Virtual Screening Workflow Research Articles

Virtual Screening and Molecular Dynamics Simulation Study of Influenza Polymerase PB2 Inhibitors.

Influenza A virus is the main cause of worldwide epidemics and annual influenza outbreaks in humans. In this study, a virtual screen was performed to identify compounds that interact with the PB2 cap-binding domain (CBD) of influenza A polymerase. A virtual screening workflow based on Glide docking was used to screen an internal database containing 8417 molecules, and then the output compounds were selected based on solubility, absorbance, and structural fingerprints. Of the 16 compounds selected for biological evaluation, six compounds were identified that rescued cells from H1N1 virus-mediated death at non-cytotoxic concentrations, with EC50 values ranging from 2.5–55.43 μM, and that could bind to the PB2 CBD of H1N1, with Kd values ranging from 0.081–1.53 μM. Molecular dynamics (MD) simulations of the docking complexes of our active compounds revealed that each compound had its own binding characteristics that differed from those of VX-787. Our active compounds have novel structures and unique binding modes with PB2 proteins, and are suitable to serve as lead compounds for the development of PB2 inhibitors. An analysis of the MD simulation also helped us to identify the dominant amino acid residues that play a key role in binding the ligand to PB2, suggesting that we should focus on increasing and enhancing the interaction between inhibitors and these major amino acids during lead compound optimization to obtain more active PB2 inhibitors.

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure–activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R2pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC50 value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets.

Consensus Virtual Screening Identified [1,2,4]Triazolo[1,5-b]isoquinolines As MELK Inhibitor Chemotypes.

Maternal Embryonic Leucine‐zipper Kinase (MELK) is a current oncotarget involved in a diverse range of human cancers, with the usage of MELK inhibitors being explored clinically. Here, we aimed to discover new MELK inhibitor chemotypes from our in‐house compound library with a consensus‐based virtual screening workflow, employing three screening concepts. After careful retrospective validation, prospective screening and in vitro enzyme inhibition testing revealed a series of [1,2,4]triazolo[1,5‐b]isoquinolines as a new structural class of MELK inhibitors, with the lead compound of the series exhibiting a sub‐micromolar inhibitory activity. The structure‐activity relationship of the series was explored by testing further analogs based on a structure‐guided selection process. Importantly, the present work marks the first disclosure of the synthesis and bioactivity of this class of compounds.

In silico identification of novel inhibitors targeting the DNA-binding domain of the human estrogen receptor alpha

The human estrogen receptor alpha (ERα) is an important regulator in breast cancer development and progression. The frequent ERα mutations in the ligand-binding domain (LBD) can increase the resistance of antiestrogen drugs, highlighting the need to develop new drugs to target ERα-positive breast cancer. In this study, we combined molecular docking, molecular dynamics simulations and binding free energy calculations to develop a structure-based virtual screening workflow to identify hit compounds capable of interfering with the recognition of ERα by the specific response element of DNA. A druggable pocket on the DNA binding domain (DBD) of ERα was identified as the potential binding site. The hits binding modes were further analyzed to reveal the structural characteristics of the DBD–inhibitor complexes. The core structure of the lead molecules was synthesized and was found to inhibit the E2-induced cell proliferation in MCF-7 cell lines. These findings provide an insight into the structural basis of ligand-ERα for alternate sites beyond the LBD-based pocket. The core structure proposed in this study could potentially be used as the lead molecule for further rational optimization of the antiestrogen drug structure with stronger binding of DBD and higher activity.

In Silico Identification of Tripeptides as Lead Compounds for the Design of KOR Ligands.

The kappa opioid receptor (KOR) represents an attractive target for the development of drugs as potential antidepressants, anxiolytics and analgesics. A robust computational approach may guarantee a reduction in costs in the initial stages of drug discovery, novelty and accurate results. In this work, a virtual screening workflow of a library consisting of ~6 million molecules was set up, with the aim to find potential lead compounds that could manifest activity on the KOR. This in silico study provides a significant contribution in the identification of compounds capable of interacting with a specific molecular target. The main computational techniques adopted in this experimental work include: (i) virtual screening; (ii) drug design and leads optimization; (iii) molecular dynamics. The best hits are tripeptides prepared via solution phase peptide synthesis. These were tested in vivo, revealing a good antinociceptive effect after subcutaneous administration. However, further work is due to delineate their full pharmacological profile, in order to verify the features predicted by the in silico outcomes.

