Staphylococcus aureus causes various life-threatening diseases in humans and developed resistance to several antibiotics. Lipophilic membrane (LLM) protein regulates bacterial lysis rate and ...methicillin resistance level in S. aureus. To identify potential lead molecules, we performed a structure-based pharmacophore modeling by consideration of pharmacophore properties from LLM-tunicamycin complex. Further, virtual screening of ZINC database against the LLM was conducted and compounds were assessed for Lipinski and ADMET properties. Based on pharmacokinetic, and molecular docking, five potential inhibitors (ZINC000072380005, ZINC000257219974, ZINC000176045471, ZINC000035296288, and ZINC000008789934) were identified. Molecular dynamics simulation (MDS) of these five molecules was performed to evaluate the dynamics and stability of protein after binding of the ligands. Several MDS analysis like RMSD, RMSF, Rg, SASA, and PCA confirm that identified compounds exhibit higher binding affinity as compared to tunicamycin for LLM. The binding free energy analysis reveals that five compounds exhibit higher binding energy in the range of −218.76 to −159.52 kJ/mol, which is higher as compared to tunicamycin (-116.13 kJ/mol). Individual residue decomposition analysis concludes that Asn148, Asp151, Asp208, His271, and His272 of LLM play a significant role in the formation of lower energy LLM-inhibitor(s) complexes. These predicted molecules displayed pharmacological and structural properties and may be further used to develop novel antimicrobial compounds against S. aureus.
Communicated by Ramaswamy H. Sarma
Transmembrane serine protease 2 (TMPRSS2) has been established as one of the host proteins that facilitate entry of coronaviruses into host cells. One of the approaches often employed towards ...preventing the entry and proliferation of viruses is computer-aided inhibition studies to identify potent compounds that can inhibit activity of viral targets in the host through binding at the active site. In this study, we developed a pharmacophore model of reportedly potent drugs against severe acute respiratory syndrome coronaviruses 1 and 2 (SARS-CoV-1 and -2). The model was used to screen the ZINC database for commercially available compounds having similar features with the experimentally tested drugs. The top 3000 compounds retrieved were docked into the active sites of a homology-modelled TMPRSS2. Docking scores of the top binders were validated and the top-ranked compounds were subjected to ADME, Lipinski's and medicinal Chemistry property predictions for druglikeness analyses. Two lead compounds, ZINC64606047 and ZINC05296775, were identified having binding affinities higher than those of the reference inhibitors, favorable interactions with TMPRSS2 active site residues and good ADME and medicinal chemistry properties. Molecular dynamics simulation was used to assess the stability and dynamics of the interactions of these compounds with TMPRSS2. Binding free energy and contribution energy evaluations were determined using MMPBSA method. Analyses of the trajectory dynamics collectively established further that the lead compounds bound and interacted stably with active site residues of TMPRSS2. Nonetheless, experimental studies are needed to further assess the potentials of these compounds as possible therapeutics against coronaviruses.
Communicated by Ramaswamy H. Sarma.
Targeting HPV16 E6 has emerged as an effective drug target for the treatment/management of cervical cancer. We utilized pharmacophore-based virtual screening, molecular docking, absorption, ...distribution, metabolism and excretion (ADME) prediction, and molecular dynamics simulation approach for identifying potential inhibitors of HPV16 E6. Initially, we generated a ligand-based pharmacophore model based on the features of four known HPV16 E6 inhibitors (CA24, CA25, CA26, and CA27) via the PHASE module implanted in the Schrödinger suite. We constructed four-point pharmacophore features viz., three hydrogen bond acceptors (A) and one aromatic ring (R). The common pharmacophore feature further employed as a query for virtual screening against the ASINEX database via Schrödinger suite. The pharmacophore-based virtual screening filtered out top 2000 hits, based on the fitness score. We then applied the high throughput virtual screening (HTVS), standard precision (SP) and extra precision (XP). 1000 compounds were obtained from HTVS docking. Based on the glide score, they were further filtered to 500 hits by employing docking in standard precision mode. Finally, the best four hits and a negative molecule were identified using docking in XP mode. The four lead compounds and a negative molecule were then further subjected to ADME profile prediction by engaging Qikprop module. The ADME properties of the four lead molecules indicate good pharmacokinetic (PK) properties rather than the negative molecule. The binding stability of the HPV16 E6-hit complexes were investigated at a different time scale (100 ns) by using the desmond package and the results were examined using Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) and it revealed the stability of the protein-ligand complex throughout the simulation. Key residues, CYS 51 and GLN 107, also play a crucial role in enhancing the stability of the protein-ligand complex during the simulation. Furthermore, the binding free energy of the HPV16 E6-leads complexes was analyzed by prime which revealed that the ΔGbind coulomb and ΔGbind vdW interactions are crucially contributes to the binding affinity. In order to validate the computational findings, the efficacy of benzoimidazole and benzotriazole were ascertained for regulating ME180 cervical cancer cell survival, migration and ability to release MMP-2.
