A computational study on structural and functional consequences of nsSNPs in human dopa decarboxylase.

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson's disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening of natural compounds against wild and mutant (R1155K, A1156T and D1168A) NS3-4A protease of Hepatitis C virus.

NS3-4A, a serine protease, is a primary target for drug development against Hepatitis C Virus (HCV). However, the effectiveness of potent next-generation protease inhibitors is limited by the emergence of mutations and resulting drug resistance. To address this, in this study a structure-based drug design approach is employed to screen a large library of 7320 natural compounds against both wild-type and mutant variants of NS3-4A protease. Telaprevir, a widely used protease inhibitor, was recruited as the control drug. The top 10 compounds with favorable binding affinities underwent drug-likeness evaluation. Based on ADMET studies, complexes of NP_024762 and NP_006776 were selected for molecular dynamic simulations. Principal component analysis (PCA) was employed to explore the conformational space and protein dynamics of the protein-ligand complex using a Free Energy Landscape (FEL) approach. The cosine values obtained from FEL analysis ranged from 0 to 1, and eigenvectors with cosine values below 0.2 were chosen for further analysis. To forecast binding free energies and evaluate energy contributions per residue, the MM-PBSA method was employed. The results highlighted the crucial role of amino acids in the catalytic domain for the binding of the protease with phytochemicals. Stable associations between the top compounds and the target protease were confirmed by the formation of hydrogen bonds in the binding pocket involving residues: His1057, Gly1137, Ser1139, and Ala1157. These findings suggest the potential of these compounds for further validation through biological evaluation.Communicated by Ramaswamy H. Sarma.

Computational screening of natural compounds from Salvia plebeia R. Br. for inhibition of SARS-CoV-2 main protease.

The novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has emerged to be the reason behind the COVID-19 pandemic. It was discovered in Wuhan, China and then began spreading around the world, impacting the health of millions. Efforts for treatment have been hampered as there are no antiviral drugs that are effective against this virus. In the present study, we have explored the phytochemical constituents of Salvia plebeia R. Br., in terms of its binding affinity by targeting COVID-19 main protease (Mpro) using computational analysis. Molecular docking analysis was performed using PyRx software. The ADMET and drug-likeness properties of the top 10 compounds showing binding affinity greater than or equal to - 8.0 kcal/mol were analysed using pkCSM and DruLiTo, respectively. Based on the docking studies, it was confirmed that Rutin and Plebeiosides B were the most potent inhibitors of the main protease of SARS-CoV-2 with the best binding affinities of - 9.1 kcal/mol and - 8.9 kcal/mol, respectively. Further, the two compounds were analysed by studying their biological activity using the PASS webserver. Molecular dynamics simulation analysis was performed for the selected protein-ligand complexes to confirm their stability at 300 ns. MM-PBSA provided the basis for analyzing the affinity of the phytochemicals towards Mpro by calculating the binding energy, and secondary structure analysis indicated the stability of protease structure when it is bound to Rutin and Plebeiosides B. Altogether, the study identifies Rutin and Plebeiosides B to be potent Mpro inhibitors of SARS-CoV-2. Supplementary Information: The online version contains supplementary material available at 10.1007/s42535-021-00304-z.

Ligand-based design approach of potential Bcl-2 inhibitors for cancer chemotherapy.

BACKGROUND AND OBJECTIVES: Overexpression of prosurvival Bcl-2 family members make tumor cells resistant to conventional cancer therapeutic agents. It is commonly observed feature in many different types of human tumors. Hence, small-molecules as Bcl-2 inhibitors may have a promising therapeutic potential for the treatment of human cancer. The given study focusses on development of novel and small Bcl-2 inhibitors using ligand-based drug design approach. METHODS: Ligand based pharmacophore was generated using the PHASE tool of Schrödinger and screened ZINC database through ZINCPharmer webserver to identify compounds with similar features. Compounds having good fitness score were selected for molecular docking and binding interactions were compared with drugs in market as well as trials. QSAR model was generated using advanced AutoQSAR tool and validated for prediction of unknown compounds. QSAR prediction of in silico active identified three potential compounds and were subjected to investigate stability by molecular dynamics simulations and MM-PBSA binding energy calculations. RESULTS: Study identified three in silico potential molecules with good stability and binding affinity. Further substructure search and pIC50 value prediction has identified six more molecules. Total nine molecules have demonstrated good drug likeness features. CONCLUSION: Final oral rat LD50 calculation of nine molecules has identified three hit molecules i.e., ZINC76760927, ZINC76768675 and ZINC52767796 for further in vitro and in vivo testing as safe and potential Bcl-2 inhibitors.

