Virtual screening, ADMET profiling, molecular docking and dynamics approaches to search for potent selective natural molecules based inhibitors against metallothionein-III to study Alzheimer's disease

Network pharmacology and multitarget analysis of Nigella sativa in the management of diabetes and obesity: a computational study

Obesity and diabetes are commonly associated with one another and represent a significant global health issue, with a recent surge in disease incidence. Nigella sativa, also known as black cumin, is believed to possess several health benefits, including anti-diabetic, anticancer, antioxidant, antimicrobial, and anti-obesity properties. In this study, we aimed to identify the active compounds derived from N. sativa, which can potentially inhibit key protein targets and signaling pathways associated with diabesity treatment. We employed an exhaustive in silico search, which led to the identification of 22 potential compounds. Out of these, only five hits were found to be non-toxic, including Arabic and ascorbic acids, dihydrocodeine, catechin, and kaempferol. Our analysis revealed that these hits were associated with genes such as AKT1, IL6, SRC, and EGFR. Finally, we conducted molecular docking and molecular dynamics simulations, which identified kaempferol as the best binder for AKT1 in comparison to the reference molecule. Overall, our in silico integrated pipeline provides a useful approach to identify non-toxic phytocompounds as promising drug candidates to treat diabetes and obesity.Communicated by Ramaswamy H. Sarma.

Computational and Preclinical Prediction of the Antimicrobial Properties of an Agent Isolated from Monodora myristica: A Novel DNA Gyrase Inhibitor

The African nutmeg (Monodora myristica) is a medically useful plant. We, herein, aimed to critically examine whether bioactive compounds identified in the extracted oil of Monodora myristica could act as antimicrobial agents. To this end, we employed the Schrödinger platform as the computational tool to screen bioactive compounds identified in the oil of Monodora myristica. Our lead compound displayed the highest potency when compared with levofloxacin based on its binding affinity. The hit molecule was further subjected to an Absorption, Distribution, Metabolism, Excretion (ADME) prediction, and a Molecular Dynamics (MD) simulation was carried out on molecules with PubChem IDs 529885 and 175002 and on three standards (levofloxacin, cephalexin, and novobiocin). The MD analysis results demonstrated that two molecules are highly compact when compared to the native protein; thereby, this suggests that they could affect the protein on a structural and a functional level. The employed computational approach demonstrates that conformational changes occur in DNA gyrase after the binding of inhibitors; thereby, this resulted in structural and functional changes. These findings expand our knowledge on the inhibition of bacterial DNA gyrase and could pave the way for the discovery of new drugs for the treatment of multi-resistant bacterial infections.

Integrating Network Pharmacology and Molecular Docking Approaches to Decipher the Multi-Target Pharmacological Mechanism of Abrus precatorius L. Acting on Diabetes

Type 2 diabetes mellitus (T2DM) is a notable health care load that imposes a serious impact on the quality of life of patients. The small amount of reported data and multiple spectra of pathophysiological mechanisms of T2DM make it a challenging task and serious economic burden in health care management. Abrus precatorius L. is a slender, perennial, deciduous, and woody twining plant used in various regions of Asia to treat a variety of ailments, including diabetes mellitus. Various in vitro studies revealed the therapeutic significance of A. precatorius against diabetes. However, the exact molecular mechanism remains unclarified. In the present study, a network pharmacology technique was employed to uncover the active ingredients, their potential targets, and signaling pathways in A. precatorius for the treatment of T2DM. In the framework of this study, we explored the active ingredient–target–pathway network and figured out that abrectorin, abrusin, abrisapogenol J, sophoradiol, cholanoic acid, precatorine, and cycloartenol decisively contributed to the development of T2DM by affecting AKT1, MAPK3, TNFalpha, and MAPK1 genes. Later, molecular docking was employed to validate the successful activity of the active compounds against potential targets. Lastly, we conclude that four highly active constituents, namely, abrusin, abrisapogenol J, precatorine, and cycloartenol, help in improving the body’s sensitivity to insulin and regulate the expression of AKT1, MAPK3, TNFalpha, and MAPK1, which may act as potential therapeutic targets of T2DM. Integrated network pharmacology and docking analysis revealed that A. precatorius exerted a promising preventive effect on T2DM by acting on diabetes-associated signaling pathways. This provides a basis to understand the mechanism of the anti-diabetes activity of A. precatorius.

