Assessment of the Interaction of Aggregatin Protein with Amyloid-Beta (Aβ) at the Molecular Level via In Silico Analysis.

Alzheimer's disease is a major neurodegenerative illness whose prevalence is increasing worldwide but the molecular mechanism remains unclear. There is some scientific evidence that the molecular complexity of Alzheimer's pathophysiology is associated with the formation of extracellular amyloid-beta plaques in the brain. A novel cross- phenotype association analysis of imaging genetics reported a brain atrophy susceptibility gene, namely FAM222A and the protein Aggregatin encoded by FAM222A interacts with amyloid-beta (A?)-peptide (1-42) through its N-terminal A? binding domain and facilitates A? aggregation. The function of Aggregatin protein is unknown, and its three-dimensional structure has not been analyzed experimentally yet. Our goal was to investigate the interaction of Aggregatin with A? in detail by in silico analysis, including the 3D structure prediction analysis of Aggregatin protein by homology modeling. Our analysis verified the interaction of the C-terminal domain of model protein with the N-terminal domain of A?. This is the first attempt to demonstrate the interaction of Aggregatin with the A?. These results confirmed in vitro and in vivo study reports claiming FAM222A helping to ease the aggregating of the A?-peptide.

Assessment of Interaction of Human OCT 1-3 Proteins and Metformin Using Silico Analyses.

Metformin, a drug frequently used by diabetic patients as the first-line treatment worldwide, is positively charged and is transported into the cell through human organic cation transporter (hOCT 1-3) proteins. We aimed to mimic the cellular uptake of metformin by hOCT1-3 with various bioinformatics methods and tools. 3D structure of OCT1-3 proteins was predicted by considering the structures and function of these proteins. We predicted functional regions (active and ligand binding sites) of OCT1-3 and performed comparative bioinformatics analysis. The predicted structure of hOCT1-3 was then analyzed in the Blind Docking server and the results were confirmed with predicted binding site residues and conserved domain regions. We simulated the OCT1-3 and metformin docking and also validated the docking procedure with other substrates of HOCT1-3 proteins. We selected the best poses of metformin docking simulations as per binding energy (-5.27 to -4.60 kcal/mol). Lastly, we validated the static description of protein-ligand (OCT-Metformin) interactions by performing molecular dynamics simulation. Eventually, we obtained stable simulation of OCT-metformin interaction.