Identification of Broad-Spectrum MMP Inhibitors by Virtual Screening.

Matrix metalloproteinases (MMPs) are the family of proteases that are mainly responsible for degrading extracellular matrix (ECM) components. In the skin, the overexpression of MMPs as a result of ultraviolet radiation triggers an imbalance in the ECM turnover in a process called photoaging, which ultimately results in skin wrinkling and premature skin ageing. Therefore, the inhibition of different enzymes of the MMP family at a topical level could have positive implications for photoaging. Considering that the MMP catalytic region is mostly conserved across different enzymes of the MMP family, in this study we aimed to design a virtual screening (VS) workflow to identify broad-spectrum MMP inhibitors that can be used to delay the development of photoaging. Our in silico approach was validated in vitro with 20 VS hits from the Specs library that were not only structurally different from one another but also from known MMP inhibitors. In this bioactivity assay, 18 of the 20 compounds inhibit at least one of the assayed MMPs at 100 μM (with 5 of them showing around 50% inhibition in all the tested MMPs at this concentration). Finally, this VS was used to identify natural products that have the potential to act as broad-spectrum MMP inhibitors and be used as a treatment for photoaging.

Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors

The inconsistencies in the performance of the virtual screening (VS) process, depending on the used software and structural conformation of the protein, is a challenging issue in the drug design and discovery field. Varying performance, especially in terms of early recognition of the potential hit compounds, negatively affects the whole process and leads to unnecessary waste of the time and resources. Appropriate application of the ensemble docking and consensus-scoring approaches can significantly increase reliability of the VS results. Dihydroorotate dehydrogenase (DHODH) is a key enzyme in the pyrimidine biosynthesis pathway. It is considered as a valuable therapeutic target in cancer, autoimmune and viral diseases. Based on the conducted benchmark study and analysis of the effect of different combinations of the applied methods and approaches, here we suggested a structure-based virtual screening (SBVS) workflow that can be used to increase the reliability of VS.

Discovery of a new series of PI3K-δ inhibitors from Virtual Screening

PI3K-δ mediates key immune cell signaling pathways and is a target of interest for treatment of oncological and immunological disorders. Here we describe the discovery and optimization of a novel series of PI3K-δ selective inhibitors. We first identified hits containing an isoindolinone scaffold using a combined ligand- and receptor-based virtual screening workflow, and then improved potency and selectivity guided by structural data and modeling. Careful optimization of molecular properties led to compounds with improved permeability and pharmacokinetic profile, and high potency in a whole blood assay.

Structural Investigations of Aroylindole Derivatives through 3D-QSAR and Multiple Pharmacophore Modeling for the Search of Novel Colchicines Inhibitor

Background : The ligand and structure based integrated strategies are being repeatedly and effectively employed for the precise search and design of novel ligands against various disease targets. Aroylindole derivative has a similar structural analogy as Combretastatin A-4, and exhibited potent anticancer activity on several cancer cell lines. Objective: To identify structural features of aroylindole derivatives through 3D-QSAR and multiple pharmacophore modelling for the search of novel colchicines inhibitor via virtual screening. Method: The present study utilizes ligand and structure based methodology for the establishment of structure activity correlation among trimethoxyaroylindole derivatives and the search of novel colchicines inhibitor via virtual screening. The 3D-QSAR studies were performed using Phase module and provided details of relationship between structure and biological activity. A single ligand based pharmacophore model was generated from Phase on compound 3 and compound 29 and three energetically optimized structure based pharmacophore models were generated from epharmacophore for co-crystallized ligand, compound 3 and compound 29 with protein PBD ID 1SA0, 5EYP and 5LYJ. These pharmacophoric features containing hit-like compounds were collected from commercially available ZINC database and screened using virtual screening workflow. Results and Discussion: The 3D-QSAR model studies with good PLSs statistics for factor four was characterized by the best prediction coefficient Q2 (0.8122), regression R2 (0.9405), SD (0.2581), F (102.7), P (1.56e-015), RMSE (0.402), Stability (0.5411) and Pearson-r (0.9397). The generated epharmacophores have GH scores over 0.5 and AUAC ≥ 0.7 indicated that all the pharmacophores were suitable for pharmacophore-based virtual screening. The virtual screened compounds ZINC12323179, ZINC01642724, and ZINC14238006 have showed similar structural alignment as co-crystallized ligand and showed the hydrogen bonding of ligand with ASN101, SER178, THR179, VAL238, CYS241 amino acid of protein. Conclusion: The study illustrates that the ligand and structure based pharmacophoric approach is beneficial for identification of structurally diverse hits, having better binding affinity on colchicines binding site as novel anticancer agents.

