Identification of critical chemical features for Aurora kinase-B inhibitors using Hip-Hop, virtual screening and molecular docking

Computational identification and experimental characterization of an aurora kinase inhibitor

The serine/threonine kinases of the aurora family are critical for completing various stages of mitotic cell division. They are frequently overexpressed in various cancers, associated with poor prognosis, and have been validated as an attractive drug target. Despite promising preclinical results, the clinical development of small molecule inhibitors targeting aurora kinases is often hampered by limited efficacy as single agents and severe side effects. Recent discoveries of the synthetic interaction of aurora A with various tumor suppressors and its involvement in the development of resistance to third-generation EGFR inhibitors have renewed interest in finding aurora kinase inhibitors. This study utilized computational approaches to discover an aurora kinase inhibitor. Chemical features of two structurally distinct inhibitors of aurora kinase were exploited to develop a molecular shape and color-based model for the virtual screening of small synthetic molecules in the Enamine database. Six hit compounds validated through docking and Molecular Dynamics (MD) simulation studies were evaluated in a cell-based assay. Only MC-688 inhibited both aurora kinases (A and B) and bound to both kinases in a competition binding assay. Analysis of STD-NMR and 2D NOESY spectra confirmed the computationally predicted binding mode of MC-688 with the ATP binding pocket of aurora A. MC-688 inhibited cell proliferation and long-term treatment of HCT116 colorectal cancer cells with MC-688 induced abrogated mitosis, ultimately leading to apoptotic cell death. In conclusion, MC-688 was computationally identified and experimentally validated as a new pan-aurora inhibitor that induces aurora phenotype in cells and can be used as a lead for further optimization.

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein–ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using in vitro and in vivo methods.

Pharmacophore-based virtual screening approach for identification of potent natural modulatory compounds of human Toll-like receptor 7

Toll-like receptor 7 (TLR7) is a transmembrane glycoprotein playing very crucial role in the signaling pathways involved in innate immunity and has been demonstrated to be useful in fighting against infectious disease by recognizing viral ssRNA & specific small molecule agonists. In order to find novel human TLR7 (hTLR7) modulators, computational ligand-based pharmacophore modeling approach was used to identify the molecular chemical features required for the modulation of hTLR7 protein. A training set of 20 TLR7 agonists with their known experimental activity was used to create pharmacophore model using 3D-QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best developed hypothesis consists of four pharmacophoric features namely, one hydrogen bond donor (HBD), one ring aromatic (RA), and two hydrophobic (HY) character. The developed hypothesis was then validated by different methods such as cost analysis, test set method, and Fischer's test method for consistency. Hence, this validated model was further employed for screening of natural hit compounds from InterBioScreen Natural product database, consisting of more than 60,000 natural compounds and derivatives. The screened hit compounds were subsequently filtered by Lipinski's rule of 5, ADME and toxicity parameters and molecular docking studies to remove the false positive rates. Finally, molecular docking analysis led to identification of the (3a'S,6a'R)-3'-(3,4-dihydroxybenzyl)-5'-(3,4-dimethoxyphenethyl)-5-ethyl-3',3a'-dihydro-2'H-spiro[indoline-3,1'-pyrrolo[3,4-c]pyrrole]-2,4',6'(5'H,6a'H)-trione (Compound ID: STOCK1N-65837) as potent hTLR7 modulator due to its better docking score and molecular interactions compared to other compounds. The result of virtual screening was further validated using molecular dynamics (MD) simulation analysis. Thus, a 30 ns MD simulation analysis revealed high stability and effective binding of STOCK1N-65837 within the binding site of hTLR7. Therefore, the present study provides confidence for the utility of the selected chemical feature based pharmacophore model to design novel TLR7 modulators with desired biological activity.

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Synthesis, Molecular Docking, Molecular Dynamics Studies, and Biological Evaluation of 4H-Chromone-1,2,3,4-tetrahydropyrimidine-5-carboxylate Derivatives as Potential Antileukemic Agents

