Multistep Virtual Screening Research Articles

Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study

ABSTRACT The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure–activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion – toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2.

Identification of selective mtbDHFR inhibitors by virtual screening and experimental approaches.

mtbDHFR-targeting inhibition has become a promising approach for tuberculosis treatment. In the current research, a multi-step virtual screening effort toward ZINC and MCE databases was devoted to discover novel mtbDHFR inhibitors. Based on binding affinity of small molecules through molecular docking study in AutoDock Vina, the number of compounds was reduced to 952,688. Further, these compounds were employed by a step-by-step multiple docking programs of Schrödinger suite and filtered by pharmacokinetics and PAINS parameters. Finally, nine ZINC compounds and 400MCE compounds were obtained. These compounds of binding ability were tested with mtbDHFR by FluoPol-ABPP approach established in this work. Finally, AF-353 compound was found to have strong binding effect to mtbDHFR. AF-353 was further tested for mtb and hDHFR enzymatic activities, and it was proved to possess 50-fold selectivity toward mtbDHFR over hDHFR. In silico MD simulation results supported this selectivity.

Multistep virtual screening based identification of homeodomain-interacting protein kinase 2 inhibitors: An opportunity for treating Chronic Kidney Disease

Homeodomain-Interacting Protein Kinase 2 (HIPK2) is a critical regulator of multiple pathways upstream of Chronic Kidney Disease (CKD). With the purpose of seeking HIPK2 inhibitors with novel scaffolds, a multistep virtual screening from the SPECS library was conducted. The screening process generally included similarity screening, random forest modeling, docking and ADMET prediction. Ten compounds were finally purchased and evaluated in biological activity test. The activity results demonstrated that the compound 1 (SPECS ID AG-401/37408043) can inhibit enzymatic activity and cell viability of high-glucose-induced rat normal renal interstitial fibroblast cells (NRK49F) with moderate activity. Its binding modes with the HIPK2 ATP binding site were analyzed. Overall, the compound 1 with a rather new scaffold might be used as the lead for the future structural optimization to seek potent HIPK2 inhibitors.

Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model.

DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, development of DGAT1 inhibitors with novel chemical scaffolds is desired and important in the drug discovery. In this investigation, the multistep virtual screening methods, including machine learning methods and common feature pharmacophore model, were developed and used to identify novel DGAT1 inhibitors from BioDiversity database with 30,000 compounds. 531 compounds were predicted as DGAT1 inhibitors by combination of machine learning methods comprising of SVM, NB and RP models. Then, 12 agents were filtered from 531 compounds by using the common feature pharmacophore model. The 3D chemical structures of the 12 hits coordinated with surface charges and isosurface have been carefully analyzed by the established 3D-QSAR model. Finally, 8 compounds with desired properties were retained from the final hits and have been assigned to another research group to complete the follow-up compound synthesis and biologic evaluation.

Discovery of novel Staphylococcus aureus penicillin binding protein 2a inhibitors by multistep virtual screening and biological evaluation

Penicillin-binding protein 2a (PBP2a) is an essential protein involved in the resistance to β-lactam antibiotics acquired by methicillin-resistant Staphylococcus aureus (MRSA) and is a potential antibacterial target. In the current study, we employed a strategy that combined virtual screening with biological evaluation to discover novel inhibitors of PBP2a. In this investigation, a hybrid virtual screening method, consisting of drug-likeness evaluation (Lipinski’s Rule of Five and ADMET) and rigid (LibDock) and semi-flexible (CDOCKER) docking-based virtual screenings, was used for retrieving novel PBP2a inhibitors from commercially available chemical databases. 11 compounds were selected from the final hits and subsequently shifted to experimental studies. Among them, Hit 2, Hit 3, and Hit 10 exhibited excellent anti-MRSA ATCC 33591 activity and weak toxicity in vitro. The affinity of the three compounds to bind to PBP2a was further confirmed by surface plasmon resonance (SPR) experiments and molecular dynamics (MD) simulation. An inter-complex interaction study showed that all hit compounds adapted well to the allosteric site of the PBP2a protein. In addition, Hit 2 (with best binding affinity to PBP2a, KD = 1.29 × 10−7 M) significantly inhibits proliferation of MRSA clinical isolates. Together, the 3 hit compounds, especially Hit 2, may be potential non-β-lactam antibiotics against MRSA and the work will provide clues for the future development of specific compounds that block the interaction of PBP2a with their targets.

