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Prediction of Protein Hydration using Deep Learning
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- SATO Kochi
- Artificial Intelligence Research Center, AIST
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- OIDE Mao
- Protein Research Institute, Osaka University
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- NAKASAKO Masayoshi
- Department of Physics, Faculty of Science and Technology, Keio University
Bibliographic Information
- Other Title
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- 深層学習によるタンパク質水和構造予測
- シンソウ ガクシュウ ニ ヨル タンパクシツ スイワ コウゾウ ヨソク
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Description
<p>Hydration is necessary for protein folding, stability, and functions. The hydration structure of proteins is formed inside proteins and at the interfaces between bulk solvent and protein, and has been visualized mostly using cryogenic X-ray crystallography. From massive structural data of proteins with hydration structures, we developed a three-dimensional convolutional network to generate distributions of the existence probability of hydration water molecules on protein surfaces and in protein cavities. We also devised a positional search method of hydration water molecules based on the probability maps. The predicted hydration sites are located on an average within 0.3 Å from the experimentally identified sites in crystal structures.</p>
Journal
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- Nihon Kessho Gakkaishi
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Nihon Kessho Gakkaishi 67 (3), 170-175, 2025-08-31
The Crystallographic Society of Japan
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Details 詳細情報について
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- CRID
- 1390024013997474432
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- NII Book ID
- AN00188364
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- ISSN
- 18845576
- 03694585
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- NDL BIB ID
- 034356236
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL Search
- Crossref
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- Abstract License Flag
- Disallowed