Screening of natural compounds from Cyperus rotundus Linn against SARS-CoV-2 main protease (Mpro): An integrated computational approach.

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that has been spreading across the globe. The World Health Organization (WHO) declared it as a public health emergency. The treatment of COVID-19 has been hampered due to the lack of effective therapeutic efforts. Main Protease (Mpro) is a key enzyme in the viral replication cycle and its non-specificity to human protease makes it a potential drug target. Cyperus rotundus Linn, which belongs to the Cyperaceae family, is a traditional herbal medicine that has been widely studied for its antiviral properties. In this study, a computational approach was used to screen natural compounds from C. rotundus Linn using BIOVIA Discovery Suite and novel potential molecules against Mpro of SARS-CoV-2 were predicted. Molecular docking was performed using LibDock protocol and selected ligands were further subjected to docking analysis by CDOCKER. The docking scores of the selected ligands were compared with standard antiretroviral drugs such as lopinavir and ritonavir to assess their binding potentials. Interaction pharmacophore analysis was then performed for the compounds exhibiting good binding scores to evaluate their protein-ligand interactions. The selected protein-ligand complexes were subjected to molecular dynamics simulation for 50 ns. Results of binding free energy analysis revealed that two compounds-ß-amyrin and stigmasta-5,22-dien-3-ol-exhibited the best binding interactions and stability. Finally, absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were performed to understand the pharmacokinetic properties and safety profile of the compounds. The overall results indicate that the phytochemicals from Cyperus rotundus Linn, namely ß-amyrin and stigmasta-5,22-dien-3-ol, can be screened as potential inhibitors of SARS-CoV-2 Mpro.

Structure-based design approach of potential BCL-2 inhibitors for cancer chemotherapy.

B-cell lymphoma 2 (BCL-2) family is one of the chief regulators of cellular apoptosis. The intricate interactions between pro-apoptotic and anti-apoptotic genes of the BCL-2 family dictate the apoptotic balance of the cell. An overexpression of the anti-apoptotic members of BCL-2 is indicative of cell death evasion and cancer metastasis. Among the four BCL-2 homology domains, the BH3 domain plays a key role in the suppression of BCL-2 expression. Therefore, BH3-mimetic drugs are currently investigated for their suitability as BCL-2 inhibitors. In the present study, we followed a structure-based pharmacophore modelling approach to identify BH3-mimetic small molecules, to formulate a more precise and targeted cancer treatment regimen. To identify proteins with similar binding features, a structure-based pharmacophore model was generated based on the structure of Bcl-2 complexed with Venetoclax (PDB-ID:6O0K). Compounds with good fitness score and pharmacophore features, screened from the ZINC database, were subjected to (i) molecular docking studies, (ii) molecular mechanics-generalized Born surface area (MM-GBSA), and (iii) absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction. From the analysis, two molecules were identified: ZINC68728276 and ZINC14166367, with docking scores of -7.323 and -8.649 kcal/mol and free binding energies (MM-GBSA) of -72.913 and -72.291 kcal/mol, respectively. The structural parameters and binding affinity of these complexes were validated through molecular dynamics simulation and molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) free energy calculations and compared with Venetoclax. The results indicated stability and good binding affinity of both the compounds. The study identified ZINC68728276 and ZINC14166367 as in silico potential Bcl-2 inhibitors, which can be further considered for in vitro studies.