Discovery of Novel Phosphodiesterase-2A Inhibitors by Structure-Based Virtual Screening, Structural Optimization, and Bioassay

Jatamansinol from Nardostachys jatamansi: a multi-targeted neuroprotective agent for Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial progressive and irreversible neurodegenerative disorder characterized by severe memory impairment and cognitive disability in the middle and old-aged human population. There are no proven drugs for AD treatment and prevention. In Ayurveda, medhya plants are used to prepare Rasayana, and its consumption improves memory and cognition. Nardostachys jatamansi (D.Don) DC is a medhya plant used in traditional medicine to treat neurological disorders, and its unique pyranocoumarins can be a potential drug candidate for AD. Given its traditional claims, this study aims to find the multi-target potential efficacy of the ligands (drug molecules) against the AD from N. jatamansi pyranocoumarins using computational drug discovery techniques. Drug likeliness analysis confirms that pyranocoumarins of N. jatamansi, such as seselin, jatamansinol, jatamansine, jatamansinone, and dihydrojatamansin are probable drug candidates for AD. Molecular docking, molecular dynamic simulations, and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) analysis confirm that dihydrojatamansin inhibits acetylcholinesterase (AChE), and jatamansinol inhibits butyrylcholinesterase (BuChE), glycogen synthase kinase 3β (GSK3β), and kelch-like ECH-associating protein 1 (Keap1) AD therapeutic targets. Therefore, this study provides potential multi-target inhibitors that would further validate experimental studies, leading to new treatments for AD.Communicated by Ramaswamy H. Sarma.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

Battling BTK mutants with noncovalent inhibitors that overcome Cys481 and Thr474 mutations in Waldenström macroglobulinemia therapy: structural mechanistic insights on the role of fenebrutinib

Recently, the non-covalent Bruton tyrosine kinase (BTK) inhibitor fenebrutinib was presented as a therapeutic option with strong inhibitory efficacy against a single (C481S) and double (T474S/C481S) BTK variant in the treatment of Waldenström macroglobulinemia (WM). However, the molecular events surrounding its inhibition mechanism towards this variant remain unresolved. Herein, we employed in silico methods such as molecular dynamic simulation coupled with binding free energy estimations to explore the mechanistic activity of the fenebrutinib on (C481S) and (T474S/C481S) BTK variant, at a molecular level. Our investigations reveal that amino acid arginine contributed immensely to the total binding energy, this establishing the cruciality of amino acid residues, Arg132 and Arg156 in (C481S) and Arg99, Arg137, and Arg132 in (T474S/C481S) in the binding of fenebrutinib towards both BTK variants. The structural orientations of fenebrutinib within the respective hydrophobic pockets allowed favorable interactions with binding site residues, accounting for its superior binding affinity by 24.5% and relative high hydrogen bond formation towards (T474S/C481S) when compared with (C481S) BTK variants. Structurally, fenebrutinib impacted the stability, flexibility, and solvent accessible surface area of both BTK variants, characterized by various alterations observed in the bound and unbound structures, which proved enough to disrupt their biological function. Findings from this study, therefore, provide insights into the inhibitory mechanism of fenebrutinib at the atomistic level and reveal its high selectivity towards BTK variants. These insights could be key in designing and developing BTK mutants' inhibitors to treat Waldenström macroglobulinemia (WM).

Distal mutation V486M disrupts the catalytic activity of DPP4 by affecting the flap of the propeller domain

Dipeptidyl peptidase-4 (DPP4) plays a crucial role in regulating the bioactivity of glucagon-like peptide-1 (GLP-1) that enhances insulin secretion and pancreatic β-cell proliferation, making it a therapeutic target for type 2 diabetes. Although the crystal structure of DPP4 has been determined, its structure-function mechanism is largely unknown. Here, we examined the biochemical properties of sporadic human DPP4 mutations distal from its catalytic site, among which V486M ablates DPP4 dimerization and causes loss of enzymatic activity. Unbiased molecular dynamics simulations revealed that the distal V486M mutation induces a local conformational collapse in a β-propeller loop (residues 234-260, defined as the flap) and disrupts the dimerization of DPP4. The "open/closed" conformational transitions of the flap whereby capping the active site, are involved in the enzymatic activity of DPP4. Further site-directed mutagenesis guided by theoretical predictions verified the importance of the conformational dynamics of the flap for the enzymatic activity of DPP4. Therefore, the current studies that combined theoretical modeling and experimental identification, provide important insights into the biological function of DPP4 and allow for the evaluation of directed DPP4 genetic mutations before initiating clinical applications and drug development.

