Updated on 2024/03/25

写真a

 
CHIKENJI, George
 
Organization
Graduate School of Engineering Applied Physics 3 Assistant Professor
Graduate School
Graduate School of Engineering
Undergraduate School
School of Engineering Physical Science and Engineering
Title
Assistant Professor

Degree 1

  1. Doctor of Science ( 2002.4   Osaka University ) 

Research Areas 1

  1. Others / Others  / Biophysics

Current Research Project and SDGs 4

  1. タンパク質のデノボデザイン

  2. タンパク質の構造バイオインフォマティクス

  3. 計算生物学

  4. protein structure prediction

Research History 5

  1. Assistant Professor, Department of Computational Science and Engineering, Graduate School of Engineering, Nagoya University

    2007.4

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    Country:Japan

  2. Reseach Assosiate, Department of Computational Science and Engineering, Graduate School of Engineering, Nagoya University

    2005.8 - 2007.3

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    Country:Japan

  3. Postdoctoral Fellow of MEXT Japan

    2005.4 - 2005.7

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    Country:Japan

  4. Postdoctoral Research Fellow of the Japan Society for the Promotion of Science

    2002.4 - 2005.3

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    Country:Japan

  5. Predoctoral Research Fellow of the Japan Society for the Promotion of Science (DC1)

    1999.4 - 2002.3

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    Country:Japan

Education 3

  1. Osaka University   Graduate School, Division of Natural Science   Department of Physics

    1999.4 - 2002.3

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    Country: Japan

  2. Osaka University   Graduate School, Division of Natural Science   Department of Physics

    1997.4 - 1999.3

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    Country: Japan

  3. Tokyo Metropolitan University   Faculty of Science   Department of Physics

    1993.4 - 1997.3

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    Country: Japan

Professional Memberships 2

  1. Protein Science Society of Japan

  2. * The Biophysical Society of Japan

 

Papers 23

  1. Exploration of novel & alpha;& beta;-protein folds through de novo design Reviewed International journal

    Minami Shintaro, Kobayashi Naohiro, Sugiki Toshihiko, Nagashima Toshio, Fujiwara Toshimichi, Tatsumi-Koga Rie, Chikenji George, Koga Nobuyasu

    NATURE STRUCTURAL & MOLECULAR BIOLOGY   Vol. 30 ( 8 ) page: 1132 - +   2023.8

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1038/s41594-023-01029-0

    Web of Science

  2. The Structural Rule Distinguishing a Superfold: A Case Study of Ferredoxin Fold and the Reverse Ferredoxin Fold Invited Reviewed

    Nishina Takumi, Nakajima Megumi, Sasai Masaki, Chikenji George

    MOLECULES   Vol. 27 ( 11 )   2022.6

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    Authorship:Last author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.3390/molecules27113547

    Web of Science

  3. The register shift rules for βαβ-motifs for de novo protein design Reviewed

    Hiroto Murata, Hayao Imakawa, Nobuyasu Koga, George Chikenji

    PLoS ONE   Vol. 16 ( 8 ) page: e0256895   2021

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    Authorship:Last author, Corresponding author   Language:English  

    DOI: 10.1371/journal.pone.0256895

    Web of Science

  4. MICAN-SQ: a sequential protein structure alignment program that is applicable to monomers and all types of oligomers Reviewed

    Shintaro Minami, Kengo Sawada, Motonori Ota, and George Chikenji

    Bioinformatics   Vol. 34 ( 19 ) page: 3324 - 3331   2018.10

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    Authorship:Last author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1093/bioinformatics/bty369

    Web of Science

  5. The cavity method to protein design problem Reviewed

    Tomoei Takahashi, George Chikenji, Kei Tokita

    Journal of Statistical Mechanics   Vol. 2022   page: 103403   2022.10

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    Language:English   Publishing type:Research paper (scientific journal)  

  6. Lattice protein design using Bayesian learning Reviewed

    Takahashi Tomoei, Chikenji George, Tokita Kei

    PHYSICAL REVIEW E   Vol. 104 ( 1 )   2021.7

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  7. A prospective compound screening contest identified broader inhibitors for Sirtuin 1 Reviewed International coauthorship