Characterization of the structure, stability, and activity of hypoglycemic peptides from Moringa oleifera seed protein hydrolysates

Moringa oleifera seed protein hydrolysates exhibit good hypoglycemic activity, but their specific peptide components have not yet been characterized. Here, we identified the ultrafiltration peptide components (<3 kDa) of M. oleifera seed protein hydrolysates. A highly active α-glucosidase inhibitory peptide with an IC₅₀ value of 109.65 μM (MoHpP-2) with the amino acid sequence KETTTIVR was identified. We characterized its structural properties, stability, and hypoglycemic activity. MoHpP-2 was found to be an amphipathic peptide with a β-turn structure, and the hemolysis of red blood cells was not observed when its concentration was lower than 2 mg mL^-1. MoHpP-2 was stable under weakly acidic conditions, at temperatures lower than 60 °C, and at high ion concentrations. Western blotting revealed that MoHpP-2 affected the PI3K and AMPK pathways of HepG2 cells. Molecular docking revealed that MoHpP-2 interacted with α-glucosidase through hydrogen bonding and hydrophobic forces. Thus, MoHpP-2 from M. oleifera seeds could be used to make hypoglycemic functional foods.

Computational insight of dexamethasone against potential targets of SARS-CoV-2

The health sector has been on the race to find a potent therapy for coronavirus disease (COVID)-19, a diseases caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2. Repurposed anti-viral drugs have played a huge role in combating the virus, and most recently, dexamethasone (Dex) have shown its therapeutic activity in severe cases of COVID-19 patients. The study sought to provide insights on the anti-COVID-19 mechanism of Dex at both atomic and molecular level against SARS-CoV-2 targets. Computational methods were employed to predict the binding affinity of Dex to SARS-CoV-2 using the Schrodinger suite (v2020-2). The target molecules and ligand (Dex) were retrieved from PDB and PubChem, respectively. The selected targets were SARS-CoV-2 main protease (Mpro), and host secreted molecules glucocorticoid receptor, and Interleukin-6 (IL-6). Critical analyses such as Protein and ligand preparation, molecular docking, molecular dynamic (MD) simulations, and absorption, distribution, metabolism, excretion (ADME), and toxicity analyses were performed using the targets and the ligand as inputs. Dex showed stronger affinity to its theoretical (glucocorticoid) receptor with a superior docking score of -14.7 and a good binding energy value of -147.48 kcal/mol; while short hydrogen bond distances were observed in both Mpro and IL-6 when compared to glucocorticoid receptor. Based on these findings, Dex-target complexes were used to perform MD simulations to analyze Dex stability at 50 ns. This study demonstrates that Dex could bind to both the viral and host receptors as a potential drug candidate for COVID-19. To ascertain the biological fitness of this study, other SARS-CoV-2 targets should be explored. Also, the in vitro studies of dexamethasone against several SARS-CoV-2 targets warrant further investigation.Communicated by Ramaswamy H. Sarma.

