Point mutation consideration in CcO protein of the electron transfer chain by MD simulation

Identifying potential neuroprotective polyphenols targeting endoplasmic reticulum stress through an in silico approach

The endoplasmic reticulum (ER) is essential in many cellular processes, including protein processing, lipid metabolism, and calcium storage. Dysregulation of ER function has been linked with neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, etc. The primary pathological alteration explicated in the diseases is the accumulation of misfolded proteins in the neuronal cells. ER stress-associated activation of PERK-mediated pro-apoptotic cell death leads to neurodegeneration. In this study, we have primarily screened the potential polyphenols evidenced for neuroprotective activity. The 24 polyphenols were selected to explore their binding affinity towards various proteins of ER cascade such as pPERK (phospho-PERK), EIF2 (Eukaryotic Initiation Factor 2), and ATF4 (Activating Transcription Factor 4). On the basis of binding affinity, four phytopolyphenols were further selected for in-silico ADMET and molecular dynamic simulation. Among them curcumin found to be the most promising and serve as a potential hit against all three targets of ER cascade. The selected proteins' active site has demonstrated high stability of curcumin binding according to molecular dynamics findings. Though curcumin exhibited a significant hit in interaction with targets but needs to be further improved in drug-ability criteria. Thus, seventy derivatives of curcumin scaffold (from the published literature) were also screened with improve in druggability criteria, which showed good interaction with unfolded protein response related targets. The new scaffolds serve considerable potential to be developed as novel polyphenolic lead for neurodegenerative disorders.Communicated by Ramaswamy H. Sarma.

Inhibition potential of natural flavonoids against selected omicron (B.1.19) mutations in the spike receptor binding domain of SARS-CoV-2: a molecular modeling approach

The omicron (B.1.19) variant of contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is considered a variant of concern (VOC) due to its increased transmissibility and highly infectious nature. The spike receptor-binding domain (RBD) is a hotspot of mutations and is regarded as a prominent target for screening drug candidates owing to its crucial role in viral entry and immune evasion. To date, no effective therapy or antivirals have been reported; therefore, there is an urgent need for rapid screening of antivirals. An extensive molecular modelling study has been performed with the primary goal to assess the inhibition potential of natural flavonoids as inhibitors against RBD from a manually curated library. Out of 40 natural flavonoids, five natural flavonoids, namely tomentin A (-8.7 kcal/mol), tomentin C (-8.6 kcal/mol), hyperoside (-8.4 kcal/mol), catechin gallate (-8.3 kcal/mol), and corylifol A (-8.2 kcal/mol), have been considered as the top-ranked compounds based on their binding affinity and molecular interaction profiling. The state-of-the-art molecular dynamics (MD) simulations of these top-ranked compounds in complex with RBD exhibited stable dynamics and structural compactness patterns on 200 nanoseconds. Additionally, complexes of these molecules demonstrated favorable free binding energies and affirmed the docking and simulation results. Moreover, the post-simulation validation of these interacted flavonoids using principal component analysis (PCA) revealed stable interaction patterns with RBD. The integrated results suggest that tomentin A, tomentin C, hyperoside, catechin gallate, and corylifol A might be effective against the emerging variants of SARS-CoV-2 and should be further evaluated using in-vitro and in-vivo experiments.Communicated by Ramaswamy H. Sarma.

Construction of a QSAR Model Based on Flavonoids and Screening of Natural Pancreatic Lipase Inhibitors

