Atom-based 3D-QSAR, induced fit docking, and molecular dynamics simulations study of thieno[2,3-b]pyridines negative allosteric modulators of mGluR5

Discerning of isatin-based monoamine oxidase (MAO) inhibitors for neurodegenerative disorders by exploiting 2D, 3D-QSAR modelling and molecular dynamics simulation

Almost a billion people worldwide suffer from neurological disorders, which pose public health challenges. An important enzyme that is well-known for many neurodegenerative illnesses is monoamine oxidase (MAO). Although several promising drugs for the treatment of MAO inhibition have recently been examined, it is still necessary to identify the precise structural requirements for robust efficacy. Atom-based, field-based, and GA-MLR (genetic algorithm multiple linear regression) models were created for this investigation. All of the models have strong statistical (R2 and Q2) foundations because of both internal and external validation. Our dataset's molecule has a higher docking score than safinamide, a well-known and co-crystallized MAO-B inhibitor, as we also noticed. Using the SwissSimilarity platform, we further inquired which of our docked molecules would be the best for screening. We chose ZINC000016952895 as the screen molecule with the best binding docking score (XP score = -13.3613). Finally, the 100 ns for the ZINC000016952895-MAO-B complex in our MD investigations is stable. For compounds that we hit, also anticipate ADME properties. Our research revealed that the successful compound ZINC000016952895 might pave the way for the future development of MAO inhibitors for the treatment of neurological disease.Communicated by Ramaswamy H. Sarma.

Exploiting butyrylcholinesterase inhibitors through a combined 3-D pharmacophore modeling, QSAR, molecular docking, and molecular dynamics investigation†

Alzheimer's disease (AD), a neurodegenerative condition associated with ageing, can occur. AD gradually impairs memory and cognitive function, which leads to abnormal behavior, incapacity, and reliance. By 2050, there will likely be 100 million cases of AD in the world's population. Acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibition are significant components of AD treatment. This work developed models using the genetic method multiple linear regression, atom-based, field-based, and 3-D pharmacophore modelling. Due to internal and external validation, all of the models have solid statistical (R² > 0.81 and Q² > 0.77) underpinnings. From a pre-plated CNS library (6055), we discovered a hit compound using virtual screening on a QSAR model. Through molecular docking, additional hit compounds were investigated (XP mode). Finally, a molecular dynamics simulation revealed that the Molecule5093-4BDS complex was stable (100 ns). Finally, the expected ADME properties for the hit compounds (Molecule5093, Molecule1076, Molecule4412, Molecule1053, and Molecule3344) were found. According to the results of our investigation and the prospective hit compounds, BuChE inhibitors may be used as a treatment for AD.

Alzheimer's disease (AD), a neurodegenerative condition associated with ageing, can occur.

Atom-based 3D-QSAR and DFT analysis of 5-substituted 2-acylaminothiazole derivatives as HIV-1 latency-reversing agents

HIV-1 latency consists of viral DNA; integrated inside the host genome; it remains transcriptional silent. Combined Antiretroviral Therapy (cART) and the host immune system fail to recognize the latency cells or reservoirs, representing a major barrier to eradicating the HIV-1 infection. The Shock and Kill emerged as a promising strategy to target these cells using Latency reversal agents (LRAs); partially succeeded in producing viral mRNA but failed to reduce the size of reservoirs. In this Context, 2-acylaminothiazole class derivatives appeared as promising HIV-1 latency-reversing agents. In this study, we have developed an atom-based 3 D-QSAR model by utilizing the 49 active compounds of the 5-substituted 2-acylaminothiazoles derivatives. These compounds are further randomly divided into training (37) and test (12) datasets, yielding statistically significant R² (0.90) and Q² (0.85) results, respectively. The internal and external validation of the model shows highly robust and reliable results. Next, the model was visualized to check the favourable and unfavourable groups in terms of hydrogen bond donor, electron-withdrawing and hydrophobic group on the most active compound 96 and least active compound 30. The investigated model reveals the structural insights required for obtaining more leads that are potent. Finally, DFT calculations on the most and least active compounds were performed to support the atom-based 3 D-QSAR model. Overall, this study will aid in understanding the minimum structural requirement and functional group required for screening the novel potent leads as HIV-1 latency reversal agents.Communicated by Ramaswamy H. Sarma.

