Constructing ligand affinity prediction model of proton-coupled oligopeptide transporter to screen functional food ingredients as novel substrates
| Project/Area Number |
26560060
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Eating habits
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| Research Institution | University of Shizuoka |
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Principal Investigator |
Ito Keisuke 静岡県立大学, 食品栄養科学部, 准教授 (40580460)
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| Co-Investigator(Kenkyū-buntansha) |
三好 規之 静岡県立大学, 食品栄養科学部, 准教授 (70438191)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2016: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2014: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | ペプチド輸送体 / 機能性ペプチド / 食品機能成分 / 生体吸収 / ハイスループットスクリーニング / Saccharomyces cerevisiae / ジペプチド / トリペプチド |
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| 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.
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Report
(4 results)
Research Products
(19 results)