Computational design of binder as the LC3-p62 protein-protein interaction

Cellular autophagy is an intracellular degradation pathway, which transports damaged, deformed, senescent or non-functional proteins and organelles to lysosome for digestion and degradation. Cellular autophagy is deeply evolutionarily conservedfromyeasttomammaliancells, and many homologous proteins of the autophahgy regulators are found in several species. This physiological process maintains the steady state of cells. Furtheremore, autophagy dysfunction is closely related to various diseases, such as neurodegenerative diseases, inflammation-related diseases, cardiovascular diseases, metabolic diseases, etc. The LC3 and p62 protein protein interaction (PPI) promotes the formation of autophagosomes and delivers polyubiquitinated "cargoes" to autophagic degradation. Therefore, LC3-p62 PPI plays an integral role in the formation of autophagosomes and effectively inhibits autophagy. However, there are still few studies on the LC3-p62 PPI inhibitors for its unclear molecular mechanism. Furthermore, most of these inhibitors are macromolecules with poorly active, and small molecules are particularly scarce. In this article, the computation method was used to identify the hot spot and design peptides as the binder of LC3-p62 PPI. Findings from this work provide a reference for the follow-up research of discovering small molecule inhibitors targeting LC3-p62 PPI.

Design, Bio-evaluation and Molecular Dynamics Simulation of Novel GSK-3β Inhibitors

Glycogen synthase kinase 3 beta (GSK-3β) is considered as a promising drug target for the treatment of Alzheimer's disease (AD). In the present study, two compound libraries were selected for virtual screening based on pharmacophore models of GSK-3β to discover new inhibitors. Nine potential hits were retained for biological investigation and four of these compounds showed GSK-3β inhibitory activity (with the IC₅₀ values in sub-micromolar range on GSK-3β). Compounds 6 and 9 have good safety. They do not have any significant in vitro cytotoxicity against PC12 and SH-SY5Y neuroblastoma cells at concentrations up to 90 μM. Based on the inhibitory activity and druggability properties, compound 8 is the preferred molecule, and it is a promising lead for the development of the GSK-3β inhibitors for reducing the abnormal hyperphosphorylation of tau protein and relieving AD.

Design, synthesis and biological evaluation of novel pyrazolopyrimidone derivatives as potent PDE1 inhibitors

Phosphodiesterase-1 (PDE1) is a promising drug target closely related to central and peripheral diseases. With the assistance of molecular docking and dynamics simulations, we designed and synthesized a novel series of pyrazolopyrimidone derivatives as effective and metabolically stable inhibitors against PDE1. Most compounds have good inhibitory activities against PDE1 at the concentration of 20 nM. Compound 2j with the IC₅₀ of 21 nM against PDE1B, shows good metabolic stability in the rat liver microsomes (RLM) (t_1/2 of 28.5 min), indicating that compound 2j can be used as a tool to explore the molecular recognition mechanism between inhibitors and the target protein PDE1.

Design, synthesis, docking, molecular dynamics and bioevaluation studies on novel N-methylpicolinamide and thienopyrimidine derivatives with inhibiting NF-κB and TAK1 activities: Cheminformatics tools RDKit applied in drug design

Using cheminformatics tools RDKit and literature investigation, four series of 24 thienopyrimidine/N-methylpicolinamide derivatives substituted with pyrimidine were designed, synthesized and evaluated for activities against three cancer cell lines (MDA-MB-231, HCT116 and A549), TAK1 kinase and NF-κB signaling pathway. Almost all compounds showed selectivity toward the A549 cell lines and the most promising compound 38 could inhibit TAK1 kinase and NF-κB signaling pathway with the IC₅₀ values of 0.58 and 0.84 μM. Moreover, 38 can induce cell cycle arrest of A549 cells at the G2/M checkpoint with 30.57% and induce apoptosis (34.94%) in a concentration-dependent manner. And western blot showed that compound 38 could inhibit TNF-α-induced IκBα phosphorylation, IκBα degradation, p65 phosphorylation and TAK1 phosphorylation, and reduce the expression of p65. What's more, the studies of docking, molecular dynamics, MM/PBSA and frequency analysis theoretically supported the conclusions of the bioevaluation.