    Shuntaro Chiba, Masahito Ohue, Anastasiia Gryniukova, Petro Borysko, Sergey Zozulya, Nobuaki Yasuo, Ryunosuke Yoshino, Kazuyoshi Ikeda, Woong-Hee Shin, Daisuke Kihara, Mitsuo Iwadate, Hideaki Umeyama, Takaaki Ichikawa, Reiji Teramoto, Kun-Yi Hsin, Vipul Gupta, Hiroaki Kitano, Mika Sakamoto, Akiko Higuchi, Nobuaki Miura, Kei Yura, Masahiro Mochizuki, Chandrasekaran Ramakrishnan, A. Mary Thangakani, D. Velmurugan, M. Michael Gromiha, Itsuo Nakane, Nanako Uchida, Hayase Hakariya, Modong Tan, Hironori K. Nakamura, Shogo D. Suzuki, Tomoki Ito, Masahiro Kawatani, Kentaroh Kudoh, Sakurako Takashina, Kazuki Z. Yamamoto, Yoshitaka Moriwaki, Keita Oda, Daisuke Kobayashi, Tatsuya Okuno, Shintaro Minami, George Chikenji, Philip Prathipati, Chioko Nagao, Attayeb Mohsen, Mari Ito, Kenji Mizuguchi, Teruki Honma, Takashi Ishida, Takatsugu Hirokawa, Yutaka Akiyama, Masakazu Sekijima

    Scientific Reports   Vol. 9 ( 1 ) page: 19585   2019.12

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    Language:English  

  8. A prospective compound screening contest identified broader inhibitors for Sirtuin 1

    Chiba Shuntaro, Ohue Masahito, Gryniukova Anastasiia, Borysko Petro, Zozulya Sergey, Yasuo Nobuaki, Yoshino Ryunosuke, Ikeda Kazuyoshi, Shin Woong-Hee, Kihara Daisuke, Iwadate Mitsuo, Umeyama Hideaki, Ichikawa Takaaki, Teramoto Reiji, Hsin Kun-Yi, Gupta Vipul, Kitano Hiroaki, Sakamoto Mika, Higuchi Akiko, Miura Nobuaki, Yura Kei, Mochizuki Masahiro, Ramakrishnan Chandrasekaran, Thangakani A. Mary, Velmurugan D., Gromiha M. Michael, Nakane Itsuo, Uchida Nanako, Hakariya Hayase, Tan Modong, Nakamura Hironori K., Suzuki Shogo D., Ito Tomoki, Kawatani Masahiro, Kudoh Kentaroh, Takashina Sakurako, Yamamoto Kazuki Z., Moriwaki Yoshitaka, Oda Keita, Kobayashi Daisuke, Okuno Tatsuya, Minami Shintaro, Chikenji George, Prathipati Philip, Nagao Chioko, Mohsen Attayeb, Ito Mari, Mizuguchi Kenji, Honma Teruki, Ishida Takashi, Hirokawa Takatsugu, Akiyama Yutaka, Sekijima Masakazu

    SCIENTIFIC REPORTS   Vol. 9   2019.12

  9. Rules for connectivity of secondary structure elements in protein: Two-layer alpha beta sandwiches

    Minami Shintaro, Chikenji George, Ota Motonori

    PROTEIN SCIENCE   Vol. 26 ( 11 ) page: 2257 - 2267   2017.11

  10. An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes Reviewed

    Shuntaro Chiba, Takashi Ishida, Kazuyoshi Ikeda, Masahiro Mochizuki, Reiji Teramoto, Y-h Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Chandrasekaran Ramakrishnan, A. Mary Thangakani, D. Velmurugan, Michael Gromiha, Tatsuya Okuno, Koya Kato, Shintaro Minami, George Chikenji, Shogo D. Suzuki, Keisuke Yanagisawa, Woong-Hee Shin, Daisuke Kihara, Kazuki Yamamoto, Yoshitaka Moriwaki, Nobuaki Yasuo, Ryunosuke Yoshino, Sergey Zozulya, Petro Borysko, Roman Stavniichuk, Teruki Honma, Takatsugu Hirokawa, Yutaka Akiyama, and Masakazu Sekijima