Virtual screening identifies a novel piperazine-based insect juvenile hormone agonist.

Juvenile hormone (JH) agonists constitute a subclass of insect growth regulators and play important roles in insect pest management. In this work, a multi-step virtual screening program was executed to find novel JH agonists. A database of 5 million purchasable compounds was sequentially processed with three computational filters: (i) shape and chemical similarity as compared to known JH-active compounds; (ii) molecular docking simulations against a Drosophila JH receptor, methoprene-tolerant; and (iii) free energy calculation of ligand–receptor binding using a modified MM/PBSA (molecular mechanics/Poisson–Boltzmann surface area) protocol. The 11 candidates that passed the three filters were evaluated in a luciferase reporter assay, leading to the identification of a hit compound that contains a piperazine ring system (EC50=870 nM). This compound is structurally dissimilar to known JH agonists and synthetically easy to access; therefore, it is a promising starting point for further structure optimization.

Discovery of 2-(5-(quinolin-6-yl)-1,3,4-oxadiazol-2-yl)acetamide derivatives as novel PI3Kα inhibitors via docking-based virtual screening

PI3Kα is an attractive target for PIK3CA mutated malignant tumor and searching for lead compounds with novel scaffold is important for the development of PI3Kα inhibitors. Therefore, the strategy of docking-based virtual screening was performed to discovery potent inhibitors. The 4L2Y_A PI3Kα crystal structure was used as the model protein receptor due to its high docking reliability. After the multistep virtual screening protocol and biological evaluation, three hits were picked up and further similarity searching led to more potent 2-(5-(quinolin-6-yl)-1,3,4-oxadiazol-2-yl)acetamide derivatives ES-25 and ES-27. In addition, the primary SAR of these novel derivatives was discussed, which provide a basis for the further structural modification.

Rational molecular targeting of the inter-subunit interaction between human cardiac troponin hcTnC and hcTnI using switch peptide-competitive biogenic medicines

The human cardiac troponin (hcTn) has been implicated in diverse cardiovascular diseases (CDs). The protein function is regulated by the inter-subunit interaction between the N-terminal domain of hcTnC and the C-terminal switch peptide of hcTnI; disruption of the interaction has been recognized as a potential therapeutic strategy for CDs. Here, we report use of biogenic medicines as small-molecule competitors to directly disrupt the protein–protein interaction by competitively targeting the core binding site (CBS) of hcTnC NTD domain. A multistep virtual screening protocol is performed against a biogenic compound library to identify competitor candidates and competition assay is employed to verify the screening results. Consequently, two compounds Collismycin and Compound e are identified as strong competitors (CC50 < 10 μM) with hcTnI for hcTnC CBS site, while other tested compounds are found to have moderate (CC50 = 10–100 μM), low (CC50 > 100 μM) or no (CC50 = N.D.) potency. The competitor ligands are anchored at the core groove of hcTnC CBS site through aromatic and hydrophobic interactions, while few peripheral hydrogen bonds are formed to further confer specificity for domain–compound recognition. These molecular-level findings would benefit from further in vitro and in vivo studies at cellular and animal levels, which can help to practice the ultimate therapeutic purpose.

Molecular modeling studies to discover novel mIDH2 inhibitors with high selectivity for the primary and secondary mutants

Mutant isocitrate dehydrogenase 2 (mIDH2) is an emerging target for the treatment of cancer. AG-221 is the first mIDH2 inhibitor approved by the FDA for acute myeloid leukemia treatment, but its acquired resistance has recently been observed, necessitating the development of new inhibitor. In this study, a multi-step virtual screening protocol was employed for the analysis of a large database of compounds to identify potential mIDH2 inhibitors. To this end, we firstly utilized molecular dynamics (MD) simulations and binding free energy calculations to elucidate the key factors affecting ligand binding and drug resistance. Based on these findings, the receptor-ligand interaction-based pharmacophore (IBP) model and hierarchical docking-based virtual screening were sequentially carried out to assess 212,736 compounds from the Specs database. The resulting hits were finally ranked by PAINS filter and ADME prediction and the top compounds were obtained. Among them, six molecules were identified as mIDH2 putative inhibitors with high selectivity by interacting with the capping residue Asp312. Furthermore, subsequent docking and MD experiments demonstrated that compound V2 might have potential inhibitory activity against the AG-221-resistant mutants, thereby making it a promising lead for the development of novel mIDH2 inhibitors.