Treasuring the computational approach in medicinal plant research

Medicinal plants serve as a valuable source of secondary metabolites since time immemorial. Computational Research in 21st century is giving more attention to medicinal plants for new drug design as pharmacological screening of bioactive compound was time consuming and expensive. Computational methods such as Molecular Docking, Molecular Dynamic Simulation and Artificial intelligence are significant Insilico tools in medicinal plant research. Molecular docking approach exploits the mechanism of potential phytochemicals into the target active site to elucidate its interactions and biological therapeutic properties. MD simulation illuminates the dynamic behavior of biomolecules at atomic level with fine quality representation of biomolecules. Dramatical advancement in computer science is illustrating the biological mechanism via these tools in different diseases treatment. The advancement comprises speed, the system configuration, and other software upgradation to insights into the structural explanation and optimization of biomolecules. A probable shift from simulation to artificial intelligence has in fact accelerated the art of scientific study to a sky high. The most upgraded algorithm in artificial intelligence such as Artificial Neural Networks, Deep Neural Networks, Neuro-fuzzy Logic has provided a wide opportunity in easing the time required in classical experimental strategy. The notable progress in computer science technology has paved a pathway for understanding the pharmacological functions and creating a roadmap for drug design and development and other achievement in the field of medicinal plants research. This review focus on the development and overview in computational research moving from static molecular docking method to a range of dynamic simulation and an advanced artificial intelligence such as machine learning.

Synthesis and biological activities study of novel phthalimides and phenylpyrazolo[1,5-a]pyrimidines

Phosphodiesterase II (PDE2) is mainly distributed in brain and heart cells, and it is a potential therapeutic target for the treatment of central nervous system (CNS) diseases such as Alzheimer’s disease. Based on the structure of the existing PDE2 inhibitor BAY60-7550, a series of novel phthalimides and phenylpyrazolo[1,5- a]pyrimidines have been designed and prepared. Furthermore, after evaluating their inhibitory activity toward PDE2, compound 7-oxo- N-phenethyl-5-phenyl-4,7-dihydropyrazolo[1,5- a]pyrimidine-3-carboxamide is found to have the optimum inhibitory potential (IC50: 1.82 ± 0.29 μM). Discovery Studio software used to simulate the structure–activity relationship between this compound and the PDE2 protein crystal 4HTX to illustrate the binding modes, which provides favorable guidance for the further development of effective PDE2 inhibitors.

Molecular modeling for potential cathepsin L inhibitor identification as new anti-photoaging agents from tropical medicinal plants

Ultraviolet exposure can cause photoaging toward the human skin which is begun by the inflammation on the exposure area, also resulting in activation of a degradative enzyme cathepsin L. This enzyme is one of the interesting novel therapeutic targets for antiaging agents. Three plants, named Kleinhovia hospita, Aleurites moluccana, and Centella asiatica, are well-known in the tropical region as anti-inflammatory herbs. The aims of this study were to predict the antiaging activity of the 31 compounds from these plants via inhibition of cathepsin L. All compounds were minimized their energies and then used in molecular docking. After that, molecular dynamics (MD) simulation was employed for the 5 candidate ligands and the positive control; schinol. Interaction analysis results of the pre-MD and post-MD simulation structures were obtained. Furthermore, a toxicity test was performed using ADMET Predictor 7.1. Based on the molecular docking and the MD simulation results, kleinhospitine A, β-amyrin, and castiliferol exhibited lower binding free energy than schinol (-27.0925, -28.6813, -26.0037 kcal/mol) and also had interactions with the S´ region binding site. The toxicity test indicated that β-amyrin is the most potential candidate since it exhibited the lowest binding energy and the high safety level.

Three flavanols delay starch digestion by inhibiting α-amylase and binding with starch

The study aimed to reveal the different mechanisms of delaying starch digestion by ECG, EGCG and Procyanidin based on the perspective of α-amylase-flavanol interaction and starch-flavanol interaction. The interaction characteristics of flavanols with α-amylase were studied from five aspects: enzyme inhibition, kinetics, fluorescence quenching, circular dichroism (CD) and computer simulation. The IC50 of flavanols (ECG, EGCG and Procyanidin) against α-amylase were 172.21 ± 0.22, 732.15 ± 0.13 and 504.45 ± 0.19 μg/mL according to the results of α-amylase inhibition experiment, respectively. ECG and Procyanidin showed mixed inhibition against α-amylase, while EGCG showed non-competition against α-amylase. However, thermodynamic parameters，computer-based docking and dynamic simulation proved that ECG and EGCG-α-amylase complexs were mainly driven by van der Waals and hydrogen bonds, while Procyanidin-α-amylase complexs was driven by hydrophobic interaction. In addition, it was indicated, by means of starch‑iodine complex spectroscopy, that flavanols inhibited the digestion of starch not only through bind with α-amylase but also through bind with starch. Thus, flavanols as a starch-based food additive have the potential to be employed as adjuvant therapy for diabetes.