    Scientific Reports   Vol. 7   page: s41598-017-10275-4   2017.9

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    Language:English   Publishing type:Research paper (scientific journal)  

  11. An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes

    Chiba Shuntaro, Ishida Takashi, Ikeda Kazuyoshi, Mochizuki Masahiro, Teramoto Reiji, Taguchi Y-h., Iwadate Mitsuo, Umeyama Hideaki, Ramakrishnan Chandrasekaran, Thangakani A. Mary, Velmurugan D., Gromiha M. Michael, Okuno Tatsuya, Kato Koya, Minami Shintaro, Chikenji George, Suzuki Shogo D., Yanagisawa Keisuke, Shin Woong-Hee, Kihara Daisuke, Yamamoto Kazuki Z., Moriwaki Yoshitaka, Yasuo Nobuaki, Yoshino Ryunosuke, Zozulya Sergey, Borysko Petro, Stavniichuk Roman, Honma Teruki, Hirokawa Takatsugu, Akiyama Yutaka, Sekijima Masakazu

    SCIENTIFIC REPORTS   Vol. 7   2017.9

  12. Rules for connectivity of secondary structure elements in protein: two-layer αβ sandwiches Reviewed

    Shintaro Minami, George Chikenji, and Motonori Ota

    Protein Science   Vol. 26 ( 11 ) page: 2257-2267   2017.8

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    Language:English   Publishing type:Research paper (scientific journal)  

  13. Importance of consensus region of multiple-ligand templates in a virtual screening method Reviewed

    Tatsuya Okuno, Koya Kato, Shintaro Minami, Tomoki P. Terada, Masaki Sasai, George Chikenji

    Biophysics and Physicobiology   Vol. 13   page: 149-156   2016.7

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

  14. Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. Reviewed

    S Chiba, K Ikeda, T Ishida, MM Gromiha, YH Taguchi, M Iwadate, H Umeyama, KY Hsin, H Kitano, K Yamamoto, N Sugaya, K Kato, T Okuno, G Chikenji, M Mochizuki, N Yasuo, R Yoshino, K Yanagisawa, T Ban, R Teramoto, C Ramakrishnan, AM Thangakani, D Velmurugan, P Prathipati, J Ito, Y Tsuchiya, K Mizuguchi, T Honma, T Hirokawa, Y Akiyama, M Sekijima

    Scientific reports   Vol. 5   page: 17209   2015.11

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1038/srep17209

  15. VS-APPLE: A Virtual Screening Algorithm Using Promiscuous Protein-Ligand Complexes Reviewed

    Tatsuya Okuno, Koya Kato, Tomoki P Terada, Masaki Sasai, George Chikenji

    Journal of chemical information and modeling   Vol. 55 ( 6 ) page: 1108-1119   2015.6

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.jcim.5b00134

  16. How a Spatial Arrangement of Secondary Structure Elements Is Dispersed in the Universe of Protein Folds Invited Reviewed

    Shintaro Minami, Kengo Sawada, George Chikenji

      Vol. 9 ( 9 ) page: e107959   2014.9

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1371/journal.pone.0107959

  17. MICAN : a protein structure alignment algorithm that can handle Multiple-chains, Inverse alignments, Ca only models, Alternative alignments, and Non-sequential alignments Reviewed

    Shintaro Minami, Kengo Sawada, George Chikenji

    BMC Bioinformatics   Vol. 14   page: 24   2013.1

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    Language:English   Publishing type:Research paper (scientific journal)  

    Background

    Protein pairs that have the same secondary structure packing arrangement but have different topologies have attracted much attention in terms of both evolution and physical chemistry of protein structures. Further investigation of such protein relationships would give us a hint as to how proteins can change their fold in the course of evolution, as well as a insight into physico-chemical properties of secondary structure packing. For this purpose, highly accurate sequence order independent structure comparison methods are needed.
    Results

