Stanley Ly, Michael Andreeff, Prashen Chelikani, Venkata Lokesh Battula. Characterization of a small molecule inhibitor for GD3 synthase (ST8SIA1), a novel target in breast cancer stem-like cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1169.

A benchmark study of loop modeling methods applied to G protein-coupled receptors.

G protein-coupled receptors (GPCR) are important drug discovery targets. Despite progress, many GPCR structures have not yet been solved. For these targets, comparative modeling is used in virtual ligand screening to prioritize experimental efforts. However, the structure of extracellular loop 2 (ECL2) is often poorly predicted. This is significant due to involvement of ECL2 in ligand binding for many Class A GPCR. Here we examine the performance of loop modeling protocols available in the Rosetta (cyclic coordinate descent [CCD], KIC with fragments [KICF] and next generation KIC [NGK]) and Molecular Operating Environment (MOE) software suites (de novo search). ECL2 from GPCR crystal structures served as the structure prediction targets and were divided into four sets depending on loop length. Results suggest that KICF and NGK sampled and scored more loop models with sub-angstrom and near-atomic accuracy than CCD or de novo search for loops of 24 or fewer residues. None of the methods were able to sample loop conformations with near-atomic accuracy for the longest targets ranging from 25 to 32 residues based on 1000 models generated. For these long loop targets, increased conformational sampling is necessary. The strongly conserved disulfide bond between Cys3.25 and Cys45.50 in ECL2 proved an effective filter. Setting an upper limit of 5.1Å on the S-S distance improved the lowest RMSD model included in the top 10 scored structures in Groups 1-4 on average between 0.33 and 1.27Å. Disulfide bond formation and geometry optimization of ECL2 provided an additional incremental benefit in structure quality.

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design.

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present Structure template-based ab initio ligand design solution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.

Computational screening of medicinal plant phytochemicals to discover potent pan-serotype inhibitors against dengue virus

Emergence of Dengue as one of the deadliest viral diseases prompts the need for development of effective therapeutic agents. Dengue virus (DV) exists in four different serotypes and infection caused by one serotype predisposes its host to another DV serotype heterotypic re-infection. We undertook virtual ligand screening (VLS) to filter compounds against DV that may inhibit inclusively all of its serotypes. Conserved non-structural DV protein targets such as NS1, NS3/NS2B and NS5, which play crucial role in viral replication, infection cycle and host interaction, were selected for screening of vital antiviral drug leads. A dataset of plant based natural antiviral derivatives was developed. Molecular docking was performed to estimate the spatial affinity of target compounds for the active sites of DV’s NS1, NS3/NS2B and NS5 proteins. The drug likeliness of the screened compounds was followed by ADMET analysis whereas the binding behaviors were further elucidated through molecular dynamics (MD) simulation experiments. VLS screened three potential compounds including Canthin-6-one 9-O-beta-glucopyranoside, Kushenol W and Kushenol K which exhibited optimal binding with all the three conserved DV proteins. This study brings forth novel scaffolds against DV serotypes to serve as lead molecules for further optimization and drug development against all DV serotypes with equal effect against multiple disease causing DV proteins. We therefore anticipate that the insights given in the current study could be regarded valuable towards exploration and development of a broad-spectrum natural anti-dengue therapy.

NAPOLI: A Graph-Based Strategy to Detect and Visualize Conserved Protein-Ligand Interactions in Large-Scale.

Essential roles in biological systems depend on protein-ligand recognition, which is mostly driven by specific non-covalent interactions. Consequently, investigating these interactions contributes to understanding how molecular recognition occurs. Nowadays, a large-scale data set of protein-ligand complexes is available in the Protein Data Bank, what led several tools to be proposed as an effort to elucidate protein-ligand interactions. Nonetheless, there is not an all-in-one tool that couples large-scale statistical, visual, and interactive analysis of conserved protein-ligand interactions. Therefore, we propose nAPOLI (Analysis of PrOtein-Ligand Interactions), a web server that combines large-scale analysis of conserved interactions in protein-ligand complexes at the atomic-level, interactive visual representations, and comprehensive reports of the interacting residues/atoms to detect and explore conserved non-covalent interactions. We demonstrate the potential of nAPOLI in detecting important conserved interacting residues through four case studies: two involving a human cyclin-dependent kinase 2 (CDK2), one related to ricin, and other to the human nuclear receptor subfamily 3 (hNR3). nAPOLI proved to be suitable to identify conserved interactions according to literature, as well as highlight additional interactions. Finally, we illustrate, with a virtual screening ligand selection, how nAPOLI can be widely applied in structural biology and drug design. nAPOLI is freely available at bioinfo.dcc.ufmg.br/napoli/.