Identification of potential human pancreatic α-amylase inhibitors from natural products by molecular docking, MM/GBSA calculations, MD simulations, and ADMET analysis

Structural insight into the lead identification of a dual inhibitor of PDE1B and PDE10A: Integrating pharmacophore-based virtual screening, molecular docking, and structure-activity-relationship approaches

Schizophrenia is a chronic neuropsychiatric disorder affecting more than 1% of the world's population. Current antipsychotic treatments show inadequacy in mitigating the negative and cognitive symptoms of schizophrenia. In addition, these medications cause undesirable extrapyramidal side effects. According to the studies, inhibition of phosphodiesterase (PDE) 1B and PDE10A simultaneously can alleviate positive, negative, and cognitive symptoms of schizophrenia. Thus, this study aims to identify new dual inhibitors of PDE1B and PDE10A using ligand-based pharmacophore modelling, virtual screening, and molecular docking studies. Accordingly, the generated pharmacophore models of PDE1B and PDE10A comprised hydrogen bond acceptor, aromatic ring, and hydrophobic features. These features were essential for retrieving the active hits from the Universal Natural Product Database in the virtual screening. Additional filters were subsequently employed to identify potential hits that could be developed into central nervous system-active compounds. Hits meeting all the screening criteria were subjected to docking studies with PDE1B and PDE10A. Among these hits, UNPD167314 exhibited significant binding affinities for the target receptors. It occupied the P-clamp and displayed hydrophobic, aromatic, and hydrogen bond interactions with the active site residues of both receptors, thus selected as a lead compound for the design of potent and selective dual inhibitors. The structural modifications of UNPD167314 resulted in the design of 35 novel inhibitors. Out of 35, four compounds exhibited high and comparable binding affinities for both PDE1B and PDE10A, making them promising candidates for further evaluation and optimisation.

Assessment of the in vitro anti-diabetic activity with molecular dynamic simulations of limonoids isolated from Adalia lemon peels

Limonoids are important constituents of citrus that have a significant impact on promoting human health. Therefore, the primary focus of this research was to assess the overall limonoid content and isolate limonoids from Adalia lemon (Citrus limon L.) peels for their potential use as antioxidants and anti-diabetic agents. The levels of limonoid aglycones in the C. limon peel extract were quantified through a colorimetric assay, revealing a concentration of 16.53 ± 0.93 mg/L limonin equivalent. Furthermore, the total concentration of limonoid glucosides was determined to be 54.38 ± 1.02 mg/L. The study successfully identified five isolated limonoids, namely limonin, deacetylnomilin, nomilin, obacunone 17-O-β-D-glucopyranoside, and limonin 17-O-β-D-glucopyranoside, along with their respective yields. The efficacy of the limonoids-rich extract and the five isolated compounds was evaluated at three different concentrations (50, 100, and 200 µg/mL). It was found that both obacunone 17-O-β-D-glucopyranoside and limonin 17-O-β-D-glucopyranoside possessed the highest antioxidant, free radical scavenging, and anti-diabetic activities, followed by deacetylnomilin, and then the limonoids-rich extract. The molecular dynamic simulations were conducted to predict the behavior of the isolated compounds upon binding to the protein's active site, as well as their interaction and stability. The results revealed that limonin 17-O-β-D-glucopyranoside bound to the protein complex system exhibited a relatively more stable conformation than the Apo system. The analysis of Solvent Accessible Surface Area (SASA), in conjunction with the data obtained from Root-Mean-Square Deviation (RMSD), Root-Mean-Square Fluctuation (RMSF), and Radius of Gyration (ROG) computations, provided further evidence that the limonin 17-O-β-D-glucopyranoside complex system remained stable within the catalytic domain binding site of the human pancreatic alpha-amylase (HPA)-receptor. The research findings suggest that the limonoids found in Adalia lemon peels have the potential to be used as effective natural substances in creating innovative therapeutic treatments for conditions related to oxidative stress and disorders in carbohydrate metabolism.

Purification of ursolic acid and β-sitosterol from endophytic Alternaria alternata for their alpha-amylase inhibitory activity

Fungal endophytes are a known warehouse of bioactive compounds with multifarious applications. In the present investigation two compounds, β-Sitosterol (1) and ursolic acid (2), were isolated from Alternaria alternata, an endophytic fungus associated with Morus alba Linn for the first time. The structure of the compounds was elucidated on the basis of comprehensive spectral analysis (UV, IR, 1 H-, 13 C- and 2D-NMR, as well as HRESI-MS). In the in vitro alpha amylase inhibitory assay both compounds (1) and (2) show potent antidiabetic activity. In support, Docking studies indicate significant binding affinity of the isolated compounds. Hence from the present study, it can be concluded that endophytic fungi in Morus alba Linn can find use in antidiabetic drug development in the medicinal industry.Communicated by Ramaswamy H. Sarma.

