Virtual Screening Campaign Research Articles

Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays.

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.

Identification of free fatty acid receptor 2 agonists using virtual screening

Structure- and ligand-based virtual-screening methods (docking, 2D- and 3D-similarity searching) were analyzed for their effectiveness in virtual screening against FFAR2. To evaluate the performance of these methods, retrospective virtual screening was performed. Statistical quality of the methods was evaluated by BEDROC and RIE. The results revealed that electrostatic similarity search protocol using EON (ET combo) outperformed all other protocols with outstanding enrichment of >95% in top 1% and 2% of the dataset with an AUC of 0.958. Interestingly, the hit lists that are obtained from different virtual-screening methods are generally highly complementary to hits found from electrostatic similarity searching. These results suggest that considering electrostatic similarity searching first increases the chance of identifying more (and more diverse) active compounds from a virtual-screening campaign. Accordingly, prospective virtual screening using electrostatic similarity searching was used to identify novel FFAR2 ligands. The discovered compounds provide new chemical matter starting points for the initiation of a medicinal chemistry campaign.

PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS.

We describe here our tool named PyPLIF HIPPOS, which was newly developed to analyze the docking results of AutoDock Vina and PLANTS. Its predecessor, PyPLIF (https://github.com/radifar/pyplif), is a molecular interaction fingerprinting tool for the docking results of PLANTS, exclusively. Unlike its predecessor, PyPLIF HIPPOS speeds up the computational times by separating the reference generation and docking analysis. PyPLIF HIPPOS also offers more options compared to PyPLIF. PyPLIF HIPPOS for Linux is stored as the Supporting Information in this application note and can be accessed in GitHub (https://github.com/radifar/PyPLIF-HIPPOS). Additionally, we present here the application of the tool in a retrospective structure-based virtual screening campaign targeting neuraminidase.

Machine learning classification can reduce false positives in structure-based virtual screening

With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that aims to generate highly compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework. In retrospective benchmarks, our classifier shows outstanding performance relative to other scoring functions. In a prospective context, nearly all candidate inhibitors from a screen against acetylcholinesterase show detectable activity; beyond this, 10 of 23 compounds have IC50 better than 50 μM. Without any medicinal chemistry optimization, the most potent hit has IC50 280 nM, corresponding to Ki of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.

Rigorous Free Energy Simulations in Virtual Screening.

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

Molecular dynamics, MM/PBSA and in vitro validation of a novel quinazoline-based EGFR tyrosine kinase inhibitor identified using structure-based in silico screening

EGFR-TK has been a target strongly associated with the development of NSCLCs. A structure-based virtual screening campaign was launched against EGFR-TK by virtual screening a 3D library of 167 commercially available small molecules downloaded from ChemBridge Corporation. The virtual screen identified 12 virtual hit molecules, which were biologically evaluated against an EGFR-TK inhibitor-sensitive NSCLC cell line, A549. A quinazoline-based molecule 1, was most active and displayed ∼58% cytotoxicity at 20 μM single dose. The mode of cell death suggests molecule 1 induced apoptosis, which is characteristic of EGFR-TK pathway inhibition. A 50 ns MD simulation was conducted on three different systems: free EGFR-TK, molecule 1 complexed to EGFR-TK, and the positive control, lapatinib, complexed to EGFR-TK. The MD simulations showed increase in stabilisation of the EGFR-TK structure for the complexed systems, i.e., lower RMSDs and RMSFs for complexed EGFR-TK structures compared to the free EGFR-TK system. The binding affinities were estimated using MM/PBSA in the last 10 ns of the MD simulation that revealed comparable binding free energies between molecule 1 and lapatinib, ΔGbind = −25.0 and −23.9 kcal/mol, respectively. Per residue binding free energy decomposition studies revealed non-polar interactions contributed mostly to the binding free energies. Residues Leu718, Arg841 and Phe856 were predicted to contribute most to the binding free energies for molecule 1.