Current State-of-the-art In-house and Cloud-Based Applications of Virtual Polymorph Screening of Pharmaceutical Compounds: A Challenging Case ofAZD1305

We demonstrate the successful application of the state-of-the-art AstraZeneca in-house and XtalPi cloud-based virtual polymorph screening workflows in support of stable form selection of crystallin...

Discovery of novel heterocyclic amide-based inhibitors: an integrative in-silico approach to targeting soluble epoxide hydrolase

Inhibition of soluble epoxide hydrolase (sEH) is considered as an emerging druggable target to reduce blood pressure, improve insulin sensitivity, and decrease inflammation. Despite the availability of different classes of sEH small molecule inhibitors for the potential treatment of hypertension, only a few candidates have reached clinical trials, making the optimal control of blood pressure presently unattainable. This necessity motivated us to explore a series of novel quinazoline-4(3H)-one and 4,6-disubstituted pyridin-2(1H)-one derivatives targeting sEH enzyme. Herein, comprehensive computational investigations were performed to probe the inhibition efficacy of these potent compounds in terms of inhibitor-enzyme interactions against sEH. In this study, the 39 in-house with a focused library comprising 39 in-house synthesized compounds were selected. The structure-based pharmacophore modeling was developed based on the crystal structure of sEH with its co-crystallized biologically active inhibitor. The generated hypotheses were applied for virtual screening-based PHASE fitness scores. Docking-based virtual screening workflows were used to generate lead compounds using HTVS, SP and XP based GLIDE G-score values. The candidate leads were filtered using ADMET pharmacological and physicochemical properties screening. A 100-ns of molecular dynamics simulations with Molecular dynamics simulations (100 ns) were performed to explore the binding affinities of the considered compounds. Our study identified four best candidates from quinazoline-4(3H)-one derivatives, which indicated that a quinazolinone ring serves as a suitable scaffold to develop novel small molecule sEH inhibitors.

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease.

The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available.

Discovery of drug-like acetylcholinesterase inhibitors by rapid virtual screening of a 6.9million compound database.

Cholinesterase inhibitors remain the mainstay of Alzheimer's disease treatment, and the search for new inhibitors with better efficacy and side effect profiles is ongoing. Virtual screening (VS) is a powerful technique for searching large compound databases for potential hits. This study used a sequential VS workflow combining ligand-based VS, molecular docking and physicochemical filtering to screen for central nervous system (CNS) drug-like acetylcholinesterase inhibitors (AChEIs) amongst the 6.9million compounds of the CoCoCo database. Eleven in silico hits were initially selected, resulting in the discovery of an AChEI with a Ki of 3.2µM. In vitro kinetics and in silico molecular dynamics experiments informed the selection of an additional seven analogues. This led to the discovery of two further AChEIs, with Ki values of 2.9µM and 0.65µM. All three compounds exhibited reversible, mixed inhibition of acetylcholinesterase. Importantly, the in silico physicochemical filter facilitated the discovery of CNS drug-like compounds, such that all three inhibitors displayed high in vitro blood-brain barrier model permeability.