A computational biology approach for the identification of potential SARS-CoV-2 main protease inhibitors from natural essential oil compounds

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fomented a climate of fear worldwide due to its rapidly spreading nature, and high mortality rate. The World Health Organization (WHO) declared it as a global pandemic on 11th March, 2020. Many endeavors have been made to find appropriate medications to restrain the SARS CoV-2 infection from spreading but there is no specific antiviral therapy to date. However, a computer-aided drug design approach can be an alternative to identify probable drug candidates within a short time. SARS-CoV-2 main protease is a proven drug target, and it plays a pivotal role in viral replication and transcription. Methods: In this study, we identified a total of 114 essential oil compounds as a feasible anti-SARS-CoV-2 agent from several online reservoirs. These compounds were screened by incorporating ADMET profiling, molecular docking, and 50 ns of molecular dynamics simulation to identify potential drug candidates against the SARS-CoV-2 main protease. The crystallized SARS-CoV-2 main protease structure was collected from the RCSB PDB database (PDB ID 6LU7). Results: According to the results of the ADMET study, none of the compounds have any side effects that could reduce their druglikeness or pharmacokinetic properties. Out of 114 compounds, we selected bisabololoxide B, eremanthin, and leptospermone as our top drug candidates based on their higher binding affinity scores, and strong interaction with the Cys 145-His 41 catalytic dyad. Finally, the molecular dynamics simulation was implemented to evaluate the structural stability of the ligand-receptor complex. MD simulations disclosed that all the hits showed conformational stability compared to the positive control α-ketoamide. Conclusions: Our study showed that the top three hits might work as potential anti-SARS-CoV-2 agents, which can pave the way for discovering new drugs, but for experimental validation, they will require more in vivo trials.

Combined 4D-QSAR and target-based approaches for the determination of bioactive Isatin derivatives

The hybrid method of the Electron-Conformational Genetic Algorithm (EC-GA) was used to determine the pharmacophore groups and to estimate anticancer activity in isatin derivatives using a robust 4D-QSAR software (EMRE). To build the model, each compound is represented by a set of conformers rather than a single conformation. The Electron Conformational Matrix of Congruity (ECMC) is composed via EMRE software. Electron Conformational Submatrix of Activity (ECSA) was calculated by the comparison of these matrices. Genetic algorithm was used to select important variables to predict theoretical activity. The model with the best seven parameters produced satisfactory results. The E statistics technique was applied to the generated EC-GA model to evaluate the individual contribution of each of the descriptors on biological activity. The r² and q² values of the training set compounds were found to be 0.95 and 0.93, respectively. Because no previous 4D-QSAR studies on isatin derivatives have been conducted, this study is important in the development of new isatin derivatives. In this study, 27 isatin derivatives whose activities were estimated using the hybrid EC-GA method were also investigated through molecular docking and molecular dynamics simulations for their BCL-2 inhibitory activity.

Current status and future prospects of drug-target interaction prediction

Drug-target interaction prediction is important for drug development and drug repurposing. Many computational methods have been proposed for drug-target interaction prediction due to their potential to the time and cost reduction. In this review, we introduce the molecular docking and machine learning-based methods, which have been widely applied to drug-target interaction prediction. Particularly, machine learning-based methods are divided into different types according to the data processing form and task type. For each type of method, we provide a specific description and propose some solutions to improve its capability. The knowledge of heterogeneous network and learning to rank are also summarized in this review. As far as we know, this is the first comprehensive review that summarizes the knowledge of heterogeneous network and learning to rank in the drug-target interaction prediction. Moreover, we propose three aspects that can be explored in depth for future research.

An Integrated Computational and Experimental Approach to Identifying Inhibitors for SARS-CoV-2 3CL Protease