P2Y12, a G-protein coupled receptor is involved in platelets plug formation and it amplifies and maintains the process of platelet aggregation. P2Y12 is considered as a potent target to inhibit ...platelet aggregation in thrombotic and cardiac emergencies. This research focuses on in silico inhibition of P2Y12 by structure-based drug design techniques. Initially, drug-like compounds were selected from ZINC database by structure-based pharmacophore model. Subsequently, 4479 compounds matched with the pharmacophore model that were scrutinized by molecular docking. Later, based on docking score and rank, top 10% of the docked library was selected to predict their pharmacokinetic properties and 191 compounds possessed good pharmacokinetic profile. The binding pattern of those compounds were analyzed to select novel, less toxic and more potent P2Y12 antagonists. In protein-ligand interaction analysis, seven compounds showed significant binding potential, therefore examined through molecular dynamic simulation. Among the selected hits, two compounds (CP31 and CP32) exhibited higher binding energies in SANDER Poisson-Boltzmann Surface Area (PBSA) approach than agonist bound P2Y12 (4PXZ) and antithrombotic drug bound P2Y12 (4NTJ), while one compound (CP25) showed comparable binding energy than 4NTJ. The binding free energy analysis reflect that interactions of all selected Hits with P2Y12 are promising and specifically CP25, CP31, and CP32 could serve as novel inhibitors of P2Y12.
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•P2Y12 receptor is considered as a potential drug target in thrombotic and cardiac emergencies.•Structure-based virtual screening techniques including pharmacophore modeling and molecular docking were employed to screen drug-like compounds from ZINC database against P2Y12.•Based on good docking score, pharmacokinetic properties and binding interactions, several novel skeletons were selected as less toxic and more potent P2Y12 antagonists.•The selected hits exhibited good binding energies in SANDER Poisson-Boltzmann Surface Area (PBSA) approach.
The Coronavirus Disease 2019, caused by the severe acute respiratory syndrome coronavirus 2 is an exceptionally contagious disease that leads to global epidemics with elevated mortality and ...morbidity. There are currently no efficacious drugs targeting coronavirus disease 2019, therefore, it is an urgent requirement for the development of drugs to control this emerging disease. Owing to the importance of nucleocapsid protein, the present study focuses on targeting the N-terminal domain of nucleocapsid protein from severe acute respiratory syndrome coronavirus 2 to identify the potential compounds by computational approaches such as pharmacophore modeling, virtual screening, docking and molecular dynamics. We found three molecules (ZINC000257324845, ZINC000005169973 and ZINC000009913056), which adopted a similar conformation as guanosine monophosphate (GMP) within the N-terminal domain active site and exhibiting high binding affinity (>−8.0 kcalmol
−1
). All the identified compounds were stabilized by hydrogen bonding with Arg107, Tyr111 and Arg149 of N-terminal domain. Additionally, the aromatic ring of lead molecules formed π interactions with Tyr109 of N-terminal domain. Molecular dynamics and Molecular mechanic/Poisson-Boltzmann surface area results revealed that N-terminal domain - ligand(s) complexes are less dynamic and more stable than N-terminal domain - GMP complex. As the identified compounds share the same corresponding pharmacophore properties, therefore, the present results may serve as a potential lead for the development of inhibitors against severe acute respiratory syndrome coronavirus 2.
Communicated by Ramaswamy H. Sarma
Several drugs were found after their market approval to unexpectedly inhibit adrenal 11β-hydroxylase (CYP11B1)-dependent cortisol synthesis. Known side-effects of CYP11B1 inhibition include ...hypertension and hypokalemia, due to a feedback activation of adrenal steroidogenesis, leading to supraphysiological concentrations of 11-deoxycortisol and 11-deoxycorticosterone that can activate the mineralocorticoid receptor. This results in potassium excretion and sodium and water retention, ultimately causing hypertension. With the risk known but usually not addressed in preclinical evaluation, this study aimed to identify drugs and drug candidates inhibiting CYP11B1. Two conceptually different virtual screening methods were combined, a pharmacophore based and an induced fit docking approach. Cell-free and cell-based CYP11B1 activity measurements revealed several inhibitors with IC50 values in the nanomolar range. Inhibitors include retinoic acid metabolism blocking agents (RAMBAs), azole antifungals, α2-adrenoceptor ligands, and a farnesyltransferase inhibitor. The active compounds share a nitrogen atom embedded in an aromatic ring system. Structure activity analysis identified the free electron pair of the nitrogen atom as a prerequisite for the drug-enzyme interaction, with its pKa value as an indicator of inhibitory potency. Another important parameter is drug lipophilicity, exemplified by etomidate. Changing its ethyl ester moiety to a more hydrophilic carboxylic acid group dramatically decreased the inhibitory potential, most likely due to less efficient cellular uptake. The presented work successfully combined different in silico and in vitro methods to identify several previously unknown CYP11B1 inhibitors. This workflow facilitates the identification of compounds that inhibit CYP11B1 and therefore pose a risk for inducing hypertension and hypokalemia.