Targeting α-amylase enzyme through multi-fold structure-based virtual screening and molecular dynamic simulation

α-Amylase play important role in hydrolyses of α-bonds of large α-linked polysaccharides; thus, it is a potential drug target in diabetes mellites (DM) and its inhibition is one of the therapeutic strategies in DM. With the aim to discover novel and safer therapeutic molecules to combat diabetes, a huge dataset of ∼0.69 billion compounds from ZINC20 database were screened against α-amylase using multi-fold structure-based virtual screening protocol. Based on receptor-based pharmacophore model, docking results, pharmacokinetic profile, molecular interactions with α-amylase, several compounds were retrieved as lead candidates to be further scrutinized in the in vitro assay and in vivo animal testing. Among the selected hits, CP26 exhibited the highest binding free energy in MMGB-SA analysis, followed by CP7 and CP9, which is higher than the binding free energy of acarbose. While CP20 and CP21 showed comparative binding free energy to acarbose. All the selected ligands showed acceptable binding energy range, therefore, several molecules with enhanced efficacy can be designed by derivatizing these molecules. The in-silico results indicates that the selected molecules could serve as potential selective α-amylase inhibitors and can be used for the treatment of diabetes.Communicated by Ramaswamy H. Sarma.

In silico exploration of CB2 receptor agonist in the management of neuroinflammatory conditions by pharmacophore modeling

Endocannabinoid system plays a pivotal role in controlling neuroinflammation, and modulating this system may not only aid in managing symptoms of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, Multiple sclerosis, Epilepsy, Central and Peripheral neuropathic pain, but also, have the potential to target these diseases at an early-stage. In the present study, six different pharmacophore hypotheses were generated from Cannabidiol (CBD)-Cannabinoid Receptor subtype-2 (CB2) and then Zinc database was screened for identification of hit molecules. Identified 215 hit molecules were subjected to preliminary screening with ADMET and drug likeness properties, and about 48 molecules were found with no violations and toxicity properties. In molecular docking studies, six compounds showed better binding energy than CBD and β-caryophyllene (known inhibitor of CB2). These six molecules were designated as leads and subjected to re-docking with glide tool and Lead1 (ZINC000078815430) showed docking score of -9.877 kcal/mol, whereas CBD and β-caryophyllene showed score of -9.664 and -8.499 kcal/mol, respectively. Lead1 and CBD were evaluated for stability studies with Desmond tool by molecular dynamic simulation studies. Lead1 showed better stability than CBD in all studied parameters such as RMSD, RMSF, SSE, Rg, SASA, etc. In MM-GBSA free energy calculations, ΔGbinding energy of CB2-CBD complex and CB2-Lead1 were found to be -103.13±11.19 and -107.94±5.42 kcal/mol, respectively. Six lead molecules stated in the study hold promise with respect to CBD agonistic activity for treating and/or managing chronic conditions and can be explored as an alternative for early-stage cure, which has not yet been experimentally explored.

Identification of Druggable Binding Sites and Small Molecules as Modulators of TMC1

Our ability to hear and maintain balance relies on the proper functioning of inner ear sensory hair cells, which translate mechanical stimuli into electrical signals via mechano-electrical transducer (MET) channels, composed of TMC1/2 proteins. However, the therapeutic use of ototoxic drugs, such as aminoglycosides and cisplatin, which can enter hair cells through MET channels, often leads to profound auditory and vestibular dysfunction. Despite extensive research on otoprotective compounds targeting MET channels, our understanding of how small molecule modulators interact with these channels remains limited, hampering the discovery of novel compounds. Here, we propose a structure-based screening approach, integrating 3D-pharmacophore modeling, molecular simulations, and experimental validation. Our pipeline successfully identified several novel compounds and FDA-approved drugs that reduced dye uptake in cultured cochlear explants, indicating MET modulation activity. Molecular docking and free-energy estimations for binding allowed us to identify three potential drug binding sites within the channel pore, phospholipids, and key amino acids involved in modulator interactions. We also identified shared ligand-binding features between TMC and structurally related TMEM16 protein families, providing novel insights into their distinct inhibition, while potentially guiding the rational design of MET-channel-specific modulators. Our pipeline offers a broad application to discover small molecule modulators for a wide spectrum of mechanosensitive ion channels.