A series of 4H-chromone-1,2,3,4-tetrahydropyrimidine-5-carboxylates derivatives were synthesized via a three component one-pot condensation of chromone-3-carbaldehyde, alkyl acetoacetate, and urea or thiourea, using MCM-41-SO₃H as efficient nanocatalysts and evaluated for their anticancer activity using a combined in silico docking and molecular dynamics protocol to estimate the binding affinity of the title compounds with the Bcr-Abl oncogene. Two programs, AutoDock 4 and AutoDock Vina software were applied to dock the target protein with synthesized compounds and ATP. AutoDock runs resulted in binding energy scores from -7.8 to -10.16 kcal/mol for AutoDock 4 and -6.9 to -8.5 (kcal/mol) for AutoDock Vina. Furthermore, molecular dynamics (MD) simulations are performed using Gromacs for up to 20 ns simulation time investigating the stability of a ligand-protein complex. Finally, a theoretical experiment using MD simulation for 10 ns was performed without defining the initial coordinates, and the affinity binding of ligand to receptors was directly studied, which revealed that the ligand approaches the active sites. The relative free binding energy for the structure 06 (S06), which has the highest binding energy in Autodock 4 and Autodock Vina (-10.10 and -8.5 kcal/mol, respectively), was also evaluated by molecular mechanics (MM) with Poisson-Boltzmann (PB) and a surface area solvation (MM-PBSA) method using g_mmpbsa tools for the last 15 ns MD. On the basis of binding energy scores, a negative binding energy value of 73.6 kcal/mol, S06, was recognized as the dominant potential inhibitors. The cytotoxic properties of S06 was evaluated against three cell lines, acute T cell leukemia (Jurkat), human chronic myelogenous leukemia, (K562) and human foreskin fibroblast (Hu02) using the microculture tetrazolium test MTT assay. Cisplatin was used as the reference agent. The results indicated that S06 has a higher safety index (SI = 0.73, IC50 = 152.64 μg/mL for Jurkat and IC50 = 110.25 μg/mL for Hu02, P < 0.05 means ± SD for four independent experiments) compared to cisplatin (SI = 0.56, IC50 = 8.86 μg/mL for Jurkat and IC50 = 4.96 μg/mL for Hu02). The in silico results indicated that the proposed structures, which have no toxic effects, are potential tyrosine kinase inhibitors (TKIs) that target Bcr-Abl and thus prevent uncontrolled cell growth (proliferation) but not necessarily cell death (apoptosis) and might potentially constitute an interesting novel class of targeted antileukemic drugs, which deserve further studies.

Computational investigation of inhibitory mechanism of flavonoids as bovine serum albumin anti-glycation agents

Background

Glycation of serum albumin and its consequence products were considered as an important factor in drug distribution and diabetic complications, therefore finding the glycation inhibitors and their inhibitory mechanisms became a valuable field of study. In this work, bovine serum albumin (BSA) became a subject as a model protein for analyzing the inhibitory mechanism of flavonoids, known as natural BSA glycation inhibitors in the early stage of glycation.

Methods

Firstly, for theoretical study, the three-dimensional model of BSA structure was generated by homology modeling and refined through molecular dynamic simulation. Secondly, several validation methods (statistical assessment methods and also neural network methods) by simultaneous docking study were employed for insurance about accuracy of our simulation. Then docking studies were performed for visualizing the relation between flavonoids’ binding sites and BSA glycation sites besides, the correlation analyzes between calculated binding energy and reported experimental inhibitory IC₅₀ values of the flavonoids set, was considered to explore their molecular inhibitory mechanism.

Results

The quality assessment methods and simultaneous docking studies on interaction of quercetin (as the most studied flavonoids) with BSA and Human serum albumin (HAS), confirm the accuracy of simulation and the second stage of docking results which were in close agreement with experimental observations, suggest that the potential residues in flavonoids binding sites (which were place neighbor of tryptophan 212 within 5Ǻ) cannot be considered as one of glycation sites.

Conclusions

Based on the results, flavonoids don’t participate in inhibitory interference mechanism, and also, the differentiation between complexes of flavonoids with BSA and HSA could destroy the speculation of using them as an exchangeable model protein in study of serum albumin and flavonoids interactions.

In vitro and in vivo characterization of a benzofuran derivative, a potential anticancer agent, as a novel Aurora B kinase inhibitor

Aurora B is a serine/threonine kinase that has a key role in mitosis and is overexpressed in cancer cells. Aberrations in Aurora B are highly correlated with tumorigenesis and cancer development, so many studies have focused on the development of Aurora B kinase inhibitors. Based on one of our previous high-throughput screening studies, we identified lead compound S6, a small-molecule benzofuran derivative that binds Aurora B and inhibits its kinase activity in vitro. S6 also displayed high selectivity for Aurora B inhibition. The cytotoxicity of S6 was assessed against a panel of 21 cancer cell lines. The cervical cancer cell line HeLa, liver cancer cell line HepG2 and colon cancer cell line SW620 were the most sensitive to S6 treatment. We found that S6 decreased the proliferation and colony formation of these three cell lines and elevated their percentages of cells in the G2/M phase of the cell cycle. S6 also inhibited phospho-histone H3 on Ser 10, a natural biomarker of endogenous Aurora B activity. The growth suppression of liver cancer QGY-7401 xenograft tumors was observed in nude mice after S6 administration, and this effect was accompanied by the in vivo inhibition of phospho-histone H3 (Ser 10). Taken together, we conclude that targeting Aurora B with compound S6 may be a novel strategy for cancer treatment, and additional studies are warranted.

