Figure 8

Analyses of aggregation-prone amino acid residues of Aβ1–42 peptide chain and monomer(A1)-monomer(B1) (A1B1dimer) interaction-sites, and interaction-sites between asiaticoside vs. monomer (A1) and dimer (A1B1) after docking. ¹FoldAmyloid describes a method of prediction of amyloidogenic regions from protein sequence [27], while ²AGGRESCAN predicts and evaluates of “hot spots” of aggregation in polypeptides. By identifying aggregation-prone segments in proteins, AGGRESCAN facilitates the identification of possible therapeutic targets for anti-depositional strategies in conformational diseases [28]. ³ProA: Protein Aggregation Prediction Server, which provides access to two protein aggregation propensity prediction algorithms, based on the amino acid physicochemical properties important to protein aggregation [29]. ⁴Cons-PPISP is a consensus neural network method for predicting protein-protein interaction sites. Given the structure of a protein, cons-PPISP predicts the residues that likely form the binding site for another protein. The inputs to the neural network include position-specific sequence profiles and solvent accessibilities of each residue and its spatial neighbors. The neural network is trained on known structures of protein-protein complexes [32]. ⁵KFC2 (Knowledge-based FADE and Contacts) server predicts binding “hot spots” within protein-protein interfaces by recognizing structural features indicative of important binding contacts. The server analyzes several chemical and physical features surrounding an interface residue and predicts the classification of the residue using a model trained on prior experimental data [33,34]. ^6,7For comparison, whether aisaticoside-binding site overlaps with the aggregation-prone hot-spot amino acids and/or the monomer-monomer (A1-B1dimer) intersurface interaction sites, the docking results, derived from the Molegro Virtual Docker (MVD)6 [23] and PatchDock7 [35] of Figure 9 are also shown here.