Academic literature on the topic 'Virtual ligand screen'

Employing ensemble Protein-Ligand Interaction Fingerprints (ensPLIF) as descriptors in post retrospective Structure-Based Virtual Screening (SBVS) campaigns Quantitative Structure-Activity Relationship (QSAR) analysis has been proven to significantly increase the predictive ability in the identification of potent ligands for estrogen receptor alpha (ERα). In the research presented in this article, similar approaches have been performed to construct and retrospectively validate an SBVS protocol to identify marginal ligands for ERα. Based on both validated SBVS protocols, a graphical-user-interface (GUI) application to identify if a compound is a non-, moderate or potent ligand for ERα was developed. The GUI application was subsequently used to virtually screen genistin, genistein, daidzin, and daidzein, followed by in vitro test employing a cytotoxic assay using 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) method.

The ubiquitous AAA+ ATPase p97 functions as a dynamic molecular machine driving several cellular processes. It is essential in regulating protein homeostasis, and it represents a potential drug target for cancer, particularly when there is a greater reliance on the endoplasmic reticulum–associated protein degradation pathway and ubiquitin–proteasome pathway to degrade an overabundance of secreted proteins. Here, we report a case study for using fragment-based ligand design approaches against this large and dynamic hexamer, which has multiple potential binding sites for small molecules. A screen of a fragment library was conducted by surface plasmon resonance (SPR) and followed up by nuclear magnetic resonance (NMR), two complementary biophysical techniques. Virtual screening was also carried out to examine possible binding sites for the experimental hits and evaluate the potential utility of fragment docking for this target. Out of this effort, 13 fragments were discovered that showed reversible binding with affinities between 140 µM and 1 mM, binding stoichiometries of 1:1 or 2:1, and good ligand efficiencies. Structural data for fragment–protein interactions were obtained with residue-specific [U-2H] 13CH3-methyl-labeling NMR strategies, and these data were compared to poses from docking. The combination of virtual screening, SPR, and NMR enabled us to find and validate a number of interesting fragment hits and allowed us to gain an understanding of the structural nature of fragment binding.

Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual screening campaigns with a view to repurposing known drugs as potential allosteric (and/or orthosteric) ligands for the hM2 muscarinic subtype which was indeed resolved in complex with an allosteric modulator thus allowing a precise identification of this binding cavity. First, a docking protocol was developed and optimized based on binding space concept and enrichment factor optimization algorithm (EFO) consensus approach by using a purposely collected database including known allosteric modulators. The so-developed consensus models were then utilized to virtually screen the DrugBank database. Based on the computational results, six promising molecules were selected and experimentally tested and four of them revealed interesting affinity data; in particular, dequalinium showed a very impressive allosteric modulation for hM2. Based on these results, a second campaign was focused on bis-cationic derivatives and allowed the identification of other two relevant hM2 ligands. Overall, the study enhances the understanding of the factors governing the hM2 allosteric modulation emphasizing the key role of ligand flexibility as well as of arrangement and delocalization of the positively charged moieties.

COX-2 is a type of Non-steroidal mitigating drug (NSAID) that legitimately targets COX-2, a protein liable for irritation and torment. Selectivity for COX-2 decreases the danger of peptic ulceration and is the fundamental component of celecoxib, rofecoxib and different individuals from this medication class. COX-2 selectivity doesn't appear to influence other antagonistic impacts of NSAIDs (most prominently an expanded danger of renal disappointment), and a few outcomes have excited the doubt that there may be an expansion in danger for cardiovascular failure, apoplexy and stroke by a relative increment in thromboxane. The target of this investigation is to screen drug-like compounds from Zinc database and to predict the potency and activity by using Virtual Screening and Molecular Docking Study. The scope of the study extends to predict the feasibility of the compounds for Drug development. Hence, this examination expresses the significance of little particle libraries and their utilization to upgrade drug revelation measure earlier amalgamation. This way to deal with screen original mixes as COX-2 inhibitors from ZINC information base relies upon different boundaries, for example, Lipinski's standard of 5, pharmacophoric bunches appended on the ligand, size of the dataset and compound libraries among others. Additional, exertion can be stretched out to consider the receptor-ligand associations tentatively, and assessment of their organic action would help in planning mixes dependent on simulated screening strategies.

The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.

The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.