The newly evolved SARS-CoV-2 has caused the COVID-19 pandemic, and the SARS-CoV-2 main protease 3CLpro is essential for the rapid replication of the virus. Inhibiting this protease may open an alternative avenue toward therapeutic intervention. In this work, a computational docking approach was developed to identify potential small-molecule inhibitors for SARS-CoV-2 3CLpro. Totally 288 potential hits were identified from a half-million bioactive chemicals via a protein-ligand docking protocol. To further evaluate the docking results, a quantitative structure activity relationship (QSAR) model of 3CLpro inhibitors was developed based on existing small molecule inhibitors of the 3CLproSARS- CoV- 1 and their corresponding IC50 data. The QSAR model assesses the physicochemical properties of identified compounds and estimates their inhibitory effects on 3CLproSARS- CoV- 2. Seventy-one potential inhibitors of 3CLpro were selected through these computational approaches and further evaluated via an enzyme activity assay. The results show that two chemicals, i.e., 5-((1-([1,1'-biphenyl]-4-yl)-2,5-dimethyl-1H-pyrrol-3-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione and N-(4-((3-(4-chlorophenylsulfonamido)quinoxalin-2-yl)amino)phenyl)acetamide, effectively inhibited 3CLpro SARS-CoV-2 with IC50's of 19 ± 3 μM and 38 ± 3 μM, respectively. The compounds contain two basic structures, pyrimidinetrione and quinoxaline, which were newly found in 3CLpro inhibitor structures and are of high interest for lead optimization. The findings from this work, such as 3CLpro inhibitor candidates and the QSAR model, will be helpful to accelerate the discovery of inhibitors for related coronaviruses that may carry proteases with similar structures to SARS-CoV-2 3CLpro.

Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors

B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.

The influence of calculated physicochemical properties of compounds on their ADMET profiles

We analyzed the influence of calculated physicochemical properties of more than 20,000 compounds on their P-gp and BCRP mediated efflux, microsomal stability, hERG inhibition, and plasma protein binding. Our goal was to provide guidance for designing compounds with desired pharmacokinetic profiles. Our analysis showed that compounds with ClogP less than 3 and molecular weight less than 400 will have high microsomal stability and low plasma protein binding. Compounds with logD less than 2.2 and/or basic pKa larger than 5.3 are likely to be BCRP substrates and compounds with basic pKa less than 5.2 and/or acidic pKa less than 13.4 are less likely to inhibit hERG. Based on these results, compounds with MW < 400, ClogP < 3, basic pKa < 5.2 and acidic pKa < 13.4 are likely to have good bioavailability and low hERG inhibition.

In Ovo and In Silico Evaluation of the Anti-Angiogenic Potential of Syringin

INTRODUCTION

Cancer is considered as one of the deadliest human diseases today. Angiogenesis, the propagation of new blood vessels from pre-existing vasculature, is a critical step in the progression of cancer as it is essential in the growth and metastasis of tumors. Hence, suppression of angiogenesis is a promising approach in cancer therapy. Syringin, a phenylpropanoid glycoside with a molecular formula of C17H24O9, has been found to exhibit chemopreventive effects. However, its anti-angiogenic activity and the underlying mechanism of action are still unknown.

METHODS

In this work, in ovo chorioallantoic membrane (CAM) assay has been conducted to evaluate the effect of syringin on neovascularization. Additionally, reverse molecular docking studies have been performed in order to identify the probable enzyme targets in the angiogenesis pathway.

RESULTS

Treatment with syringin showed significant dose-dependent inhibition of blood vessel length and junctions in the CAM of duck eggs; the anti-angiogenic activity of syringin at 100 µM and 200 µM is comparable with 200 µM of the positive control celecoxib. The results of reverse docking studies indicate that syringin binds the strongest to dihydrofolate reductase (DHFR) and, to some extent, with transforming growth factor-beta receptor type 1 (TGF-βR1), vascular endothelial growth factor receptor 2 (VEGFR2), and matrix metalloproteinase-2 (MMP-2). Furthermore, ADMET models revealed that syringin potentially possesses excellent pharmacokinetic and toxicity profiles.

CONCLUSION

This study demonstrates the potential of syringin as an anti-angiogenic agent and elicits further investigations to establish its application in cancer suppression.

