In silico pentapeptide design for the inhibition between S100 calcium-binding A9 (S100A9) proteins

Abstract

Context

S100 calcium-binding protein A9 (S100A9) is easily assembled into amyloid aggregates in solution. These amyloid aggregates cause retinal toxicity and act as an attachment core for Aβ fibrillar plaques that contribute to Alzheimer’s disease progression. The overexpression of S100A9 is also noticed in various malignancies. Therefore, the S100A9 amyloid formation inhibition is of significant interest. In comparison with small-molecule drugs, short peptides demonstrate higher specificity, potency, and biosafety. Hence, it could be beneficial to identify potential peptides to inhibit or disrupt S100A9 amyloid aggregation. Typical peptide design and identification via experimental means requires extensive preparation procedures and is limited to random selection of peptides. Virtual screening therefore offers an unbiased, higher throughput, and economically efficient approach in peptide drug development. Here, we reported in silico pentapeptide design against S100A9 and studied the interaction of pentapeptide with S100A9 that leads to the binding of the peptide with S100A9.

Method

Docking simulation resulted in three top binding free energy tripeptides (WWF, WPW, and YWF) with comparable affinity towards a known S100A9 inhibitor (polyphenol oleuropein aglycone; OleA). Subsequently, pentapeptides that consist of the three core tripeptides were selected from a pre-constructed pentapeptide library for further evaluation with docking simulation. Based on best docked binding free energy, two pentapeptides (WWPWH and WPWYW) were selected and subjected to 500 ns molecular dynamics (MD) simulation to study the important features that lead to the binding with S100A9. MMGBSA binding free energy calculation estimated − 30.38, − 24.58, and − 30.31 kcal/mol for WWPWH, WPWYW, and OleA, respectively. The main driving force for pentapeptide-S100A9 recognition was contributed by the electrostatic interaction. The results demonstrate that at in silico level, this workflow is able to design potential pentapeptides that are comparable with OleA and might be the lead molecule for future use to disaggregate S100A9 fibrils.