Discovery of small molecule entry inhibitors targeting the linoleic acid binding pocket of SARS-CoV-2 spike protein

Spike glycoprotein has a significant role in the entry of SARS-CoV-2 to host cells, which makes it a potential drug target. Continued accumulation of non-synonymous mutations in the receptor binding domain of spike protein poses great challenges in identifying antiviral drugs targeting this protein. This study aims to identify potential entry inhibitors of SARS-CoV-2 using virtual screening and molecular dynamics (MD) simulations from three distinct chemical libraries including Pandemic Response Box, Drugbank and DrugCentral, comprising 6971 small molecules. The molecules were screened against a binding pocket identified in the receptor-binding domain (RBD) region of the spike protein which is known as the linoleic acid binding pocket, a highly conserved motif among several SARS-CoV-2 variants. Through virtual screening and binding free energy calculations, we identified four top-scoring compounds, MMV1579787 ([2-Oxo-2-[2-(3-phenoxyphenyl)ethylamino]ethyl]phosphonic acid), Tretinoin, MMV1633963 ((2E,4E)-5-[3-(3,5-dichlorophenoxy)phenyl]penta-2,4-dienoic acid) and Polydatin, which were previously reported to have antibacterial, antifungal or antiviral properties. These molecules showed stable binding on MD simulations over 100 ns and maintained stable interactions with TYR365, PHE338, PHE342, PHE377, TYR369, PHE374 and LEU368 of the spike protein RBD that are found to be conserved among SARS-CoV-2 variants. Our findings were further validated with free energy landscape, principal component analysis and dynamic cross-correlation analysis. Our in silico analysis of binding mode and MD simulation analyses suggest that the identified compounds may impede viral entrance by interacting with the linoleic acid binding site of the spike protein of SARS-CoV-2 regardless of its variants, and they thus demand for further in vitro and in vivo research.Communicated by Ramaswamy H. Sarma.

Repurposing FDA-approved drugs as NLRP3 inhibitors against inflammatory diseases: machine learning and molecular simulation approaches

Activation of NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) has been associated with multiple chronic pathologies, including diabetes, atherosclerosis, and rheumatoid arthritis. Moreover, histone deacetylases (HDACs), specifically HDAC6 is required for the NLRP3 inflammasome to assemble and activate. Thus, NLRP3 serves as an attractive target for the development of novel therapeutic approaches. Several companies are now attempting to develop specific modulators of the NLRP3 inflammasome, but only a handful of small molecules of NLRP3 inflammasome inhibitors, such as MCC950 and Tranilast, are currently available for clinical use. However, their use is limited due to severe side effects and short half-lives. Thus, the repurposing of FDA-approved drugs with NLRP3 inhibitory activity is needed. The present study was aimed at repurposing preexisting drugs that might act as safe and effective NLRP3 inhibitors. A library of 2,697 FDA-approved drugs was screened for binding with NLRP3 (PDB: 7ALV) using Glide (Schrödinger). The top seven FDA-approved drugs with potential binding affinities were selected based on docking scores and subjected to ADMET profiling using pkCSM and SwissADME. The binding of the ADMET-favorable FDA-approved drugs to NLRP3 was validated using MMGBSA (Prime) and Molecular Dynamics (Desmond) in the Schrödinger suite. ADMET profiling revealed that of the seven best docking drugs, empagliflozin and citicoline had good drug-likeness properties. Moreover, MMGBSA analysis and molecular dynamics demonstrated that empagliflozin and citicoline exhibited stable ligand-NLRP3 interactions in the presence of solvents. This study sheds light on the ability of various FDA-approved drugs to act as NLRP3 inhibitors.Communicated by Ramaswamy H. Sarma.

Integrative Approach for Designing Novel Triazole Derivatives as α-Glucosidase Inhibitors: QSAR, Molecular Docking, ADMET, and Molecular Dynamics Investigations

In response to the increasing prevalence of diabetes mellitus and the limitations associated with the current treatments, there is a growing need to develop novel medications for this disease. This study is focused on creating new compounds that exhibit a strong inhibition of alpha-glucosidase, which is a pivotal enzyme in diabetes control. A set of 33 triazole derivatives underwent an extensive QSAR analysis, aiming to identify the key factors influencing their inhibitory activity against α-glucosidase. Using the multiple linear regression (MLR) model, seven promising compounds were designed as potential drugs. Molecular docking and dynamics simulations were employed to shed light on the mode of interaction between the ligands and the target, and the stability of the obtained complexes. Furthermore, the pharmacokinetic properties of the designed compounds were assessed to predict their behavior in the human body. The binding free energy was also calculated using MMGBSA method and revealed favorable thermodynamic properties. The results highlighted three novel compounds with high biological activity, strong binding affinity to the target enzyme, and suitability for oral administration. These results offer interesting prospects for the development of effective and well-tolerated medications against diabetes mellitus.

