BIOCOMPUTATIONAL AND PHARMACOLOGICAL ANALYSIS OF PHYTOCHEMICALS FROM ZINGIBER OFFICINALE (GINGER), ALLIUM SATIVUM (GARLIC), AND MURRAYAKOENIGII (CURRY LEAF) IN CONTRAST TO TYPE 2-DIABETES

Objective: This study was aimed to analyze the inhibitory effect of the flavonoid class of phytochemicals present in ginger (Zingiber Officinale), garlic (Allium sativum), and curry leaf (Murrayakoenigii) against some receptors of type-2 diabetes such as human aldose reductase receptor, mitogen synthase kinase receptor, as well as dipeptidyl peptidase receptor by implementing several in silico analysis techniques. Methods: The 3D structures of the flavonoid class of phytochemicals of all the three plants were retrieved from the PubChem database in 3D SDF format and were converted to PDB format using PyMol software. These phytochemicals were subjected to in silico tools such as SwissADME,  Pre-ADMET, and iMODS web server. The PDB-IDs of the targeted receptors human aldose reductase, dipeptidyl peptidase-IV, and mitogen synthase kinase were retrieved from Protein Data Bank in PDB format. All these receptors were then prepared for docking procedure using Autodock Tools. Now, both the prepared proteins and ligands were subjected to docking analysis using Pyrex (AutodockVina). Results: Naringenin and kaempferol showed excellent docking results with the aldose reductase receptor. On the other hand, rutin showed the best docking score with dipeptidyl peptidase receptor-IV, whereas, epigallocatechin showed the best docking results with mitogen synthase kinase receptor. The ADME analysis showed that resveratrol had the best gastrointestinal absorption as well as high blood-brain barrier permeability. Conclusion: Overall, the molecular docking results when analyzed showed a good binding affinity with the targeted receptors of diabetes. The ADME analysis and molecular docking results of the phytochemicals concluded that these compounds can be used as a potential cure for treating diabetes

HIGH-THROUGHPUT SCREENING AND DYNAMIC STUDIES OF SELECTED COMPOUNDS AGAINST SARS-COV-2

Objective: This study was aimed to analyze the inhibitory effect of the drugs used in nanocarrier as well as nanoparticles formulation based drug delivery system selected from PubChem database literature against 3CLpro (3C-like protease) receptor of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) by implementing several in silico analysis techniques. Methods: This paper detailed a molecular docking-based virtual screening of 5240 compounds previously utilized in nanoparticle and nanocarrier drug delivery systems utilizing AutoDock Vina software on 3CL protease to discover potential inhibitors using a molecular docking technique. Results: According to the results of the screening, the top two compounds, PubChem Id 58823276 and PubChem Id 60838 exhibited a high affinity for the 3CL protease binding region. Their binding affinities were-9.6 and-8.5 kJ/mol, indicating that they were tightly bound to the target receptor, respectively. These results outperformed those obtained using the co-crystallized native ligand, which exhibited a binding affinity of-7.4 kJ/mol. PubChem Id 60838, the main hit compound in terms of both binding affinity and ADMET analysis, displayed substantial deformability after MD simulation. As a result of the VS and molecular docking techniques, novel 3CL protease inhibitors from the PubChem database were discovered using the Lipinski rule of five and functional molecular contacts with the target protein, as evidenced by the findings of this work. Conclusion: The findings suggest that the compounds discovered may represent attractive opportunities for the development of COVID-19 3CLpro inhibitors and that they need further evaluation and investigation

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools : a molecular modeling based retrospective study

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.Peer reviewe

Screening Of Zinc Database Against Streptococcal Cysteine Protease Enzyme For Identification Of Novel Group A Streptococcus Inhibitors

Inhibition of streptococcal cysteine protease has recently emerged as quite a promising target to treat severe cases of Group A Streptococcus infections. For the identification of streptococcal cysteine protease inhibitors, structure-based virtual screening (SBVS) of ZINC Database was performed. The docking protocol was performed with the help of AutoDock Tools and AutoDock Vina software. Based on binding affinity and similarity of interactions with our target receptor streptococcal cysteine protease, 4 hit compounds were identified, which were further subjected to ADMET (Adsorption, Distribution, Metabolism, Excretion, Toxicity) and Drug-likeness to identify the best hit compound. The most potent compound showed binding of -7.7 KJ/mol with receptor streptococcal cysteine protease. It also showed 6 similar amino acid interactions with the receptor’s native ligand along with good ADME and Drug-likeness properties. Furthermore, the molecular dynamics simulation analysis revealed that the complex formed between the protein streptococcal cysteine protease and the hit compound ZINC000205429716 had good structural stability. The current study reveals the successful use of in silico SBVS methods for the identification of novel and possible streptococcal cysteine protease inhibitors, with compound ZINC000205429716 serving as a potential lead for the creation of Group A Streptococcus inhibitors

Predicting the molecular mechanism-driven progression of breast cancer through comprehensive network pharmacology and molecular docking approach

Abstract Identification of key regulators is a critical step toward discovering biomarker that participate in BC. A gene expression dataset of breast cancer patients was used to construct a network identifying key regulators in breast cancer. Overexpressed genes were identified with BioXpress, and then curated genes were used to construct the BC interactome network. As a result of selecting the genes with the highest degree from the BC network and tracing them, three of them were identified as novel key regulators, since they were involved at all network levels, thus serving as the backbone. There is some evidence in the literature that these genes are associated with BC. In order to treat BC, drugs that can simultaneously interact with multiple targets are promising. When compared with single-target drugs, multi-target drugs have higher efficacy, improved safety profile, and are easier to administer. The haplotype and LD studies of the FN1 gene revealed that the identified variations rs6707530 and rs1250248 may both cause TB, and endometriosis respectively. Interethnic differences in SNP and haplotype frequencies might explain the unpredictability in association studies and may contribute to predicting the pharmacokinetics and pharmacodynamics of drugs using FN1

Machine learning driven web-based app platform for the discovery of monoamine oxidase B inhibitors

Abstract Monoamine oxidases (MAOs), specifically MAO-A and MAO-B, play important roles in the breakdown of monoamine neurotransmitters. Therefore, MAO inhibitors are crucial for treating various neurodegenerative disorders, including Parkinson's disease (PD), Alzheimer’s disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, we developed a novel cheminformatics pipeline by generating three diverse molecular feature-based machine learning-assisted quantitative structural activity relationship (ML-QSAR) models concerning MAO-B inhibition. PubChem fingerprints, substructure fingerprints, and one-dimensional (1D) and two-dimensional (2D) molecular descriptors were implemented to unravel the structural insights responsible for decoding the origin of MAO-B inhibition in 249 non-reductant molecules. Based on a random forest ML algorithm, the final PubChem fingerprint, substructure fingerprint, and 1D and 2D molecular descriptor prediction models demonstrated significant robustness, with correlation coefficients of 0.9863, 0.9796, and 0.9852, respectively. The significant features of each predictive model responsible for MAO-B inhibition were extracted using a comprehensive variance importance plot (VIP) and correlation matrix analysis. The final predictive models were further developed as a web application, MAO-B-pred ( https://mao-b-pred.streamlit.app/ ), to allow users to predict the bioactivity of molecules against MAO-B. Molecular docking and dynamics studies were conducted to gain insight into the atomic-level molecular interactions between the ligand-receptor complexes. These findings were compared with the structural features obtained from the ML-QSAR models, which supported the mechanistic understanding of the binding phenomena. The presented models have the potential to serve as tools for identifying crucial molecular characteristics for the rational design of MAO-B target inhibitors, which may be used to develop effective drugs for neurodegenerative disorders