In silico identification of angiotensin-1 converting enzyme inhibitors using text mining and virtual screening

Cardiovascular diseases are the world's leading cause of death. Hypertension is an important risk factor for cardiovascular and renal diseases. Angiotensin-converting enzyme (ACE) can be a possible therapeutic target for managing angiotensin I conversion to angiotensin II and ultimately controlling hypertension. Indole is an significant fragment used in many medicines approved by FDA. For this reason, the molecules in their fragments containing" indol" keywords were taken from the Specs-SC (small compound) database. The predicted therapeutc activity values (TAV) of these compounds against hypertension were evaluated using binary models of QSAR by MetaCore/MetaDrug. For the 26 separate QSAR models of toxicity, molecules with measured TAV greater than 0.5 were used. 3792 non-toxic compounds were investigated by molecular docking study and molecular dynamics simulations for their ACE inhibitory activity. Glide standard precision (SP) of Maestro Molecular Modeling pocket was used to perform molecular docking. Short molecular dynamics (MD) simulations (5-ns) were carried out by initiating the top docking poses of selected 40 molecules. To quantitatively evaluate the predicted binding affinity of a screened compound, average MM/GBSA scores of screened ligands were calculated and based on their binding free energy values, hit compounds were identified for the long (100-ns) MD simulations. Root mean square deviation and root mean square fluctuations were also calculated to assess the structural characteristics and observe fluctuations of the 100-ns time scale. Thus, with the application of text mining and integrated molecular modeling we reported novel indole-based hit inhibitors for ACE-1.Communicated by Ramaswamy H. Sarma.

Investigation of novel indole-based HIV-1 protease inhibitors using virtual screening and text mining

Human immunodeficiency virus type 1 protease (HIV-1 PR) inhibitors have been used as possible therapeutic agents for HIV-1 infection in clinical study. Most of the HIV therapy-related problems usually stem from long-term opioid usage. The rapid development of drug-resistant variants limits the long-term effectiveness of current inhibitors as therapeutic agents. In addition, different side effects were reported. Further drug development is required to design new compounds which have similar efficacy as the drugs currently used in HIV infection but without having undesirable side effects. Indole derivatives were considered as one of the effective HIV inhibitors. Indole is an important fragment used in many FDAapproved medicines and used in various diseases. For this purpose, in this study the molecules containing" indole" keywords in their fragments are taken from the Specs-SC database which includes 212520 small molecules. 5194 molecules that include indole keywords are selected. These selected molecules are then screened against HIV-1 PR target protein using molecular docking simulations. Then the molecules are ranked according to the their docking scores. Top docking poses of ten ligands and FDA approved drug Amprenavir are subjected to 100 ns Molecular Dynamics (MD) simulations. Thus, by using combination of text mining and integrated molecular modeling approaches, we identified novel indole-based hits against HIV-1 PR.Communicated by Ramaswamy H. Sarma.

Combined ligand and structure-based virtual screening approaches for identification of novel AChE inhibitors

The excessive activity of acetylcholinesterase enzyme (AChE) causes different neuronal problems, especially dementia and neuronal cell deaths. Food and Drug Administration (FDA) approved drugs donepezil, rivastigmine, tacrine and galantamine are AChE inhibitors and in the treatment of Alzheimer’s disease (AD) these drugs are currently prescribed. However, these inhibitors have various adverse side effects. Therefore, there is a great need for the novel selective AChE inhibitors with fewer adverse side effects for the effective treatment. In this study, combined ligand-based and structure-based virtual screening approaches were used to identify new hit compounds from small molecules library of National Cancer Institute (NCI) containing approximately 265,000 small molecules. In the present study, we developed a computational pipeline method to predict the binding affinities of the studied compounds at the specific target sites. For this purpose, a text mining study was carried out initially and compounds containing the keyword “indol” were considered. The therapeutic activity values against AD were screened using the binary quantitative structure activity relationship (QSAR) models. We then performed docking, molecular dynamics (MD) simulations and free energy analysis to clarify the interactions between selected ligands and enzyme. Thus, in this study we identified new promising hit compounds from a large database that may be used to inhibit the enzyme activity of AChE.