Hit Identification of New Potent PqsR Antagonists as Inhibitors of Quorum Sensing in Planktonic and Biofilm Grown Pseudomonas aeruginosa.

Current treatments for Pseudomonas aeruginosa infections are becoming less effective because of the increasing rates of multi-antibiotic resistance. Pharmacological targeting of virulence through inhibition of quorum sensing (QS) dependent virulence gene regulation has considerable therapeutic potential. In P. aeruginosa, the pqs QS system regulates the production of multiple virulence factors as well as biofilm maturation and is a promising approach for developing antimicrobial adjuvants for combatting drug resistance. In this work, we report the hit optimisation for a series of potent novel inhibitors of PqsR, a key regulator of the pqs system, bearing a 2-((5-methyl-5H-[1,2,4]triazino[5,6-b]indol-3-yl)thio) acetamide scaffold. The initial hit compound 7 (PAO1-L IC50 0.98 ± 0.02 μM, PA14 inactive at 10 μM) was obtained through a virtual screening campaign performed on the PqsR ligand binding domain using the University of Nottingham Managed Chemical Compound Collection. Hit optimisation gave compounds with enhanced potency against strains PAO1-L and PA14, evaluated using P. aeruginosa pqs-based QS bioreporter assays. Compound 40 (PAO1-L IC50 0.25 ± 0.12 μM, PA14 IC50 0.34 ± 0.03 μM) is one of the most potent PqsR antagonists reported showing significant inhibition of P. aeruginosa pyocyanin production and pqs system signaling in both planktonic cultures and biofilms. The co-crystal structure of 40 with the PqsR ligand binding domain revealed the specific binding interactions occurring between inhibitor and this key regulatory protein.

Tunable approximations to control time-to-solution in an HPC molecular docking Mini-App

The drug discovery process involves several tasks to be performed in vivo, in vitro and in silico. Molecular docking is a task typically performed in silico. It aims at finding the three-dimensional pose of a given molecule when it interacts with the target protein binding site. This task is often done for virtual screening a huge set of molecules to find the most promising ones, which will be forwarded to the later stages of the drug discovery process. Given the huge complexity of the problem, molecular docking cannot be solved by exploring the entire space of the ligand poses. State-of-the-art approaches face the problem by sampling the space of the ligand poses to generate results in a reasonable time budget. In this work, we improve the geometric approach to molecular docking by introducing tunable approximations. In particular, we analysed and enriched the original implementation with tunable software knobs to explore and control the performance-accuracy trade-offs. We modelled time-to-solution of the virtual screening task as a function of software knobs, input data features, and available computational resources. Therefore, the application can autotune its configuration according to a user-defined time budget. We used a Mini-App derived by LiGenDock—a state-of-the-art molecular docking application—to validate the proposed approach. We run the enhanced Mini-App on a high-performance computing system by using a very large database of pockets and ligands. The proposed approach exposes a time-to-solution interval spanning more than one order of magnitude with accuracy degradation up to 30%, more in general providing different accuracy levels according to the needs of the virtual screening campaign.

Application of MM-PBSA Methods in Virtual Screening.

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

Targeting the KRAS oncogene: Synthesis, physicochemical and biological evaluation of novel G-Quadruplex DNA binders

The oncogene KRAS is involved in the pathogenesis of many tumors such as pancreatic, lung and colorectal cancers, thereby representing a relevant target for the treatment of these diseases. The KRAS P1 promoter contains a nuclease hypersensitive, guanine-rich sequence able to fold into a G-quadruplex motif (G4). The stabilization of this G4 structure by small molecules is emerging as a feasible approach to downregulate KRAS expression. Here, a set of novel stabilizing molecules was identified through a virtual screening campaign on the NMR structure of the 22-mer KRAS G4. The most promising hits were then submitted to structure-activity relationships studies which allowed improving their binding affinity and selectivity over double helix DNA and different G4 topologies. The best derivative (19) underwent fluorescence titration experiments and further computational studies to disclose its binding mechanism to KRAS G4. Finally, biological assays showed that this compound is capable to reduce the viability of colorectal cancer cells in which mutated KRAS acts as a driver oncogene. Thus, 19 might represent the prototype of a new class of drugs for the treatment of tumors that, expressing mutated forms of KRAS, are refractory to current therapeutic regimens.