    We have developed a novel protein structure alignment algorithm, MICAN (a structure alignment algorithm that can handle Multiple-chain complexes, Inverse direction of secondary structures, Cα only models, Alternative alignments, and Non-sequential alignments). The algorithm was designed so as to identify the best structural alignment between protein pairs by disregarding the connectivity between secondary structure elements (SSE). One of the key feature of the algorithm is utilizing the multiple vector representation for each SSE, which enables us to correctly treat bent or twisted nature of long SSE. We compared MICAN with other 9 publicly available structure alignment programs, using both reference-dependent and reference-independent evaluation methods on a variety of benchmark test sets which include both sequential and non-sequential alignments. We show that MICAN outperforms the other existing methods for reproducing reference alignments of non-sequential test sets. Further, although MICAN does not specialize in sequential structure alignment, it showed the top level performance on the sequential test sets. We also show that MICAN program is the fastest non-sequential structure alignment program among all the programs we examined here.
    Conclusions

    MICAN is the fastest and the most accurate program among non-sequential alignment programs we examined here. These results suggest that MICAN is a highly effective tool for automatically detecting non-trivial structural relationships of proteins, such as circular permutations and segment-swapping, many of which have been identified manually by human experts so far. The source code of MICAN is freely download-able at http://www.tbp.cse.nagoya-u.ac.jp/MICAN.

    DOI: 10.1186/1471-2105-14-24

  18. Roles of DNA Looping in Enhancer-Blocking Activity Reviewed

    Naoko Tokuda, Masaki Sasai, George Chikenji

    Biophysical Journal   Vol. 100 ( 1 ) page: 126-134   2011.1

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    Language:English   Publishing type:Research paper (scientific journal)  

  19. *Folding energy landscape and network dynamics of small globular proteins Reviewed

    Naoto Hori, George Chikenji, R. Stephen Berry, and Shoji Takada

      Vol. 106 ( 1 ) page: 73-78   2009.1

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    The folding energy landscape of proteins has been suggested to be funnel-like with some degree of ruggedness on the slope. How complex the landscape, however, is still rather unclear. Many experiments for globular proteins suggested relative simplicity, whereas molecular simulations of shorter peptides implied more complexity. Here, by using complete conformational sampling of 2 globular proteins, protein G and src SH3 domain and 2 related random peptides, we investigated their energy landscapes, topological properties of folding networks, and folding dynamics. The projected energy surfaces of globular proteins were funneled in the vicinity of the native but also have other quite deep, accessible minima, whereas the randomized peptides have many local basins, including some leading to seriously misfolded forms. Dynamics in the denatured part of the network exhibited basin-hopping itinerancy among many conformations, whereas the protein reached relatively well-defined final stages that led to their native states. We also found that the folding network has the hierarchic nature characterized by the scale-free and the small-world properties.

  20. *In silico chaperonin-like cycle helps folding of proteins for structure prediction Reviewed

    Tadaomi Furuta, Yoshimi Fujitsuka, George Chikenji and Shoji Takada

    Biophys. J.   Vol. 94 ( 7 ) page: 2558-2565   2008.4

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    Currently, one of the most serious problems in protein-folding simulations for de novo structure prediction is conformational sampling of medium-to-large proteins. In vivo, folding of these proteins is mediated by molecular chaperones. Inspired by the functions of chaperonins, we designed a simple chaperonin-like simulation protocol within the framework of the standard fragment assembly method: in our protocol, the strength of the hydrophobic interaction is periodically modulated to help the protein escape from misfolded structures. We tested this protocol for 38 proteins and found that, using a certain defined criterion of success, our method could successfully predict the native structures of 14 targets, whereas only those of 10 targets were successfully predicted using the standard protocol. In particular, for non-alpha-helical proteins, our method yielded significantly better predictions than the standard approach. This chaperonin-inspired protocol that enhanced de novo structure prediction using folding simulations may, in turn, provide new insights into the working principles underlying the chaperonin system.

  21. *Shaping up the protein folding funnel by local interaction: lesson from a structure prediction study Reviewed

    George Chikenji, Yoshimi Fujitsuka and Shoji Takada

    Proc. Natl. Acad. Sci. USA   Vol. 103 ( 9 ) page: 3141-3146   2006.2

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    Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of "chimera proteins." In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape.