Discovery of Novel 5-Lipoxygenase-Activating Protein (FLAP) Inhibitors by Exploiting a Multistep Virtual Screening Protocol.

Leukotrienes (LTs) are proinflammatory mediators derived from arachidonic acid (AA), which play significant roles in inflammatory diseases. The 5-lipoxygenase-activating protein (FLAP) is an integral membrane protein, which is essential for the initial step in LT biosynthesis. The aim of this study was to discover novel and chemically diverse FLAP inhibitors for treatment of inflammatory diseases requiring anti-LT therapy. Both ligand- and structure-based approaches were applied to explain the activities of known FLAP inhibitors in relation to their predicted binding modes. We gained valuable insights into the binding modes of the inhibitors by molecular modeling and generated a multistep virtual screening (VS) workflow in which 6.2 million compounds were virtually screened, and the molecular hypotheses were validated by testing VS-hit compounds biologically. The most potent hit compounds showed significant inhibition of FLAP-dependent cellular LT biosynthesis with IC50 values in the range from 0.13 to 0.87 μM. Collectively, this study provided novel bioactive chemotypes with potential for further development as effective anti-inflammatory drugs.

Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation

Histone Deacetylases (HDACs) play a significant role in the regulation of gene expression by modifying histones and non-histone substrates. Since they are key regulators in the reversible epigenetic mechanism, they are considered as promising drug targets for the treatment of various cancers. In the present study, we have developed a workflow for identification of HDAC1 inhibitors using a multistage virtual screening approach from Maybridge and Chembridge chemical library. Initially, a support vector machine based classification model was generated, followed by generation of a zinc-binding group (ZBG) based pharmacophore model. The hits screened from these models were further subjected to molecular docking. Finally, a set of twenty-three molecules were selected from Maybridge and Chembridge library. The biological evaluation of these hits revealed that three out of the twenty-three tested compounds are showing HDAC1 inhibition along with the moderate anti-proliferative activity. It was found that the identified inhibitors are exerting chromosomal loss effect in growing yeast cells. Further, to extend the activity spectrum of the identified inhibitors, the optimization guidelines were drawn with the hydration site mapping approach by using in silico tool Watermap.Communicated by Ramaswamy H. Sarma

Exploring Novel N-Myristoyltransferase Inhibitors: A Molecular Dynamics Simulation Approach.

N-Myristoyltransferase (NMT) is a cytosolic monomeric enzyme involved in the allocation of the myristoyl group to the aminoterminal of glycine in several viral and eukaryotic cellular proteins. NMT has been validated as a potential drug target against kinetoplastid for parasitic protozoa. A multistep virtual screening protocol based on the pharmacophore modeling, molecular docking, and molecular dynamics simulation was carried out. Initially, Maybridge database was virtually screened via a validated pharmacophore model. The effective pharmacophore models were accompanied with exclusion volumes to improve their receiver operating characteristic curve to identify potential NMT inhibitors. The hits identified as actives based on the 3D-pharmacophore model were evaluated by molecular docking studies. In stepwise screening, six compounds were shortlisted for the dynamic simulation to get insights into their binding mode. In conclusion, this study provides fundamental information about the architecture of the binding site and some crucial residues that may provide insights into the development of new antiparasitic agents.

Structure-based virtual screening for novel chymase inhibitors by in silico fragment mapping

The term chymase refers to a family of chymotrypsin-like serine proteases stored within the secretory granules of mast cells. Recently, a variety of small molecule inhibitors for chymase have been developed with a primary focus on the treatment of cardiovascular diseases. Despite the expected therapeutic benefit of these chymase inhibitors, they have not been used clinically. Here, we attempted to identify new chymase inhibitors using a multistep structure-based virtual screening protocol combined with our knowledge-based in silico fragment mapping technique. The mapping procedure identified fragments with novel modes of interaction at the oxyanion hole of chymase. Next, we constructed a three-dimensional (3D) pharmacophore model and retrieved eight candidate chymase inhibitors from a commercial database that included approximately five million compounds. This selection was achieved using a multistep virtual screening protocol, which combined a 3D pharmacophore-based search, docking calculations, and analyses of binding free energy. One of the eight compounds exhibited concentration-dependent chymase inhibitory activity, which could be further optimized to develop more potent chymase inhibitors.