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•Virtual screening by ligand-based pharmacophores and induced fit docking facilitates identification of CYP11B1 inhibitors•Computational prediction combined with cell-free and cell-based activity assays identified potent CYP11B1 inhibitors•Identified CYP11B1 inhibitors include RAMBAs, azole antifungals, α2-adrenoceptor ligands and farnesyltransferase inhibitors•Unwanted CYP11B1 inhibition by drugs poses a risk for hypertension and hypokalemia
This reprint is a collection of 31 original papers and four reviews, published from 2021 to 2022, focused on the application of a wide range of computational tools in medicinal chemistry projects: ...from molecular docking to artificial intelligence approaches. Applications of in silico tools are crucial in the early stages of drug design, such as planning more efficient and economic synthetic routes for chemical administration, screening of huge databases, as well as proposing hypotheses for probable mechanisms of action of drugs in macromolecular targets. Such endeavors are extremely complex and require the usage of modern and sophisticated approaches, such as artificial intelligence, data mining, computational molecular simulations through classical mechanics and quantum mechanics, molecular docking, chemoinformatics, applied mathematics, and biostatistics.
•The 3D-QSAR, virtual screening and docking studies of RET inhibitors are described.•The generated Pharmacophore hypothesis (DDRRR_1) consists of the essential features.•The virtual screening studies ...have been performed through ZINC database.•The ADME parameters showed an excellent pharmacokinetic profile.
The RET (Rearranged during transfection) is a viable target for thyroid cancer and non-small cell lung cancer. For RET inhibition, a variety of heterocyclic fused ring systems have been evaluated. In this context, pyrrolo 2, 3-d pyrimidine analogues have been proven to be a potential inhibitor of the target in the treatment of various cancers. In the present study, we have taken an initiative to model a data set of compounds from the literature and performed in-silico studies. As essential features required for the activity, the pharmacophore modeling generated a five-point pharmacophore hypothesis (DDRRR). The AB-QSAR wizard was utilized for 3D-QSAR analysis and generated significant parameters for the high predictive ability of the model (Q2 = 0.9093, R2 = 0.9621) with minimum errors (SD = 0.3043, RMSE =0.18). The virtual screening studies have been performed through the ZINC database using DDRRR_1 as a template and developed 4800 drug like molecules. These molecules further proceeded through different docking methodologies and screened four hit compounds: ZINC00198134, ZINC32124485, ZINC11856422 and ZINC41121323 showed the best docked poses with docking scores. The ADME parameters showed an excellent pharmacokinetic profile for the selected compounds that may be used further for optimization. The study results may be useful to the researchers for the development of novel RET inhibitors with improved therapeutic efficacy towards cancer treatment.
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Research onβ3-AR, the new member of the adrenoceptor family, is in its infancy and few β3-AR agonists have been approved for marketing to date. Meanwhile, β3-AR exhibited obvious species differences ...in pharmacological properties, such as between human and animals, however, the 3D structure of human β3-AR has not been published, which makes it difficult to understand the interaction between human β3-AR and its agonists. Herein, binding patterns of β3-AR agonists are explored starting from the Alphafold predicted structural model, and the obtained model was optimized by using molecular dynamics simulations. Moreover, the human β3-AR and its agonists were subjected to molecular docking, dynamics simulations, binding free energy calculations and pharmacophore modeling to elucidate the characteristics of human β3-AR activity pockets and agonist conformational relationships, including a hydrophobic group, a positively charged group as well as two hydrogen-bonded donors, which provide comprehensive insights into the interactions between human β3-AR and its agonists.
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•The binding patterns of β3-AR agonists are explored starting from the Alphafold predicted structural model followed by refinement through molecular dynamics simulation.•Through multiple integrated computational approaches, the features of the β3-AR active pocket and the structure-activity relationship of the agonists were elucidated to provide theoretical guidance for the rational design of human β3-AR agonists.
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