In silico approaches using pharmacophore model combined with molecular docking for discovery of novel ULK1 inhibitors

Background: Discovery of effective autophagy-initiating kinase ULK1 inhibitors has attracted more and more attention in cancer treatment. Methodology & results: The present study describes the application of a pharmacophore-based virtual screening and structure-based docking approach guided drug design. Compound U-2 exhibited a nanomolar range of IC₅₀ against the ULK1 target. Molecular dynamics simulation was used to assess the quality of docking studies. The determinants of binding affinity were investigated, and a different binding pattern was observed. Subsequently, prediction properties of ADMET (absorption, distribution, metabolism, excretion and toxicity) and hepatotoxicity in vitro studies indicated that U-2 possessed good drug-like properties. Moreover, western blot analysis indicated that the compound inhibited autophagic flux in cells. Conclusion: The present study provides an appropriate guideline for discovering novel ULK1 inhibitors. The novel compound may serve as a good starting point for further development and optimizations.

Synthesis and bio-evaluation of a novel selective butyrylcholinesterase inhibitor discovered through structure-based virtual screening

In recent years, butyrylcholinesterase (BChE) has gradually gained worldwide interests as a novel target for treating Alzheimer's disease (AD). Here, two pharmacophore models were generated using Schrödinger suite and used to virtually screen ChemDiv database, from which three hits were obtained. Among them, 2513-4169 displayed the highest inhibitory activity and selectivity against BChE (eeAChE IC₅₀ > 10 μM, eqBChE IC₅₀ = 3.73 ± 1.90 μM). Molecular dynamic (MD) simulation validated the binding pattern of 2513-4169 in BChE, and it could form a various of receptor-ligand interactions with adjacent residues. In vitro cytotoxicity assay proved the safety of 2513-4169 on diverse neural cell lines. Moreover, the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) assay performed on SH-SY5Y cells proved the neuroprotective effect of 2513-4169 against toxic Aβ_1-42. In vivo behavioral study further confirmed the great efficacy of 2513-4169 on reversing Aβ_1-42-induced cognitive impairment of mice and clearing the toxic Aβ_1-42 in brains. Moreover, 2513-4169 was proved to be able to cross blood-brain barrier (BBB) through a parallel artificial membrane permeation assay of BBB (PAMPA-BBB). Taken together, 2513-4169 is a promising lead compound for future optimization to discover anti-AD treating agents.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

BACKGROUND

Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process.

OBJECTIVE

The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design.

METHOD

This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained.

RESULTS

This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations.

CONCLUSION

The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

Discovery and Optimization of Chromone Derivatives as Novel Selective Phosphodiesterase 10 Inhibitors

Phosphodiesterase 10 (PDE10) inhibitors have received much attention as promising therapeutic agents for central nervous system (CNS) disorders such as schizophrenia and Huntington's disease. Recently, a hit compound 1 with a novel chromone scaffold has shown moderate inhibitory activity against PDE10A (IC₅₀ = 500 nM). Hit-to-lead optimization has resulted in compound 3e with an improved inhibitory activity (IC₅₀ = 6.5 nM), remarkable selectivity (>95-fold over other PDEs), and good metabolic stability (RLM t_1/2 = 105 min) by using an integrated strategy (molecular modeling, chemical synthesis, bioassay, and cocrystal structure). The cocrystal structural information provides insights into the binding pattern of 3e in the PDE10A catalytic domain to highlight the key role of the halogen and hydrogen bonds toward Tyr524 and Tyr693, respectively, thereby resulting in high selectivity against other PDEs. These new observations are of benefit for the rational design of the next generation PDE10 inhibitors for CNS disorders.