Multistep virtual screening for rapid identification of G Protein-Coupled Receptors Kinase 2 inhibitors for heart failure treatment

Heart failure (HF) has become an important social problem that seriously threatens human health due to its high morbidity and mortality. G Protein-Coupled Receptors Kinase 2 (GRK2), as a novel target of heart failure, is overexpressed in the pathogenesis and progression of HF. In this study, we conducted a multistep virtual screening process from the ZINC database to discover potential GRK2 inhibitors. First, by using 6 known GRK2 inhibitors with high bioactivity and diverse structures as the template molecules, 7499 compounds were obtained from ligand-based similarity screening by 4 types of fingerprints. Subsequently, these hits were further screened by three quantitative structure-activity relationship (QSAR) models, which were built by 197 known GRK2 compounds and represented by two-dimensional MOE descriptors (MOE2D), molecular fragments (MACCS) and 2- dimensional pharmacophore (CATS) based on random forest algorithm, respectively. These hit compounds were then filtered by three molecular docking packages (MOE, GOLD and GLIDE) to improve the docking accuracy for avoid losing candidate compounds. The compounds with high docking scores were selected for the evaluation of absorption, distribution, metabolism, excretion, and toxicity (ADMET). Finally, 17 compounds were identified the binding affinity to GRK2 by using SPR assay, three of which were considered as novel GRK2 inhibitors, and their binding modes with GRK2 active sites were analyzed. As new potential GRK2 inhibitors, COM5(KD = 0.172 μM) and COM17(KD = 0.595 μM) with the KD value less than 1 μM could be directly used in the next study, while the remaining hit COM4 (KD = 4.72 μM) whose KD over 1 μM can be optimized into the required low nanomole range for further study.

Discovery of natural product inhibitors of phosphodiesterase 10A as novel therapeutic drug for schizophrenia using a multistep virtual screening

The major complaint that most of the schizophrenic patients’ face is the cognitive impairment which affects the patient’s quality of life. The current antipsychotic drugs treat only the positive symptoms without alleviating the negative or cognitive symptoms of the disease. In addition, the existing therapies are known to produce extrapyramidal side effects that affect the patient adherence to the treatment. PDE10A inhibitor is the new therapeutic approach which has been proven to be effective in alleviating the negative and cognitive symptoms of the disease. A number of PDE10A inhibitors have been developed, but no inhibitor has made it beyond the clinical trials so far. Thus, the present study has been conducted to identify a PDE10A inhibitor from natural sources to be used as a lead compound for the designing of novel selective PDE10A inhibitors. Ligand and structure-based pharmacophore models for PDE10A inhibitors were generated and employed for virtual screening of universal natural products database. From the virtual screening results, 37 compounds were docked into the active site of the PDE10A. Out of 37 compounds, three inhibitors showed the highest affinity for PDE10A where UNPD216549 showed the lowest binding energy and has been chosen as starting point for designing of novel PDE10A inhibitors. The structure-activity-relationship studies assisted in designing of selective PDE10A inhibitors. The optimization of the substituents on the phenyl ring resulted in 26 derivatives with lower binding energy with PDE10A as compared to the lead compound. Among these, MA 8 and MA 98 exhibited the highest affinity for PDE10A with binding energy (−10.90 Kcal/mol).