Pancreatic lipase (PL) is a key hydrolase in lipid metabolism. Inhibition of PL activity can intervene in obesity, a global sub-health disease. The natural product is considered a good alternative to chemically synthesized drugs due to its advantages, such as low side effects. However, traditional experimental screening methods are labor-intensive and cost-consuming, and there is an urgent need to develop high-throughput screening methods for the discovery of anti-PL natural products. In this study, a high-throughput virtual screening process for anti-PL natural products is provided. Firstly, a predictable anti-PL natural product QSAR model (R2train = 0.9444, R2test = 0.8962) were developed using the artificial intelligence drug design software MolAIcal based on genetic algorithms and their conformational relationships. 1068 highly similar (FS > 0.8) natural products were rapidly enriched based on the structure-activity similarity principle, combined with the QSAR model and the ADMET model, for rapid prediction of a total of five potentially efficient anti-PL natural products (IC50pre < 2 μM). Subsequently, molecular docking, molecular dynamics simulation, and MMGBSA free energy calculation were performed to not only reveal the interaction of candidate novel natural products with the amino acid residues of PL but also to validate the stability of these novel natural compounds bound to PL. In conclusion, this study greatly simplifies the screening and discovery of anti-PL natural products and accelerates the development of novel anti-obesity functional foods.

Prediction and validation of monoclonal antibodies separation in aqueous two-phase system using molecular dynamic simulation

In order to predict how mAbs partition in 20% ethylene oxide/80% propylene oxide (v/v) random copolymer (EO₂₀PO₈₀)/water aqueous two-phase system (ATPS), a molecular dynamic simulation model was developed using Gromacs and then validated by experiments. The ATPS was applied with seven kinds of salt, including buffer salt and strong dissociation salt that were commonly employed in the purification of protein. Na₂SO₄ was shown to have the best effects on lowering EO₂₀PO₈₀ content in the aqueous phase and enhancing recovery. The content of EO₂₀PO₈₀ in the sample solution was decreased to 0.62%±0.25% and the recovery of rituximab increased to 97.88%±0.95% by adding 300 mM Na₂SO₄ into back extraction ATPS. The viability determined by ELISA was 95.57% at the same time. A strategy for constructing a prediction model for the distribution of mAbs in ATPS was proposed in consideration of this finding. Partition of trastuzumab in ATPS was predicted by the model created using this method and the prediction result was further validated by experiments. The recovery of trastuzumab reached 95.63%±2.86% under the ideal extraction conditions suggested by the prediction model.

Construction of an MLR-QSAR Model Based on Dietary Flavonoids and Screening of Natural α-Glucosidase Inhibitors

Postprandial hyperglycemia can be reduced by inhibiting α-glucosidase activity. Common α-glucosidase inhibitors such as acarbose may have various side effects. Therefore, it is important to find natural products that are non-toxic and have high α-glucosidase-inhibitory activity. In the present study, a comprehensive computational analysis of 27 dietary flavonoid compounds with α-glucosidase-inhibitory activity was performed. These included flavonoids, flavanones, isoflavonoids, dihydrochalcone, flavan-3-ols, and anthocyanins. Firstly, molecular fingerprint similarity clustering analysis was performed on the target molecules. Secondly, multiple linear regression quantitative structure-activity relationship (MLR-QSAR) models of dietary flavonoids (2D descriptors and 3D descriptors optimized), with R² of 0.927 and 0.934, respectively, were constructed using genetic algorithms. Finally, the MolNatSim tool based on the COCONUT database was used to match the similarity of each flavonoid in this study, and to sequentially perform molecular enrichment, similarity screening, and QSAR prediction. After screening, five kinds of natural product molecule (2-(3,5-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one, norartocarpetin, 2-(2,5-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one, 2-(3,4-dihydroxyphenyl)-5-hydroxy-4H-chromen-4-one, and morelosin) were finally obtained. Their IC_50pre values were 8.977, 31.949, 78.566, 87.87, and 94.136 µM, respectively. Pharmacokinetic predictions evaluated the properties of the new natural products, such as bioavailability and toxicity. Molecular docking analysis revealed the interaction of candidate novel natural flavonoid compounds with the amino acid residues of α-glucosidase. Molecular dynamics (MD) simulations and molecular mechanics/generalized Born surface area (MMGBSA) further validated the stability of these novel natural compounds bound to α-glucosidase. The present findings may provide new directions in the search for novel natural α-glucosidase inhibitors.