Accurate analysis of PTM enzymes with an mRNA display/deep learning platform

• Project/Area Number: 23K23485
• Project/Area Number (Other): 22H02218 (2022-2023)
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Multi-year Fund (2024); Single-year Grants (2022-2023)
• Section: 一般
• Review Section: Basic Section 37030:Chemical biology-related
• Research Institution: The University of Tokyo
• Principal Investigator: VINOGRADOV Alexander 東京大学, 大学院理学系研究科(理学部), 特任助教 (90845819)
• Project Period (FY): 2022-04-01 – 2025-03-31
• Project Status: Granted (Fiscal Year 2024)
• Budget Amount: ¥17,290,000 (Direct Cost: ¥13,300,000、Indirect Cost: ¥3,990,000); Fiscal Year 2024: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000); Fiscal Year 2023: ¥7,800,000 (Direct Cost: ¥6,000,000、Indirect Cost: ¥1,800,000); Fiscal Year 2022: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
• Keywords: mRNA display / Deep learning / Lysine labelling / enzyme PTM / PTM / Enzymes / Machine learning

Outline of Research at the Start

In 2024, different components of the proposed platform (genetic code reprogramming, selective lysine labeling, and the mRNA display profiling pipeline) will be integrated to establish the proposed workflow. Deep learning approaches developed over the past two years will be integrated as well.