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論文
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Sugimori, Kimikazu ; Shuku, Tomofumi ; Sugiyama, Ayumu ; Nagao, Hidemi ; Sakurai, Takeshi ; Nishikawa, Kiyoshi
概要:
We present a cluster model for the active site of oxidized azurin, and investigate the electronic structure of the activ
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e site of oxidized azurin by using density functional calculations with polarizable continuum model. The singly occupied molecular orbital and spin density in the model widely distribute around the Cu 3dx2-y2 and S(Cys112) 3p orbitals. The dependency of electronic properties such as partial charge density and spin density on the dielectric constant is discussed. We find that partial spin density and charge density on the copper ion become larger, when the dielectric constant increases. © 2005 Elsevier Ltd. All rights reserved.<br />Embargo Period 24 months
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| 2.
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論文
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Sugiyama, Ayumu ; Takamatsu, Yuichiro ; Purqon, Acep ; Mizukami, Taku ; Nagao, Hidemi ; Nishikawa, Kiyoshi
概要:
金沢大学大学院自然科学研究科計算科学<br />金沢大学理学部<br />We carry out the molecular dynamics(MD) simulation of type 1 blue copper protein az
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urin in room and some lower temperatures to investigate the behavior of hydration water molecules in the protein surface. In this study, we find the anomalous behavior of the water molecules, which depend on the system temperatures. These water molecules have hydrogen bond to the protein surface residues. We specify the residues type, being classified as the hydration donor and the hydration acceptor of water molecules. We analyze the residue type, and the bond length and bond strength between solvent water molecules in each temperature. Moreover, we estimate the B-factor of these residues which indicates the fluctuation of hydration residues in each temperature. B-factor values depend on the system temperatue althought the number of hydration residue do not depend on the temperature. © 2006 American Institute of Physics.
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| 3.
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論文
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Purqon, Acep ; Sugiyama, Ayumu ; Takamatsu, Yuichiro ; Nagao, Hidemi ; Nishikawa, Kiyoshi
概要:
金沢大学大学院自然科学研究科計算科学<br />金沢大学理学部<br />Some proteins in blue copper proteins have similar properties. In some cases it is
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not easy to distinguish the proteins each other. The study to recognize and classify in blue copper proteins has important roles to recognize the difference of similar properties, for examples, structures and residue sequences in blue copper proteins. There are many methods being developed to predict protein structure from many approachs, which one still not satisfactory yet. Therefore it is a challenge for scientists to develop or improve their methods. One of promising method is artificial neural networks (ANN). ANN is learning machine methods consisted of input, hidden and output layer. ANN is tested to recognize secondary structure in blue copper protein. It is found that ANN can distinguish for 7-type of secondary structure and recognize 72% secondary structure in blue copper protein. © 2006 American Institute of Physics.
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