| Project/Area Number |
17500191
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Bioinformatics/Life informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
SHIMIZU Kentaro The University of Tokyo, Graduate School of Agricultural and Life Sciences, Professor (80178970)
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| Co-Investigator(Kenkyū-buntansha) |
NAKAMURA Shugo The University of Tokyo, Graduate School of Agricultural and Life Sciences, Associate Professor (90272442)
TERADA Tohru The University of Tokyo, Graduate School of Agricultural and Life Scienees, Project, Associate Professor (40359641)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,650,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥150,000)
Fiscal Year 2007: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2006: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 2005: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | bioinformatics / proteome / protein structure prediction / protein structure refinement / protein structure assessment / molecular dynamics simulation / 蛋白質 / 構造予測 / 構造評価 / 構造精密化 / 分子動力学法 |
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| Research Abstract |
Comparative modeling is a powerful method that easily predicts a considerably accurate structure of a protein by using a template structure having a similar amino-acid sequence to the target protein. However, in the region where the amino acid sequence is different between the target and the template, the predicted structure remains unreliable. In such a case, the model has to be refined. In this study, we explored the possibility of a molecular dynamics-based method, using the human SAP Src Homology 2 (SH2) domain as the modeling target. The multicanonical method was used to alleviate the multiple-minima problem and the generalised Born/surface area model was used to reduce the computational cost. In addition, position restraints were imposed on the atoms in the reliable regions to avoid unnecessary conformational sampling. We found that the most populated conformational clusters in the ensemble of the model agreed well with one of the two major clusters in the ensemble of the reference simulation starting from the crystal structure. This demonstrates that the current refinement method can significantly improve the accuracy of an unreliable region in a comparative model. In this study, we also developed the method for protein structure assessment. Most protein structure prediction programs generate a set of structures of various qualities (candidates). It is necessary to select some models that are expected to have native structures from the candidates. Our method evaluates quality of a protein structure from many aspects of protein structures. This method scores each amino acid at each position in the sequences based on its degree of correlation to structural environment. Structural environment is defined based on protein local secondary structure, the area of the residue buried in the protein and inaccessible to solvent, and the fraction of side-chain area that is covered by polar atoms like water molecule.
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