A Focus on Unusual ECL2 Interactions Yields β2 -Adrenergic Receptor Antagonists with Unprecedented Scaffolds.

The binding pockets of aminergic G protein‐coupled receptors are often targeted by drugs and virtual screening campaigns. In order to find ligands with unprecedented scaffolds for one of the best‐investigated receptors of this subfamily, the β2‐adrenergic receptor, we conducted a docking‐based screen insisting that molecules would address previously untargeted residues in extracellular loop 2. We here report the discovery of ligands with a previously undescribed coumaran‐based scaffold. Furthermore, we provide an analysis of the added value that X‐ray structures in different conformations deliver for such docking screens.

Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on The hTRPM8 Channel.

Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 μs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen™ provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets.

Novel indole derivatives targeting HuR-mRNA complex to counteract high glucose damage in retinal endothelial cells

The ELAVL1 (or human antigen R - HuR) RNA binding protein stabilizes the mRNA, with an AU-rich element, of several genes such as growth factors (i.e. VEGF) and inflammatory cytokines (i.e. TNFα). We hereby carried out a virtual screening campaign in order to identify and test novel HuR-mRNA disruptors. Best-scored compounds were tested in an in-vitro model of diabetic retinopathy, namely human retinal endothelial cells (HRECs) challenged with high-glucose levels (25 mM). HuR, VEGF and TNFα protein contents were evaluated by western-blot analysis in total cell lysates. VEGF and TNFα released from HRECs were measured in cell medium by ELISA. We found that two derivatives bearing indole moiety, VP12/14 and VP12/110, modulated HuR expression and decreased VEGF and TNF-α release by HREC exposed to high glucose (HG) levels. VP12/14 and VP12/110 inhibited VEGF and TNF-α release in HRECs challenged with high glucose levels, similarly to dihydrotanshinone (DHTS), a small molecule known to interfere with HuR- TNFα mRNA binding. The present findings demonstrated that VP12/14 and VP12/110 are innovative molecules with anti-inflammatory and anti-angiogenic properties, suggesting their potential use as novel candidates for treatment of diabetic retinopathy.

Targeting mPGES-1 by a Combinatorial Approach: Identification of the Aminobenzothiazole Scaffold to Suppress PGE2 Levels.

Microsomal prostaglandin E2 synthase-1 (mPGES-1), the terminal enzyme responsible for the production of inducible prostaglandin E2, has become an attractive target for the treatment of inflammation and cancer pathologies. Starting from an aminobenzothiazole scaffold, used as an unprecedented chemical core for mPGES-1 inhibition, a Combinatorial Virtual Screening campaign was conducted, using the X-ray crystal structure of human mPGES-1. Two combinatorial libraries (6 × 104) were obtained by decorating the aminobenzothiazole scaffold with all acyl chlorides and boronates available at the Merck database. The scientific multidisciplinary approach included virtual screening workflow, synthesis, and biological evaluation and led to the identification of three novel aminobenzothiazoles 1, 3, and 13 acting as mPGES-1 inhibitors. The three disclosed hits are able to inhibit mPGES-1 in a cell-free system (IC50 = 1.4 ± 0.2, 0.7 ± 0.1, and 1.7 ± 0.2 μM, respectively), and all are endowed with antitumoral properties against A549 human cancer cell lines at micromolar concentrations (28.5 ± 1.1, 18.1 ± 0.8, and 19.2 ± 1.3 μM, respectively).

Small Molecule Inhibitors of Middle East Respiratory Syndrome Coronavirus Fusion by Targeting Cavities on Heptad Repeat Trimers.