  22. *SimFold energy function for de novo protein structure prediction: Consensus with Rosetta Reviewed

    Yoshimi Fujitsuka, George Chikenji, and Shoji Takada

    Proteins: Struct. Funct. Bioinfo.   Vol. 62 ( 2 ) page: 381-398   2006.2

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    Predicting protein tertiary structures by in silico folding is still very difficult for proteins that have new folds. Here, we developed a coarse-grained energy function, SimFold, for de novo structure prediction, performed a benchmark test of prediction with fragment assembly simulations for 38 test proteins, and proposed consensus prediction with Rosetta. The SimFold energy consists of many terms that take into account solvent-induced effects on the basis of physicochemical consideration. In the benchmark test, SimFold succeeded in predicting native structures within 6.5 A for 12 of 38 proteins; this success rate was the same as that by the publicly available version of Rosetta (ab initio version 1.2) run with default parameters. We investigated which energy terms in SimFold contribute to structure prediction performance, finding that the hydrophobic interaction is the most crucial for the prediction, whereas other sequence-specific terms have weak but positive roles. In the benchmark, well-predicted proteins by SimFold and by Rosetta were not the same for 5 of 12 proteins, which led us to introduce consensus prediction. With combined decoys, we succeeded in prediction for 16 proteins, four more than SimFold or Rosetta separately. For each of 38 proteins, structural ensembles generated by SimFold and by Rosetta were qualitatively compared by mapping sampled structural space onto two dimensions. For proteins of which one of the two methods succeeded and the other failed in prediction, the former had a less scattered ensemble located around the native. For proteins of which both methods succeeded in prediction, often two ensembles were mixed up.

  23. *Protein folding mechanisms and energy landscape of src SH3 domain studied by a structure prediction toolbox Reviewed

    George Chikenji, Yoshimi Fujitsuka, Shoji Takada

    CHEMICAL PHYSICS   Vol. 307 ( 2-3 ) page: 157-162   2004.12

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    The global energy landscape of src SH3 domain is comprehensively explored and folding mechanisms are discussed by using a physico-chemical protein model and the reversible fragment assembly method. We found that the lowest energy structure found in simulations is quite similar to the native, an apparent free energy barrier exists between the denatured and native states, and the computed folding transition state ensemble is well consistent with experimental data. Interestingly, non-native alpha-helical contents are found at early stage of folding, which also seems to be consistent with a recent experiment. These results suggest that the fragment assembly method, originally developed for structure prediction, can be used for studying folding mechanisms as well.

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Presentations 18

  1. デザインしやすいタンパク質フォールドの条件探索と新規フォールドタンパク質デザイン Invited

    千見寺浄慈

    スーパーコンピュータワークショップ2023 「 シミュレーション、インフォマティクス、AIによる生体分子科学の最前線」  2024.1.15 

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    Event date: 2024.1

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Venue:自然科学研究機構 岡崎コンファレンスセンター  