Novel small molecule therapeutic agents for Alzheimer disease: Focusing on BACE1 and multi-target directed ligands

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that effects 50 million people worldwide. In this review, AD pathology and the development of novel therapeutic agents targeting AD were fully discussed. In particular, common approaches to prevent Aβ production and/or accumulation in the brain including α-secretase activators, specific γ-secretase modulators and small molecules BACE1 inhibitors were reviewed. Additionally, natural-origin bioactive compounds that provide AD therapeutic advances have been introduced. Considering AD is a multifactorial disease, the therapeutic potential of diverse multi target-directed ligands (MTDLs) that combine the efficacy of cholinesterase (ChE) inhibitors, MAO (monoamine oxidase) inhibitors, BACE1 inhibitors, phosphodiesterase 4D (PDE4D) inhibitors, for the treatment of AD are also reviewed. This article also highlights descriptions on the regulator of serotonin receptor (5-HT), metal chelators, anti-aggregants, antioxidants and neuroprotective agents targeting AD. Finally, current computational methods for evaluating the structure-activity relationships (SAR) and virtual screening (VS) of AD drugs are discussed and evaluated.

Novel AChE and BChE inhibitors using combined virtual screening, text mining and in vitro binding assays

In the current work, we developed a computational pipeline method for predicting the binding affinities of studied compounds at the specific target sites. Since many approved therapeutic compounds involve indole or indole-derivative rings, in the current study we focused compounds including these fingerprints. Initially, 212520 compounds were retrieved from Specs-SC library and after the conversion of IUPAC text file format, compounds that include 'indol' keyword (5194 compounds) were used in binary QSAR-based models to screen against a defined therapeutic activity "Alzheimer's disease" (AD). The molecules that have higher AD therapeutic activity values (>0.5) were then used in the 26 different toxicity-QSAR models. Binary QSAR models resulted 89 hits that have high AD therapeutic activity and no toxicity. Selected 89 molecules were then screened against acetylcholinesterase (AChE) targets using molecular docking and top-docking poses of compounds were used in initially short (10 ns) molecular dynamics (MD) simulations. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) binding free energy calculations were performed for 89 ligands and tightly bound 17 ligands based on average MM/GBSA scores were selected for long (100 ns) MD simulations. The same protocol was also applied for the known 4 AChE inhibitors. Selected hits were also docked to the binding pocket of butyrylcholinesterase (BChE). Finally, based on MM/GBSA scores, as well as their corresponding docking scores and metabolite production profiles, 7 compounds were selected and their in vitro tests were performed. Out of 7 compounds, 6 of them showed μM-level inhibition for both AChE and BChE targets.Communicated by Ramaswamy H. Sarma.

Computer-Assisted Drug Virtual Screening Based on the Natural Product Databases

BACKGROUND

Computer-assisted drug virtual screening models the process of drug screening through computer simulation technology, by docking small molecules in some of the databases to a certain protein target. There are many kinds of small molecules databases available for drug screening, including natural product databases.

METHODS

Plants have been used as a source of medication for millennia. About 80% of drugs were either natural products or related analogues by 1990, and many natural products are biologically active and have favorable absorption, distribution, metabolization, excretion, and toxicology.

RESULTS

In this paper, we review the natural product databases' contributions to drug discovery based on virtual screening, focusing particularly on the introductions of plant natural products, microorganism natural product, Traditional Chinese medicine databases, as well as natural product toxicity prediction databases.

CONCLUSION

We highlight the applications of these databases in many fields of virtual screening, and attempt to forecast the importance of the natural product database in next-generation drug discovery.