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a newly emerging viral disease with fatal outcomes. However, no MERS-CoV-specific treatment is commercially available. Given the absence of previous structure-based drug discovery studies targeting MERS-CoV fusion proteins, this set of compounds is considered the first generation of MERS-CoV small molecule fusion inhibitors. After a virtual screening campaign of 1.56 million compounds followed by cell-cell fusion assay and MERS-CoV plaques inhibition assay, three new compounds were identified. Compound numbers 22, 73, and 74 showed IC50 values of 12.6, 21.8, and 11.12 μM, respectively, and were most effective at the onset of spike-receptor interactions. The compounds exhibited safe profiles against Human embryonic kidney cells 293 at a concentration of 20 μM with no observed toxicity in Vero cells at 10 μM. The experimental results are accompanied with predicted favorable pharmacokinetic descriptors and drug-likeness parameters. In conclusion, this study provides the first generation of MERS-CoV fusion inhibitors with potencies in the low micromolar range.

Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor Type.

By using an ensemble-docking strategy, we undertook a large-scale virtual screening campaign in order to identify new putative hits against the MET kinase target. Following a large molecular dynamics sampling of its conformational space, a set of 45 conformers of the kinase was retained as docking targets to take into account the flexibility of the binding site moieties. Our screening funnel started from about 80,000 chemical compounds to be tested in silico for their potential affinities towards the kinase binding site. The top 100 molecules selected—thanks to the molecular docking results—were further analyzed for their interactions, and 25 of the most promising ligands were tested for their ability to inhibit MET activity in cells. F0514-4011 compound was the most efficient and impaired this scattering response to HGF (Hepatocyte Growth Factor) with an IC of 7.2 M. Interestingly, careful docking analysis of this molecule with MET suggests a possible conformation halfway between classical type-I and type-II MET inhibitors, with an additional region of interaction. This compound could therefore be an innovative seed to be repositioned from its initial antiviral purpose towards the field of MET inhibitors. Altogether, these results validate our ensemble docking strategy as a cost-effective functional method for drug development.

Computational approaches for drug discovery against trypanosomatid-caused diseases.

During three decades, only about 20 new drugs have been developed for malaria, tuberculosis and all neglected tropical diseases (NTDs). This critical situation was reached because NTDs represent only 10% of health research investments; however, they comprise about 90% of the global disease burden. Computational simulations applied in virtual screening (VS) strategies are very efficient tools to identify pharmacologically active compounds or new indications for drugs already administered for other diseases. One of the advantages of this approach is the low time-consuming and low-budget first stage, which filters for testing experimentally a group of candidate compounds with high chances of binding to the target and present trypanocidal activity. In this work, we review the most common VS strategies that have been used for the identification of new drugs with special emphasis on those applied to trypanosomiasis and leishmaniasis. Computational simulations based on the selected protein targets or their ligands are explained, including the method selection criteria, examples of successful VS campaigns applied to NTDs, a list of validated molecular targets for drug development and repositioned drugs for trypanosomatid-caused diseases. Thereby, here we present the state-of-the-art of VS and drug repurposing to conclude pointing out the future perspectives in the field.

Integrating Electrochemical and Statistical Analysis Tools for Molecular Design and Mechanistic Understanding