ABBV-744 as a potential inhibitor of SARS-CoV-2 main protease enzyme against COVID-19

A new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and become pandemic with thousands new deaths and infected cases globally. To address coronavirus disease (COVID-19), currently no effective drug or vaccine is available. This necessity motivated us to explore potential lead compounds by considering drug repurposing approach targeting main protease (Mpro) enzyme of SARS-CoV-2. This enzyme considered to be an attractive drug target as it contributes significantly in mediating viral replication and transcription. Herein, comprehensive computational investigations were performed to identify potential inhibitors of SARS-CoV-2 Mpro enzyme. The structure-based pharmacophore modeling was developed based on the co-crystallized structure of the enzyme with its biological active inhibitor. The generated hypotheses were applied for virtual screening based PhaseScore. Docking based virtual screening workflow was used to generate hit compounds using HTVS, SP and XP based Glide GScore. The pharmacological and physicochemical properties of the selected lead compounds were characterized using ADMET. Molecular dynamics simulations were performed to explore the binding affinities of the considered lead compounds. Binding energies revealed that compound ABBV-744 binds to the Mpro with strong affinity (ΔGbind −45.43 kcal/mol), and the complex is more stable in comparison with other protein–ligand complexes. Our study classified three best compounds which could be considered as promising inhibitors against main protease SARS-CoV-2 virus.

Biased Ligands Differentially Shape the Conformation of the Extracellular Loop Region in 5-HT2B Receptors

G protein-coupled receptors are linked to various intracellular transducers, each pathway associated with different physiological effects. Biased ligands, capable of activating one pathway over another, are gaining attention for their therapeutic potential, as they could selectively activate beneficial pathways whilst avoiding those responsible for adverse effects. We performed molecular dynamics simulations with known β-arrestin-biased ligands like lysergic acid diethylamide and ergotamine in complex with the 5-HT2B receptor and discovered that the extent of ligand bias is directly connected with the degree of closure of the extracellular loop region. Given a loose allosteric coupling of extracellular and intracellular receptor regions, we delineate a concept for biased signaling at serotonin receptors, by which conformational interference with binding pocket closure restricts the signaling repertoire of the receptor. Molecular docking studies of biased ligands gathered from the BiasDB demonstrate that larger ligands only show plausible docking poses in the ergotamine-bound structure, highlighting the conformational constraints associated with bias. This emphasizes the importance of selecting the appropriate receptor conformation on which to base virtual screening workflows in structure-based drug design of biased ligands. As this mechanism of ligand bias has also been observed for muscarinic receptors, our studies provide a general mechanism of signaling bias transferable between aminergic receptors.

Cloning, functional characterization and screening of potential inhibitors for Chilo partellus chitin synthase A using in silico, in vitro and in vivo approaches

Chitin synthase (CHS) is one of the crucial enzymes that play an essential role in chitin synthesis during the molting process, and it is considered to be the specific target to control insect pests. Currently, there are no potent inhibitors available in the market, which specifically target this enzyme. Pyrimidine nucleoside peptide, nikkomycin Z, binds to nucleotide-binding sites of fungal and insect CHS. But, their mode of action is still fragmentary due to the lack of a 3Dstructure of CHS. Chilo partellus is a severe pest insect of major food crops such as maize and sorghum, in an attempt to target integument expressed cuticular CpCHS. The CpChsA cDNA was cloned, and subsequently, their developmental and tissue-specific expression was studied. The 3D structure of the CHS catalytic domain was modeled, after which natural compounds were screened using a virtual screening workflow and resulted in the identification of five hit molecules. Molecular dynamics simulations were performed to investigate the dynamics and interactions of hits with CpCHS. The obtained results revealed that the compounds kasugamycin, rutin and robinin could act as potent inhibitors of CpCHS. All three molecules were observed to significantly reduce the chitin production as validated using in vitro and in vivo studies. Thus, this study aims to provide a set of novel inhibitor molecules against CpCHS for controlling the pest population. Communicated by Ramaswamy H. Sarma

An ABSINTH-Based Protocol for Predicting Binding Affinities between Proteins and Small Molecules.

The core task in computational drug discovery is to accurately predict binding free energies in receptor-ligand systems for large libraries of putative binders. Here, the ABSINTH implicit solvent model and force field are extended to describe small, organic molecules and their interactions with proteins. We show that an automatic pipeline based on partitioning arbitrary molecules into substructures corresponding to model compounds with known free energies of solvation can be combined with the CHARMM general force field into a method that is successful at the two important challenges a scoring function faces in virtual screening work flows: it ranks known binders with correlation values rivaling that of comparable state-of-the-art methods and it enriches true binders in a set of decoys. Our protocol introduces innovative modifications to common virtual screening workflows, notably the use of explicit ions as competitors and the integration over multiple protein and ligand species differing in their protonation states. We demonstrate the value of modifications to both the protocol and ABSINTH itself. We conclude by discussing the limitations of high-throughput implicit methods such as the one proposed here.