Background The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has ravaged lives across the globe since December 2019, and new cases are still on the rise. Peoples’ ongoing sufferings trigger scientists to develop safe and effective remedies to treat this deadly viral disease. While repurposing the existing FDA-approved drugs remains in the front line, exploring drug candidates from synthetic and natural compounds is also a viable alternative. This study employed a comprehensive computational approach to screen inhibitors for SARS-CoV-2 3CL-PRO (also known as the main protease), a prime molecular target to treat coronavirus diseases. Methods We performed 100 ns GROMACS molecular dynamics simulations of three high-resolution X-ray crystallographic structures of 3CL-PRO. We extracted frames at 10 ns intervals to mimic conformational diversities of the target protein in biological environments. We then used AutoDock Vina molecular docking to virtual screen the Sigma–Aldrich MyriaScreen Diversity Library II, a rich collection of 10,000 druglike small molecules with diverse chemotypes. Subsequently, we adopted in silico computation of physicochemical properties, pharmacokinetic parameters, and toxicity profiles. Finally, we analyzed hydrogen bonding and other protein-ligand interactions for the short-listed compounds. Results Over the 100 ns molecular dynamics simulations of 3CL-PRO’s crystal structures, 6LZE, 6M0K, and 6YB7, showed overall integrity with mean Cα root-mean-square deviation (RMSD) of 1.96 (±0.35) Å, 1.98 (±0.21) Å, and 1.94 (±0.25) Å, respectively. Average root-mean-square fluctuation (RMSF) values were 1.21 ± 0.79 (6LZE), 1.12 ± 0.72 (6M0K), and 1.11 ± 0.60 (6YB7). After two phases of AutoDock Vina virtual screening of the MyriaScreen Diversity Library II, we prepared a list of the top 20 ligands. We selected four promising leads considering predicted oral bioavailability, druglikeness, and toxicity profiles. These compounds also demonstrated favorable protein-ligand interactions. We then employed 50-ns molecular dynamics simulations for the four selected molecules and the reference ligand 11a in the crystallographic structure 6LZE. Analysis of RMSF, RMSD, and hydrogen bonding along the simulation trajectories indicated that S51765 would form a more stable protein-ligand complexe with 3CL-PRO compared to other molecules. Insights into short-range Coulombic and Lennard-Jones potentials also revealed favorable binding of S51765 with 3CL-PRO. Conclusion We identified a potential lead for antiviral drug discovery against the SARS-CoV-2 main protease. Our results will aid global efforts to find safe and effective remedies for COVID-19.

Sigma-1 receptor (S1R) is an intracellular, multi-functional, ligand operated protein that also acts as a chaperone. It is considered as a pluripotent drug target in several pathologies. The publication of agonist and antagonist bound receptor structures has paved the way for receptor-based in silico drug design. However, recent studies on this subject payed no attention to the structural differences of agonist and antagonist binding. In this work, we have developed a new ensemble docking-based virtual screening protocol utilizing both agonist and antagonist bound S1R structures. This protocol was used to screen our in-house compound library. The S1R binding affinities of the 40 highest ranked compounds were measured in competitive radioligand binding assays and the sigma-2 receptor (S2R) affinities of the best S1R binders were also determined. This way three novel high affinity S1R ligands were identified and one of them exhibited a notable S1R/S2R selectivity.

Inflammation has been very evident in infectious diseases, but in recent times research has increasingly shown that a range of non-infectious diseases may present with inflammatory conditions. This fact becomes important as new anti-inflammatory drugs emerge with different targets for treatment of diseases. Virtual screening (VS) involves applying computational methods to discover new ligands for biological structures from the formation of large libraries composed of a large number of compounds. This review aims to report several studies employing a variety of VS: ligand-based and structure-based VS are being used more frequently in combination to decrease the probability of choosing false positive candidates. There are also studies that use only one approach. Docking is widely employed as structure-based VS methodology, however pharmacophore models based on the structure are becoming more prevalent. Molecular dynamics simulations, despite their computational cost, are still utilized to validate docking scores and analyze the stability of the complex ligand-structure. It is important to note that several studies employed several drug-like rules to screen structures, as well, decoys and PAINS to validate the models. Natural product databases, despite the lower number of the compounds compared to other databases that are available, are commonly referred to as a source of drug-like molecules. There is a literal explosion of software being released for a variety of purposes and several of them are free tools and/or web tools. Overall, VS studies are nowadays a normal part of medicinal chemistry to determine novel potential inhibitors for targets of inflammatory diseases.

