Comprehensive in silico analyses of flavonoids elucidating the drug properties against kidney disease by targeting AIM2

Abstract

Kidney disorders are among the most common diseases and there is a scarcity of effective treatments for chronic kidney disease. There has been a progressive improvement in specific flavonoids for protective effects against kidney diseases. Flavonoids inhibit the regulatory enzymes to control inflammation-related diseases. In the present study, a hybrid approach of molecular docking analyses and molecular dynamic simulation was followed by principal component analyses and a dynamics cross-correlation matrix. In the present study, the top-ranked five flavonoids were reported, and the maximum binding affinity was observed against AIM2. Molecular docking analyses revealed that Glu_186, Phe_187, Lys_245, Glu_248, Ile_263, and Asn_265 are potent residues against AIM2 for ligand–receptor interactions. Extensive in silico analyses suggested that procyanidin is a potential molecule against AIM2. Moreover, the site-directed mutagenesis for the reported interacting residues of AIM2 could be important for further in vitro analyses. The observed novel results based on extensive computational analyses may be significant for potential drug design against renal disorders by targeting AIM2.

Investigating the Protein-Based Therapeutic Relationship between Honey Protein SHP-60 and Bevacizumab on Angiogenesis: Exploring the Synergistic Effect through In Vitro and In Silico Analysis

“…As a monoclonal antibody, the drug with the generic name “Bevacizumab”, which targets vascular endothelial growth factor and is used to treat several cancer types in conjunction with antineoplastic medicines, comprised light as well as heavy chain protein sequences . Therefore, we separately downloaded both sequences from the DrugBank database () with accession number “DB00112” . The SWISS-Model tool was used to predict the homology models of both the HC and LC of bevacizumab using a template-based model selection strategy.…”

“…For this, rigid binding assessments of the chosen peptide interactions with the target protein were calculated by using protein–peptide docking in MD simulations. Using Newton’s classical equation of motion, MD simulations were performed to forecast the protein–peptide binding status in the physiological environment. , …”

“…Throughout the simulation period, 300 K temperature and 1 atm pressure were utilized to imitate physiological circumstances, while counterions were added to neutralize the models and 0.15 M sodium chloride was added. Trajectories were saved every 100 ps (ps) for inspection, and the stability of protein–peptide interactions was determined by measuring the RMSD over time. , …”

“… 55 Therefore, we separately downloaded both sequences from the DrugBank database ( ) with accession number “DB00112”. 56 The SWISS-Model tool was used to predict the homology models of both the HC and LC of bevacizumab 57 using a template-based model selection strategy. The PDB structures of both models were selected based on their higher sequence similarity with alignment and coverage compared to the PDB repository-based template.…”

Network pharmacology combined with molecular docking and experimental verification to elucidate the effect of flavan-3-ols and aromatic resin on anxiety

“…All of the systems were further equilibrated at a constant temperature of 300 K and a pressure of 1 bar by deploying NVT using the V-rescale thermostat and NPT using the Parrinello–Rahman barostat ensemble process for 100 ps at a constant temperature of 300 K and a pressure of 1 bar. The trajectories were stored at intervals of 100 picoseconds (ps) for subsequent analysis, and the stability of the protein–ligand complex was verified through root mean square deviation (RMSD) analysis over time 32 .…”