Integration of cloud-based molecular networking and docking for enhanced umami peptide screening from Pixian douban

Abstract

This study presents an innovative cloud-based approach, using Pixian Douban, a well-known Chinese fermented seasoning, as a case study, to improve the identification of umami peptides and explore their interactions with the T1R1/T1R3 receptor. A feature-based molecular networking method was utilized to rapidly identify a total of eighteen peptides, including seven previously unrecorded ones. Notably, the umami threshold of QIVK in an aqueous solution was determined to be 0.3215 mmol/L, surpassing the majority of peptides reported in the past three years. Molecular docking analysis further revealed the strong binding of QIVK to T1R3 receptor residues through hydrogen bonds, as well as interactions via salt bridges and electrostatic attractions. As a result, this research significantly contributes to the efficient screening of umami peptides and the elucidation of the molecular basis of umami sensory perception in complex food systems.

Keywords

• Pixian douban
• Umami peptides
• Feature-based molecular networking (FBMN)
• Molecular docking