Constructing ligand affinity prediction model of proton-coupled oligopeptide transporter to screen functional food ingredients as novel substrates

2016 Fiscal Year Final Research Report

• Project/Area Number: 26560060
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Eating habits
• Research Institution: University of Shizuoka
• Principal Investigator: Ito Keisuke 静岡県立大学, 食品栄養科学部, 准教授 (40580460)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: ペプチド輸送体

Outline of Final Research Achievements

The entire spectrum of substrate multispecificity of human peptide transporter, hPEPT2, was elucidated by the analysis using a dipeptide library. Using the dataset of dipeptide sequence with its affinity data, in silico affinity prediction models were constructed. The model construction process guided the understanding of dipeptide interactions with hPEPT2. Three physicochemical property features “Side-chain contribution to protein stability”, “Side chain interaction parameter” and ” Isoelectric point” can be considered as the main determining factors for dipeptide recognition of hPEPT2. An in silico affinity prediction model using “combinations of physicochemical compound properties” was also constructed with reasonable accuracy. This concept is applicable to screen functional food ingredients, in addition to 8,400 types of di/tripeptides, as novel substrates for the peptide transporter.

Free Research Field

食品機能開発化学