Ligand-based pharmacophore modeling and Bayesian approaches to identify c-Src inhibitors

Abstract Cellular Src (c-Src) kinases play a critical role in cell adhesion, proliferation, angiogenesis and cancer. Ligand-based pharmacophore models, used to identify the critical chemical features of c-Src inhibitors, were generated and validated by training, test and decoy sets, respectively. Best pharmacophore model, Hypo1, consists of four features such as HBA, HBD, Hy-Ar and RA. Hypo1 was used in virtual screening of the chemical databases such as Maybridge, Chembridge and NCI. The sorted compounds by Hypo1 were further reduced by applying drug-like properties and ADMET. Totally, 85 compounds which showed the good drug-like properties were selected from three databases and subjected to molecular docking for refinement of the retrieved hits by analysing the suitable orientation of the compounds in the active site of c-Src. Finally, 18 compounds were selected based on consensus scoring and hydrogen bond interactions with critical amino acids such as Met341, Thr338, Glu339 or Asp404. In addition, the Bayesian model was generated from the training set to find suitable fragments for inhibition of the c-Src function. Based on the above finding, we suggested that the Hypo1 and the good fragments from the Bayesian model will be helpful to select the compounds from various databases to identify the novel and potent c-Src inhibitor.

Pharmacophore-based virtual screening and density functional theory approach to identifying novel butyrylcholinesterase inhibitors

AIM

To identify the critical chemical features, with reliable geometric constraints, that contributes to the inhibition of butyrylcholinesterase (BChE) function.

METHODS

Ligand-based pharmacophore modeling was used to identify the critical chemical features of BChE inhibitors. The generated pharmacophore model was validated using various techniques, such as Fischer's randomization method, test set, and decoy set. The best pharmacophore model was used as a query in virtual screening to identify novel scaffolds that inhibit BChE. Compounds selected by the best hypothesis in the virtual screening were tested for drug-like properties, and molecular docking study was applied to determine the optimal orientation of the hit compounds in the BChE active site. To find the reactivity of the hit compounds, frontier orbital analysis was carried out using density functional theory.

RESULTS

Based on its correlation coefficient (0.96), root mean square (RMS) deviation (1.01), and total cost (105.72), the quantitative hypothesis Hypo1 consisting of 2 HBA, 1 Hy-Ali, and 1 Hy-Ar was selected as the best hypothesis. Thus, Hypo1 was used as a 3D query in virtual screening of the Maybridge and Chembridge databases. The hit compounds were filtered using ADMET, Lipinski's Rule of Five, and molecular docking to reduce the number of false positive results. Finally, 33 compounds were selected based on their critical interactions with the significant amino acids in BChE's active site. To confirm the inhibitors' potencies, the orbital energies, such as HOMO and LUMO, of the hit compounds and 7 training set compounds were calculated. Among the 33 hit compounds, 10 compounds with the highest HOMO values were selected, and this set was further culled to 5 compounds based on their energy gaps important for stability and energy transfer. From the overall results, 5 hit compounds were confirmed to be potential BChE inhibitors that satisfied all the pharmacophoric features in Hypo1.

CONCLUSION

This study pinpoints important chemical features with geometric constraints that contribute to the inhibition of BChE activity. Five compounds are selected as the best hit BchE-inhibitory compounds.

Molecular docking and dynamics simulation, receptor-based hypothesis: application to identify novel sirtuin 2 inhibitors

Sirtuin, NAD(+)-dependent histone deacetylase enzyme, emerged as a potential therapeutic target, and modulations by small molecules could be effective drugs for various diseases. Owing to the absence of complex structure of sirtuin 2 (SIRT2), sirtinol was docked in the NAD(+) binding site and subjected to 5-nseconds molecular dynamics (MD) simulation. LigandScout was used to develop hypotheses based on 3-representative SIRT2 complex structures from MD. Three structure-based hypotheses are generated and merged to form dynamics hypothesis. The dynamics hypothesis was validated using test and decoy sets. The results showed that dynamic hypothesis represents the complementary features of SIRT2 active site. Dynamic hypothesis was used to screen ChemDiv database, and hits were filtered through ADMET, rule of five, and two different molecular docking studies. Finally, 21 molecules were selected as potent leads based on consensus score from LigandFit, Gold fitness score, binding affinity from VINA as well as based on the important interactions with critical residues in SIRT2 active site. Hence, we suggest that the dynamic hypothesis will be reliable in the identification of SIRT2 new lead as well as to reduce time and cost in the drug discovery process.

Molecular modeling study for conformational changes of Sirtuin 2 due to substrate and inhibitor binding

Sirtuin is a member of NAD(+)-dependent deacetylase family. The structural details of Sirtuin 2 (SIRT2) complex will be very useful to discover the drug which might have beneficial effects on various diseases like cancer, diabetes, etc. Unfortunately, SIRT2 complex structure is not available yet, hence molecular docking was carried out to dock the substrate (NAD(+) and acetylated lysine) and inhibitor (sirtinol) in the NAD(+) binding site. The suitable binding orientation of substrate and inhibitor in the SIRT2 active site was selected and subjected to 5 ns molecular dynamics simulations to adjust the binding orientation of inhibitor and substrate as well as to identify the conformational changes in the active site. The result provides an insight about 3D SIRT2 structural details as well as the importance of F96 in deacetylation function. In addition, our simulations revealed the displacement of F96 upon substrate and inhibitor binding, inducing an extended conformation of loop3 and changing its interactions with the rest of SIRT2. We believe that our study could be helpful to gain a structural insight of SIRT2 and to design the receptor-based inhibitors.