Anthocyanin derivatives as potent inhibitors of SARS-CoV-2 main protease: An in-silico perspective of therapeutic targets against COVID-19 pandemic

A new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and become pandemic with thousands new deaths and infected cases globally. To treat the patients with coronavirus disease (COVID-19), currently no effective drug or vaccine is available. This necessity motivated us to explore potential lead compounds based natural products targeting main protease (Mpro) enzyme of SARS-CoV-2. The Mpro enzyme plays a key role in mediating viral replication and transcription and thus being considered as an attractive drug target. Herein, comprehensive computational investigations were performed to identify new lead compounds against main protease enzyme. In this study, the candidate anthocyanin-derived compounds from PubChem database were filtered considering antiviral characteristics of anthocyanins. The structure-based pharmacophore modeling was developed based on the co-crystallized structure of the enzyme with its biological active inhibitor. The generated hypotheses were applied for virtual screening-based PHASE Screen Score. Docking based virtual screening work flow was used to generate hit compounds using HTVS, SP and XP based Glide Gscore. The obtained hit compounds were filtered using ADMET pharmacological and physicochemical properties screening. Molecular dynamics simulations were performed to explore the binding affinities of the considered compounds. Our study identified six best anthocyanin-derived natural compounds which could be used as promising lead compounds against main protease SARS-CoV-2 virus.

AutoPH4: An Automated Method for Generating Pharmacophore Models from Protein Binding Pockets.

Pharmacophore models are widely used in computational drug discovery (e.g., in the virtual screening of drug molecules) to capture essential information about interactions between ligands and a target protein. Generating pharmacophore models from protein structures is typically a manual process, but there has been growing interest in automated pharmacophore generation methods. Automation makes feasible the processing of large numbers of protein conformations, such as those generated by molecular dynamics (MD) simulations, and thus may help achieve the longstanding goal of incorporating protein flexibility into virtual screening workflows. Here, we present AutoPH4, a new automated method for generating pharmacophore models based on protein structures; we show that a virtual screening workflow incorporating AutoPH4 ranks compounds more accurately than any other pharmacophore-based virtual screening workflow for which results on a public benchmark have been reported. The strong performance of the virtual screening workflow indicates that the AutoPH4 component of the workflow generates high-quality pharmacophores, making AutoPH4 promising for use in future virtual screening workflows as well, such as ones that use conformations generated by MD simulations.

Repurposing of cefpodoxime proxetil as potent neuroprotective agent through computational prediction and in vitro validation

In recent reports, NR2B-NMDA receptor antagonists showed more research value because of its strong targeting ability and less side effects potential. In 2016, EVT-101 was reported to bind in an almost entirely new binding region of this target. Whether strikingly different binding modes can improve targeting and reduce side effects is worth studying. In our preliminary work, we explored the binding patterns of ifenprodil and EVT-101, found the key amino acids and summarized the pharmacophores, hoping to find such antagonists that target the two binding modes simultaneously. In this study, we developed a scalable virtual screening workflow in the FDA-approved drugs library to identify novel NR2B-NMDAR antagonists based on the combination of two pharmacophores. Cefpodoxime proxetil (5) was identified as the hit compound, and it was found for the first time that 5 might have neuroprotective activity as a NR2B-NMDAR antagonist. This result interested us to make further study, the ligand-receptor interactions modeled by molecular docking studies showed that the compound could perfectly merge both the pharmacophore characteristics of ifenprodil and EVT-101 at the binding cavity between the ATDs of GluN1 and GluN2B. The accuracy of molecular docking results and binding stability of ligand-receptor complexes were validated through 100 ns molecular dynamics simulation and binding free energy calculation. Afterwards, MTT assay (49.8%±0.1%, 5 μM) on NMDA injured SH-SY5Y cells and evidence of the effect on attenuating Ca2+ influx induced by NMDA were applied to validate the computational results, further investigation showed that 5 could suppress the NR2B upregulation induced by NMDA. Communicated by Ramaswamy H. Sarma