Medicinal chemistry campaigns set the foundation for streamlined molecular design strategies through the development of quantitative structure-activity models. Our group's enduring underlying interest in reaction mechanism propelled our adaption of a similar strategy to unite mechanistic interrogation and catalyst optimization by relating reaction outputs to molecular descriptors. Through collaborative opportunities, we have recently expanded these predictive statistical modeling tools to electrocatalysis and the design of redox-active organic molecules for application as electrolytes in nonaqueous redox flow batteries. Utilizing small, strategically designed data sets for a given core structure, we develop predictive statistical models that enable rapid virtual screening campaigns to identify analogues with enhanced properties. This process relates structural parameters to the output of interest, providing insight into the structural features that influence the output under study. Furthermore, the weighting of the coefficients for each parameter in the model can furnish mechanistic insight. Such a synergistic implementation of experimental and computational tools for mechanistic insight provides a means of forecasting properties of analogues without necessitating the synthesis and analysis of each molecule of interest. Through collaborative efforts, we have demonstrated the effectiveness of these tools for predicting diverse outputs such as stability, redox potential, and nonaqueous solubility. In this Account, we outline our entry into the field of organic electrochemistry and the implementation of statistical modeling tools for designing organic electrolytes. Through these projects we were exposed to the power of electrochemical techniques as a mechanistic tool, which has provided access to critical information that would otherwise be difficult to obtain. Utilizing electroanalytical techniques, we have quantified the rates of disproportionation of a variety of cobalt complexes and developed statistical models that provide critical insight into understanding of fundamental processes involved in the disproportionation of organometallic complexes. Electroanalytical tools have also been effective in elucidating the active catalyst oxidation state in different catalytic organometallic systems for C-H functionalization. Thus, our foray into electrolyte design and electrocatalysis, in which the statistical modeling tools developed for mechanistic insight were applied in a new context, came full circle to the core foundation of our group: mechanistic understanding.

Virtual screening identification and chemical optimization of substituted 2-arylbenzimidazoles as new non-zinc-binding MMP-2 inhibitors

Matrix metalloproteinases (MMPs) are a large family of zinc-dependent endoproteases known to exert multiple regulatory roles in tumor progression and invasiveness. This encouraged over the years the approach of MMP, and particularly MMP-2, targeting for anticancer treatment. Early generations of MMP inhibitors, based on aspecific zinc binding groups (ZBGs) assembled on (pseudo)peptide scaffolds, have been discontinued due to the clinical emergence of toxicity and further drawbacks, giving the way to inhibitors with alternative zinc-chelator moieties or not binding the catalytic zinc ion.In the present paper, we continue the search for new non-zinc binding MMP-2 inhibitors: exploiting previously identified compounds, a virtual screening (VS) campaign was carried out and led to the identification of a new class of ligands. The structure-activity relationship (SAR) of the benzimidazole scaffold was explored by synthesis of several analogues whose inhibition activity was tested with enzyme inhibition assays. By performing the molecular simplification approach, we disclosed different sets of single-digit micromolar inhibitors of MMP-2, with up to a ten-fold increase in inhibitory activity and ameliorated selectivity towards off-target MMP-8, compared to selected lead compound. Molecular dynamics calculations conducted on complexes of MMP-2 with docked privileged structures confirmed that analyzed inhibitors avoid targeting the zinc ion and dip inside the S1′ pocket. Present results provide a further enrichment of our insights for the design of novel MMP-2 selective inhibitors.

Machine Learning Models Based on Molecular Fingerprints and an Extreme Gradient Boosting Method Lead to the Discovery of JAK2 Inhibitors.

Developing Janus kinase 2 (JAK2) inhibitors has become a significant focus for small-molecule drug discovery programs in recent years because the inhibition of JAK2 may be an effective approach for the treatment of myeloproliferative neoplasm. Here, based on three different types of fingerprints and Extreme Gradient Boosting (XGBoost) methods, we developed three groups of models in that each group contained a classification model and a regression model to accurately acquire highly potent JAK2 kinase inhibitors from the ZINC database. The three classification models resulted in Matthews correlation coefficients of 0.97, 0.94, and 0.97. Docking methods including Glide and AutoDock Vina were employed to evaluate the virtual screening effectiveness of our classification models. The R2 of three regression models were 0.80, 0.78, and 0.80. Finally, 13 compounds were biologically evaluated, and the results showed that the IC50 values of six compounds were identified to be less than 100 nM. Among them, compound 9 showed high activity and selectivity in that its IC50 value was less than 1 nM against JAK2 while 694 nM against JAK3. The strategy developed may be generally applicable in ligand-based virtual screening campaigns.