In vitro and In silico Analysis of the Anti-diabetic and Anti-microbial Activity of Cichorium intybus Leaf extracts

OBJECTIVE

To screen the selected phytochemicals against diabetes by docking studies in comparison with experimental analysis.

METHODS

Ethanol crude extract was obtained from the leaves of C.intybus and its chemical compounds were identified using GC- MS. Docking studies were carried out for selected phytochemicals to find the binding affinity and H-bond interaction using Schrodinger suite. Dynamic simulations were carried out for protein-ligand complex up to 50ns using desmond OPLS AA forcefield and α- Amylase and α- Glucosidase assay were carried for the ethanolic extract to infer its inhibition.

RESULTS

Four compounds were chosen for induced fit docking based on the docking score and glide energy obtained from GLIDE-XP docking. The compounds were docked with the protein target human aldose reductase (PDB ID: 2FZD) for checking the anti-diabetic nature. The molecular dynamics simulations were carried out for the most favorable compounds and stability was checked during the simulations. The ethanol extract exhibits significant α-amylase and α-glucosidase inhibitory activities with an IC50 value of 38μg and 88μg dry extract, respectively, and well compared with standard acarbose drug. The antimicrobial activity was also carried out for various extracts (Chloroform, Ethyl acetate, and Ethanol) of the same (C. intybus) screened against four selected human pathogens. Compared to other solvent extracts, ethanol and chloroform extracts show better inhibition and their minimal inhibitory concentration (MIC) value has been calculated.

CONCLUSION

In silico studies and in vitro studies reveals that C. intybus plant compounds have more potent for treating diabetes.

Structural dynamic studies on identification of EGCG analogues for the inhibition of Human Papillomavirus E7

High risk human papillomaviruses are highly associated with the cervical carcinoma and the other genital tumors. Development of cervical cancer passes through the multistep process initiated from benign cyst to increasingly severe premalignant dysplastic lesions in an epithelium. Replication of this virus occurs in the fatal differentiating epithelium and involves in the activation of cellular DNA replication proteins. The oncoprotein E7 of human papillomavirus expressed in the lower epithelial layers constrains the cells into S-phase constructing an environment favorable for genome replication and cell proliferation. To date, no suitable drug molecules exist to treat HPV infection whereas anticipation of novel anti-HPV chemotherapies with distinctive mode of actions and identification of potential drugs are crucial to a greater extent. Hence, our present study focused on identification of compounds analogue to EGCG, a green tea molecule which is considered to be safe to use for mammalian systems towards treatment of cancer. A three dimensional similarity search on the small molecule library from natural product database using EGCG identified 11 potential small molecules based on their structural similarity. The docking strategies were implemented with acquired small molecules and identification of the key interactions between protein and compounds were carried out through binding free energy calculations. The conformational changes between the apoprotein and complexes were analyzed through simulation performed thrice demonstrating the dynamical and structural effects of the protein induced by the compounds signifying the domination. The analysis of the conformational stability provoked us to describe the features of the best identified small molecules through electronic structure calculations. Overall, our study provides the basis for structural insights of the identified potential identified small molecules and EGCG. Hence, the identified analogue of EGCG can be potent inhibitors against the HPV 16 E7 oncoprotein.

Computational Drug Design Applied to the Study of Metabotropic Glutamate Receptors

Metabotropic glutamate (mGlu) receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. Here we review the application of computational methods to the study of this family of receptors. X-ray structures of the extracellular and 7-transmembrane domains have played an important role to enable structure-based modeling approaches, whilst we also discuss the successful application of ligand-based methods. We summarize the literature and highlight the areas where modeling and experiment have delivered important understanding for mGlu receptor drug discovery. Finally, we offer suggestions of future areas of opportunity for computational work.