The process of drug discovery is very slow and expensive. There is a need for reliable in silico methods; however the performance of these methods differs.

This work presents a targeted study on how the drug discovery methods used in OpenEye’s tools ROCS, EON and FRED perform on targets with small ligands. It was examined if 12 compounds (markers) somewhat similar to AMP could be detected by ROCS in a random data set comprised of 1000 compounds. It was also examined if EON could find any electrostatic similarities between the queries and the markers. The performance of FRED with respect to re-generation of bound ligand modes was examined on ten different protein/ligand complexes from the Brookhaven Protein Data Bank. It was also examined if FRED is suitable as a screening tool since several other docking methods are used in such a way. Finally it was also examined if it was possible to reduce the time requirements of ROCS when running multiconformer queries by using a combination of single conformer queries coupled with multiconformer queries.

The conclusions that could be drawn from this project were that FRED is not a good screening tool, but ROCS performs well as such. It was also found that the scoring functions are the weak spots of FRED. EON is probably very sensitive to the conformers used but can in some cases strengthen the results from ROCS. A novel and simple way to reduce the time complexity with multiconformer queries to ROCS was discovered and was shown to work well.

The Aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor and member of the basic helix-loop-helix Per/ARNT/Sim (bHLH/PAS) family of chemosensors and developmental regulators. As a member of the PAS domain family of transcription factors responsive to exogenous signals, the AhR exerts influence on many processes relating to cellular fate. The activation of AhR is widely associated with toxic endpoints related to dioxin exposure. However, the AhR also activates endogenous gene programs related to development, cellular growth, and differentiation. The AhR is able to bind a variety of ligands, leading to a wide range of biological outcomes. Recent reports have shown that the AhR can mediate tumor suppressive effects. As a ligand-activated transcription factor, the AhR has the potential to actuate a variety of transcriptional programs that are dependent on the AhR ligand. Our central hypothesis is that AhR ligands can be identified that are capable of initiating tumor suppressive functions of the AhR. We utilized complementary cell-based and in silico virtual screening approaches to identify potential AhR ligands. We developed homology models of the AhR ligand-binding domain (LBD) for virtual ligand screening (VLS) of small molecule libraries. This led to the identification of new AhR ligands 5,7- dihydroxyflavanone!and 5-hydroxy-7-methoxyflavone. Additional small molecule libraries were screened in parallel that led to identification of flutamide as a putative AhR ligand. Flutamide is clinically approved for the treatment of prostate cancer due to its ability to antagonize androgen receptor mediated transcription. We investigated the biological effects of flutamide in AhR positive cancer cells that do not express the androgen receptor and found that flutamide inhibited the growth of HepG2 cells. Suppression of AhR expression reversed the anti-proliferative effects of flutamide. We tested 15 structural analogs of flutamide, including the flutamide metabolite 2-hydroxyflutamide for activation of AhR transcriptional activity. Flutamide is unique in its ability to activate the AhR, and suppresses hepatoma cell growth. These data suggests that flutamide-induced AhR transcriptional activity is required to initiate the tumor suppressive effects. We examined changes in cell cycle checkpoint proteins after flutamide treatment and discovered increased expression of cell cycle inhibitory proteins p27[superscript Kip1] and p15[superscript INK]. We also found that transforming Growth Factor β1 (TGFβ1), which regulates both p27[superscript Kip1] and p15[superscript INK], is upregulated by flutamide. We demonstrate that TGFβ1 is upregulated by flutamide in an AhR-dependent manner and is required for suppression of proliferation by flutamide. We identify specific and unique transcriptional signatures of the AhR upon activation by flutamide, that are distinct from the potent AhR agonist 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD). In summary, we characterize flutamide as an AhR ligand and demonstrate its AhR-dependent tumor suppressive effects in hepatoma cells. We provide the first direct evidence that AhR regulates TGFβ signaling in a ligand dependent manner. We demonstrate that the AhR-induced downstream transcriptional signature and subsequent biological effects are specific to the AhR ligand. Our studies have broad impact for characterizing the AhR as a new therapeutic target in hepatocellular carcinoma.
Graduation date: 2013
Access restricted to the OSU Community at author's request from March 28, 2013 - March 28, 2015