Identify old drugs as selective bacterial β-GUS inhibitors by structural-based virtual screening and bio-evaluations

Irinotecan (CPT-11) is a cytotoxic drug that has wide applicability and usage in cancer treatment. Despite its success, patients suffer dose-dependent diarrhea, limiting the drug's efficacy. No effective therapy is available for this unmet medical need. The bacterial β-glucuronidase (β-GUS) plays pivotal role in CPT-11-induced diarrhea (CID) via activating the non-toxic SN-38G to toxic SN-38 inside intestine. By using structural-based virtual screening, three old drugs (N-Desmethylclozapine, Aspartame, and Gemifloxacin) were firstly identified as selective bacterial β-GUS inhibitors. The IC₅₀ values of the compounds in the enzyme-based and cell-based assays range from 0.0389 to 3.6040 and 0.0105 to 5.3730 μM, respectively. The compounds also showed good selectivity against mammalian β-GUS and no significant cytotoxicity in bacteria. Molecular docking and molecular dynamics simulations were performed to further investigate the binding modes of compounds with bacterial β-GUS. Binding free energy decomposition revealed that the compounds formed strong interactions with E413 in catalytic trail from primary monomer and F365' on the bacterial loop from the other monomer of bacterial β-GUS, explaining the selectivity against mammalian β-GUS. The old drugs identified here may be used as bacterial β-GUS inhibitors for CID or other bacterial β-GUS-related disorders.

Discovery of Selective Butyrylcholinesterase (BChE) Inhibitors through a Combination of Computational Studies and Biological Evaluations

As there are increased levels and activity of butyrylcholiesterase (BChE) in the late stage of Alzheimer’s disease (AD), development of selective BChE inhibitors is of vital importance. In this study, a workflow combining computational technologies and biological assays were implemented to identify selective BChE inhibitors with new chemical scaffolds. In particular, a pharmacophore model served as a 3D search query to screen three compound collections containing 3.0 million compounds. Molecular docking and cluster analysis were performed to increase the efficiency and accuracy of virtual screening. Finally, 15 compounds were retained for biological investigation. Results revealed that compounds 8 and 18 could potently and highly selectively inhibit BChE activities (IC₅₀ values < 10 μM on human BChE, selectivity index BChE > 30). These active compounds with novel scaffolds provided us with a good starting point to further design potent and selective BChE inhibitors, which may be beneficial for the treatment of AD.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

Discovery of novel acylhydrazone neuraminidase inhibitors

Neuraminidase (NA) plays a crucial role in the replication and transmission of influenza virus. NA inhibitors have been developed as effective treatments for influenza A and B infections. In this paper, a new lead neuraminidase inhibitor 6a (IC₅₀ = 7.10 ± 0.2 μM) was discovered by ligand-based virtual screening, receptor-based virtual screening, molecular dynamics simulation (MD), and bioassay validation. MD simulation indicates that the morpholinyl group of 6a could be embedded in 430-loop of NA. To exploit the 430-loop in the active site, a series of novel acylhydrazone NA inhibitors 6b-6g were designed and synthesized based on the lead compound 6a. Compound 6e exerts the most potency, with IC₅₀ value of 2.37 ± 0.5 μM against NA, which is lower than that of oseltamivir carboxylate (OC) (IC₅₀ = 3.84 μM). Overall, this work provided unique insights in the discovery of potent inhibitors against NA.

Combined pharmacophore modeling, 3D-QSAR and docking studies to identify novel HDAC inhibitors using drug repurposing

Histone deacetylases (HDACs), a critical family of epigenetic enzymes, has emerged as a promising target for antitumor drugs. Here, we describe our protocol of virtual screening in identification of novel potential HDAC inhibitors through pharmacophore modeling, 3D-QSAR, molecular docking and molecular dynamics (MD) simulation. Considering the limitation of current virtual screening works, drug repurposing strategy was applied to discover druggable HDAC inhibitor. The ligand-based pharmacophore and 3D-QSAR models were established, and their reliability was validated by different methods. Then, the DrugBank database was screened, followed by molecular docking. MD simulation (100 ns) was performed to further study the stability of ligand binding modes. Finally, results indicated the hit DB03889 with high in silico inhibitory potency was suitable for further experimental analysis.Communicated by Ramaswamy H. Sarma.

Structure based in silico identification of potentially non-steroidal brassinosteroids mimics

The identification of non-steroidal BRs-like molecules via structure-based pharmacophore virtual screening, molecular docking and bioassay.