Molecular interaction of investigational ligands with human brain acetylcholinesterase

Alzheimer's disease, a neurodegenerative disorder continues to be an area of investigation by the international researchers' fraternity. Despite all the ongoing efforts, the effective set of promising cholinesterase inhibitors available in the market for patients' use is limited. Furthermore, the currently available drugs could provide only a palliative type of treatment instead of providing a complete cure or foolproof prevention. Hence, design/discovery of fresh drug molecules as acetylcholinesterase (AChE) inhibitors still remains an urgent requirement. The drug discovery platform, MCULE in the "structure-based virtual screening" (SBVS) mode was used for high throughput ligand screening of over five million structures targeted against the AChE catalytic site. A stepwise query was made for the SBVS input. The number of hits was narrowed down in consecutive succession via varied filtration criteria as AutoDock-Vina rankings, MCULE toxicity filtration, exclusion of ligands having less than four H-bond acceptors, filtration by ΔG cutoff, rule-of-five violation and SWISS ADME profiling. This was followed by holistic analysis of all the results, thereby leading to one promising ligand. The screened out drug molecule, MCULE-5872671137-0-1 exhibited a robust interaction with the AChE catalytic site involving 20 amino acid residues, an acceptable binding free energy of -10.2 kcal/mol in addition to a favorable SWISS ADME-profie showing no harmful effects on the human body. It can be carefully stated that the molecule, MCULE-5872671137-0-1, which is chemically (3S)-N-{4-[(4-chlorophenyl)sulfanyl]phenyl}-3-hydroxypyrrolidine-1-carboxamide could function as a significant "seed" ligand for future design of potent AChE inhibitors and/or novel neuro drugs built upon the seed-scaffold.

Biaryl scaffold-focused virtual screening for anti-aggregatory and neuroprotective effects in Alzheimer's disease

Background

Alzheimer’s disease (AD) is a primary cause of dementia in ageing population affecting more than 35 million people around the globe. It is a chronic neurodegenerative disease caused by defected folding and aggregation of amyloid beta (Aβ) protein. Aβ is formed by the cleavage of membrane embedded amyloid precursor protein (APP) by using enzyme ‘transmembrane aspartyl protease, β-secretase’. Inhibition of β-secretase is a viable strategy to prevent neurotoxicity in AD. Another strategy in the treatment of AD is inhibition of acetylcholinesterase. This inhibition reduces the degradation of acetylcholine and temporarily restores the cholinergic function of neurons and improves cognitive function. Monoamine oxidase and higher glutamate levels are also found to be linked with Aβ peptide related oxidative stress. Oxidative stress leads to reduced activity of glutamate synthase resulting in significantly higher level of glutamate in brain. The aim of this study is to perform in silico screening of a virtual library of biaryl scaffold containing compounds potentially used for the treatment of AD. Screening was done against the primary targets of AD therapeutics, acetylcholinesterase, β-secretase (BACE1), Monoamine oxidases (MAO) and N-Methyl-D-aspartate (NMDA) receptor. Compounds were screened for their inhibitory potential by employing molecular docking approach using AutoDock vina. Binding energy scores were embodied in the heatmap to display varies strengths of interactions of the ligands targeting AD.

Results

Several ligands showed notable interaction with at least two targets, but the strong interaction with all the targets is shown by very few ligands. The pharmacokinetics of the interacting ligands was also predicted. The interacting ligands have good drug-likeness and brain availability essential for drugs with intracranial targets.

Conclusion

These results suggest that biaryl scaffold may be pliable to drug development for neuroprotection in AD and that the synthesis of further analogues to optimize these properties should be considered.

Electronic supplementary material

The online version of this article (10.1186/s12868-018-0472-6) contains supplementary material, which is available to authorized users.

Computer-Aided Multi-Target Management of Emergent Alzheimer's Disease

Alzheimer's disease (AD) represents an enormous global health burden in terms of human suffering and economic cost. AD management requires a shift from the prevailing paradigm targeting pathogenesis to design and develop effective drugs with adequate success in clinical trials. Therefore, it is of interest to report a review on amyloid beta (Aβ) effects and other multi-targets including cholinesterase, NFTs, tau protein and TNF associated with brain cell death to be neuro-protective from AD. It should be noted that these molecules have been generated either by target-based or phenotypic methods. Hence, the use of recent advancements in nanomedicine and other natural compounds screening tools as a feasible alternative for circumventing specific liabilities is realized. We review recent developments in the design and identification of neuro-degenerative compounds against AD generated using current advancements in computational multi-target modeling algorithms reflected by theragnosis (combination of diagnostic tests and therapy) concern.