Insights into the inhibitory potential of novel hydrazinyl thiazole-linked indenoquinoxaline against alpha-amylase: a comprehensive QSAR, pharmacokinetic, and molecular modeling study

The rising prevalence of diabetes necessitates the development of novel drugs, especially given the side effects associated with current medications like Acarbose and Voglibose. A series of 36 Hydrazinyl thiazole-linked indenoquinoxaline derivatives with notable activity against alpha-amylase were studied. To create a molecular model predicting alpha-amylase activity, a QSAR study was performed on these compounds. Molecular descriptors were calculated using Chem3D and Gaussian software and then correlated with their IC50 biological activities to form a dataset. This model data was refined using PCA and modeled with MLR. The model's performance was statistically verified (R2 =0.800; R adj 2 = 0.767; R cv 2 = 0.651) and its applicability domain was defined. It was predicted to possess high predictive power (R test 2   = 0.872). Based on this, new compounds were proposed, and their activities were predicted using the developed model. Additionally, their binding ability to the biological target was studied through molecular docking and dynamics. Their pharmacokinetics were also evaluated using ADMET predictions. Two designed compounds named AE and AB emerged as particularly promising, displaying properties that suggest substantial therapeutic potential and they can form stable complexes into the binding pocket of alpha-amylase enzyme.Communicated by Ramaswamy H. Sarma.

Sulfonamido, amido heterocyclic adducts of tetrazole derivatives as BACE1 inhibitors: in silico exploration

Alzheimer's disease is a neurodegenerative disorder accounting for 60-80% of dementia cases and is accompanied by a high mortality rate in patients above 70 years of age. The formation of senile plaques composed of amyloid-β protein is a hallmark of Alzheimer's disease. Beta-site APP cleaving enzyme 1 (BACE1) is a proteolytic enzyme involved in the degradation of amyloid precursor protein, which further degrades to form toxic amyloid-β fragments. Hence, inhibition of BACE1 was stated to be an effective strategy for Alzheimer's therapeutics. Keeping in mind the structures of different BACE1 inhibitors that had reached the clinical trials, we designed a library of compounds (total 164) based on a substituted 5-amino tetrazole scaffold which was an isosteric replacement of the cyclic amidine moiety, a common component of the BACE1 inhibitors which reached the clinical trials. The scaffold was linked to different structural moieties with the aid of an amide or sulfonamide bond to design some novel molecules. Molecular docking was initially performed and the top 5 molecules were selected based on docking scores and protein-ligand interactions. Furthermore, molecular dynamic simulations were performed for these molecules (3g, 7k, 8n, 9d, 9g) for 100 ns and MM-GBSA calculations were performed for each of these complexes. After critical evaluation of the obtained results, three potential molecules (9d, 8n, and 7k) were forwarded for prolonged stability studies by performing molecular dynamic simulations for 250 ns and simultaneous MM-GBSA calculations. It was observed that the compounds (9d, 8n, and 7k) were forming good interactions with the amino acid residues of the catalytic site of the enzyme with multiple non-covalent interactions. In MD simulations, the compounds have shown better stability and better binding energy throughout the runtime.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

In Silico and In Vitro Analyses to Repurpose Quercetin as a Human Pancreatic α-Amylase Inhibitor

Human pancreatic α-amylase (HPA), situated at the apex of the starch digestion hierarchy, is an attractive therapeutic approach to precisely regulate blood glucose levels, thereby efficiently managing diabetes. Polyphenols offer a natural and multifaceted approach to moderate postprandial sugar spikes, with their slight modulation in carbohydrate digestion and potential secondary benefits, such as antioxidant and anti-inflammatory effects. Taking into consideration the unfavorable side effects of currently available commercial medications, we aimed to study a library of polyphenols attributed to their remarkable antidiabetic properties and screened the most potent HPA inhibitor via a comprehensive in silico study encompassing molecular docking, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) calculation, molecular dynamics (MD) simulation, density functional theory (DFT) study, and pharmacokinetic properties followed by an in vitro assay. Significant hydrogen bonding with the catalytic triad residues of HPA, prominent MM/GBSA binding energy of -27.03 kcal/mol, and the stable nature of the protein-ligand complex with regard to 100 ns MD simulation screened quercetin as the best HPA inhibitor. Additionally, quercetin showed strong reactivity in the substrate-binding pocket of HPA and exhibited favorable pharmacokinetic properties with a considerable inhibitory concentration (IC50) of 57.37 ± 0.9 μg/mL against α-amylase. This study holds prospects for HPA inhibition and suggests quercetin as an approach to therapy for diabetes; however, it is imperative to conduct further research.