2013 Fiscal Year Final Research Report
Macromolecular Potential Energy Decoder Based on Graphical Model
| Project/Area Number |
23650068
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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 |
Intelligent informatics
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| Research Institution | Tokyo Institute of Technology (2012-2013)
Chiba University (2011) |
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Principal Investigator |
SHINOZAKI Takahiro 東京工業大学, 総合理工学研究科(研究院), 准教授 (80447903)
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| Co-Investigator(Kenkyū-buntansha) |
SHINODA Shinoda 東京工業大学, 大学院・情報理工学研究科, 教授 (10343097)
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| Co-Investigator(Renkei-kenkyūsha) |
SEKIJIMA Masakazu 東京工業大学, 学術国際情報センター, 准教授 (80371053)
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| Project Period (FY) |
2011 – 2013
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| Keywords | タンパク質 / 立体構造 / 因子グラフ / Max-Sumアルゴリズム / MCMC / ポテンシャルエネルギー |
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| Research Abstract |
Knowing tertiary structure is important to understand and predict protein function. However, it is an open question how to predict the tertiary structure of proteins from a sequence of amino acids. In this project, Slice Chain Max-Sum (SCMS) algorithm has been proposed. This method represents the potential function of a protein molecule as a factor graph, which is a kind of a graphical model. The factor graph is converted into a linearly structured one according to a slicing of the molecule in 3D space. Based on the converted graph, max-sum search is performed in combination with node-wise local MCMC sampling that approximates continuous variables by discrete ones. Experimental results show that SCMS is more efficient than conventional MCMC method. It is also shown that improved version of SCMS (i.e. SCMS2.0) outperforms MCMC method that is reinforced by the quasi-Newton method.
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