The effect of Benzothiazolone-2 on the expression of Metallothionein-3 in modulating Alzheimer's disease

INTRODUCTION

Metallothioneins (MTs) are a class of ubiquitously occurring low-molecular-weight cysteine- and metal-rich proteins containing sulfur-based metal clusters. MT-3 exhibits neuro-inhibitory activity. The possibility to enhance the expression of MT-3 or protect it from degradation is an attractive therapeutic target, because low levels of MT-3 were found in brains of Alzheimer's disease (AD) patients.

OBJECTIVES

The primary objective of this study was to test an enhancement of MT-3 cellular concentration after MT-3 binding treatment, which could prevent MT-3 degradation.

METHODS

MTT assay, flow-cytometry, fluorescence microscopy, quantitative real-time polymerase chain reaction, and immunodetection of MT3 were used for analysis of effect of STOCK1N-26544, STOCK1N-26929, and STOCK1N-72593 on immortalized human microglia-SV40 cell line.

RESULTS

All three tested compounds enhanced concentration of MT-3 protein in cells and surprisingly also mRNA concentration. IC50 values of tested molecules exceeded about ten times the concentration that was needed for induction of MT-3 expression. The tested compound Benzothiazolone-2 enhanced apoptosis and necrosis, but it was not of severe effect. About 80% of cells were still viable. There was no serious ROS-generation and no severe decrease in mitochondria numbers or stress induced endoplasmic reticulum changes after test treatments. The selected compound showed stable hydrophobic and electrostatic interaction during MT-3 ligand interaction.

CONCLUSION

Benzothiazolone-2 compounds significantly enhanced MT-3 protein and mRNA levels. The compounds can be looked upon as one of the probable lead compounds for future drug designing experiments in the treatment of Alzheimer's disease.

Novel ligand-based docking; molecular dynamic simulations; and absorption, distribution, metabolism, and excretion approach to analyzing potential acetylcholinesterase inhibitors for Alzheimer's disease

Acetylcholinesterase (AChE) plays an important role in Alzheimer's disease (AD). The excessive activity of AChE causes various neuronal problems, particularly dementia and neuronal cell deaths. Generally, anti-AChE drugs induce some serious neuronal side effects in humans. Therefore, this study sought to identify alternative drug molecules from natural products with fewer side effects than those of conventional drugs for treating AD. To achieve this, we developed computational methods for predicting drug and target binding affinities using the Schrodinger suite. The target and ligand molecules were retrieved from established databases. The target enzyme has 539 amino acid residues in its sequence alignment. Ligand molecules of 20 bioactive molecules were obtained from different kinds of plants, after which we performed critical analyses such as molecular docking; molecular dynamic (MD) simulations; and absorption, distribution, metabolism, and excretion (ADME) analysis. In the docking studies, the natural compound rutin showed a superior docking score of −12.335 with a good binding energy value of −73.313 kcal/mol. Based on these findings, rutin and the target complex was used to perform MD simulations to analyze rutin stability at 30 ns. In conclusion, our study demonstrates that rutin is a superior drug candidate for AD. Therefore, we propose that this molecule is worth further investigation using in vitro studies.

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

HIGHLIGHTS

Many CNS targets are being explored for multi-target drug design

New databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compounds

QSAR, virtual screening and docking methods increase the potential of rational drug design

The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D₁-R/D₂-R/5-HT_2A-R/H₃-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.

Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis

Employing the combined strategy to identify novel CCR5 inhibitors and provide a basis for rational drug design.