Identification of COX-2 inhibitors via structure-based virtual screening and molecular dynamics simulation

In an attempt to identify potential COX-2 inhibitors, a multi-step virtual screening strategy was performed on a series of compounds. For this purpose, ZINC database was screened on the basis of 50% structural similarity with celecoxib and indomethacin as the representative of COX-2 and COX-1/COX-2 inhibitors, respectively. Selected molecules were subjected to various filters such as Lipinski's rule of five and also several ADME parameters. Toxicity risk of molecules was approximated via in silico methods. Moreover, COX-2 inhibitory activities of the selected compounds were predicted using PASS program. Molecular docking was performed to improve the accuracy of screening and also to find the details of the interactions of the hit compounds with the active site. Finally, MD simulations on top-ranked structures ZINC_1130464, ZINC_3181760, ZINC_33402495 and celecoxib were carried out with COX-2. Furthermore, RMSD, RMSF, hydrogen binds, Rg and energy analysis during MD simulation certainly indicated the stable binding of selected compounds with COX-2 structure. Moreover, docking and MD results revealed that hydrophobic contacts and optimum hydrogen bonds were determinant factors in the interactions of in silico hits and COX-2.

Discovery of 3-hydroxy-3-pyrrolin-2-one-based mPGES-1 inhibitors using a multi-step virtual screening protocol.

Targeting microsomal prostaglandin E2 synthase-1 (mPGES-1) represents an efficient strategy for the development of novel drugs against inflammation and cancer with potentially reduced side effects. With this aim, a virtual screening was performed on a large library of commercially available molecules using the X-ray structure of mPGES-1 co-complexed with a potent inhibitor. Combining fast ligand-based shape alignment, molecular docking experiments, and qualitative analysis of the binding poses, a small set of molecules was selected for the subsequent steps of validation of the biological activity. Compounds 2 and 3, bearing the 3-hydroxy-3-pyrrolin-2-one nucleus, showed mPGES-1-inhibitory activity in the low micromolar range. These data highlighted the applicability of the reported virtual screening protocol for the selection of new mPGES-1 inhibitors as promising anti-inflammatory/anti-cancer drugs.

Virtual screening of some heterocyclic structures toward novel antibacterial agents

Infectious diseases and their treatment are among the most important issues in global health and economy. Moreover, increasing prevalence of antibiotic-resistant pathogenic bacteria necessitates the considerable need for discovering new drugs. Some heterocyclic structures with dihydropyridine (DHP) scaffold have been reported as antimicrobial agents. Herein, we report a structure-based virtual screening of a pool of 3,5-disubstituted DHPs and a post-analysis of virtual hits through in vitro antibacterial assessment. Four top-ranked DHP structures (6a–d) were found to interact with the relevant target active sites and exhibited superior stereoelectronic features within their enzyme inhibition. Selected compounds were synthesized and assessed for their antibacterial activity via microdilution method. Results of this study represented a significant application of multi-step virtual screening strategy in identifying privileged DHP structures as good starting points for further developments toward more potent antibacterial agents.

Potential inhibitors designed against NDM-1 type metallo-\u03b2-lactamases: an attempt to enhance efficacies of antibiotics against multi-drug-resistant bacteria

NDM-1 and its variants are the most prevalent types of metallo-β-lactamases, hydrolyze almost all antibiotics of β-lactam group leading to multiple-drug resistance in bacteria. No inhibitor has yet been obtained for NDM-1 or other class of metallo-β-lactamases. Therefore, strategies to identify novel anti-β-lactamase agents with specific mechanisms of action are the need of an hour. In this study, we have reported the discovery of novel non-β-lactam inhibitors against NDM-1 by multi-step virtual screening approach. The potential for virtually screened drugs was estimated through in vitro cell assays. Five chemical compounds were finally purchased and evaluated experimentally for their efficacies to inhibit NDM-1 producing bacterial cells, in vitro. The dissociation constants (Kd), association constant (Ka), stoichiometry (n) and binding energies (ΔG) of compounds with the respective targets were determined using isothermal titration calorimetry (ITC). Molecular dynamic simulation carried out for 25 ns revealed that these complexes were stable throughout the simulation with relative RMSD in acceptable range. Moreover, Microbiological and kinetic studies further confirmed high efficacies of these inhibitors by reducing the minimum inhibitory concentration (MIC) and catalysis of antibiotics by β-lactamases in the presence of inhibitors. Therefore, we conclude that these potential inhibitors may be used as lead molecules for future drug candidates.

In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using ‘Receptor-Ligand Pharmacophore Generation’ method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r2 of 0.996; for the test set, the correlation coefficient r2 was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.