  2. 物理的にデザイン可能なタンパク質フォールドの条件:立体構造データベース解析による知識抽出 Invited

    千見寺浄慈

    第19回日本蛋白質科学会年会  2019.6.26 

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    Event date: 2019.6

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

  3. β-α−βモチーフにおけるレジスタシフトの非対称性

    千見寺浄慈,南慎太朗,古賀信康

    第18回日本蛋白質科学会年会 

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    Event date: 2018.6

    Language:Japanese   Presentation type:Poster presentation  

    Venue:朱鷺メッセ   Country:Japan  

  4. What are the structural features of superfolds? a case study of beta-sheet proteins

    George Chikenji, Hayao Imakawa, and Shintaro Minami

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    Event date: 2017.9

    Language:English   Presentation type:Poster presentation  

    Country:Japan  

  5. スーパーフォールドの決定因子:平行βシートタンパク質を例にして

    千見寺 浄慈, 南 慎太朗

    第17回日本蛋白質科学会年会  

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    Event date: 2017.6

    Language:English   Presentation type:Poster presentation  

    Country:Japan  

  6. タンパク質立体構造におけるレア構造

    西山俊介、南新太朗、千見寺浄慈

    第16回日本蛋白質科学会年会 

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    Event date: 2016.6

    Language:Japanese   Presentation type:Poster presentation  

    Country:Japan  

  7. VS-APPLE: A Virtual Screening Algorithm Using Promiscuous Protein-Ligand Complexes

    Koya Kato, George Chikenji

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    Event date: 2015.6

    Language:Japanese   Presentation type:Poster presentation  

    Country:Japan  

  8. How does a protein fold share the same secondary structure packing arrangement with other folds?

    Shintaro Minami, George Chikenji

    The 14th annual meeting of the protein science society of Japan 

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    Event date: 2014.6

    Language:Japanese   Presentation type:Poster presentation  

    Country:Japan  

  9. 配列順序を無視したタンパク質の立体構造比較によって見えてきたこと

    南 慎太朗、澤田 賢吾、千見寺 浄慈

    第13回日本蛋白質科学会年会 

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    Event date: 2013.6

    Language:Japanese   Presentation type:Poster presentation  

    Country:Japan  

  10. タンパク質構造比較の専門家によるアラインメントを再現する、配列順序に依存しない 構造アラインメント法の開発

    南 慎太朗、澤田 賢吾、千見寺 浄慈

    第12回日本蛋白質科学会年会  

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    Event date: 2012.6

    Language:Japanese   Presentation type:Poster presentation  

    Country:Japan  

  11. The evolution of protein folds and De Novo protein structure prediction

    Shintaro Minami, Kota Nakamori, Kengo Sawada and George Chikenji

    The 11th Annual Meeting of the Protein Science Society of Japan 

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    Event date: 2011.6

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  12. Template based protein structure prediction by fragment assembly with SimFold in CASP9 International conference

    Shintaro Minami, Kengo Sawada and George Chikenji

    Third Korea-Japan Seminars on Biomolecular Sciences - Experiments and Simulations  

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    Event date: 2011.2 - 2011.3

    Language:English   Presentation type:Oral presentation (invited, special)  

    Country:Korea, Republic of  

  13. フラグメントアセンブリ法でない方法による新規フォールド予測の試み

    千見寺浄慈

    第10回蛋白質科学会 

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    Event date: 2010.6

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  14. Template-based modeling and free-modeling by fragment assembly with SimFold energy function International conference

    Asian Workshop for Protein Structure Prediction Methodology 

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    Event date: 2009.9

    Language:English   Presentation type:Oral presentation (invited, special)  

  15. タンパク質の立体構造予測の現状とフリーモデリング的テンプレートモデリングの試み

    千見寺浄慈

    蛋白研セミナー:実験と計算機科学で解明する蛋白質機能構造 

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    Event date: 2009.7

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  16. フラグメントアセンブリ法によるホモロジーモデリングとCASP8の結果

    第4回ACPワークショップ 「モンテカルロ・シミュレーション:最近の進展」 

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    Event date: 2008.12

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  17. タンパク質の立体構造予測のお話

    生物物理若手の会勉強会 

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    Event date: 2008.11

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  18. フラグメントバカによる Template Based Structure Prediction in CASP8

    CPS研究会 

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    Event date: 2008.9

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

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Research Project for Joint Research, Competitive Funding, etc. 1

  1. 立体構造予測からのタンパク質立体構造構築原理の探求

    2006

KAKENHI (Grants-in-Aid for Scientific Research) 5

  1. Principles of genome dynamics and DNA functions

    Grant number:22H00406  2022.4 - 2027.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

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    Authorship:Coinvestigator(s) 

  2. 物理的に設計可能な蛋白質フォールド空間の解明:理論と実験的検証

    Grant number:19H03166  2019.4 - 2022.3

    千見寺 浄慈

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    Authorship:Principal investigator 

    Grant amount:\17290000 ( Direct Cost: \13300000 、 Indirect Cost:\3990000 )

    本研究は、「タンパク質の天然構造として存在することができる条件」を明らかにする事を目指すものである。これを理解するための戦略として具体的には、例題として4本βストランドからなるフォールドを対象にし、以下の二つの課題に取り組む。
    (1) 物理的に設計可能なフォールドの条件を、データベース解析、シミュレーション、および理論を用いて明らかにする。
    (2) 本研究で提案する設計可能なフォールドの条件の妥当性を批判的に検証するために、設計可能と予測されたフォールドの中で、データベースに未だ存在しないフォールドをもつタンパク質を合理設計し、実際に合成し構造決定まで行う。
    タンパク質立体構造データベース解析を行い、平行βシートのレジスタシフトに関する経験的ルール(ネガティブレジスターシフトは禁止されているようにみえる、といったことや、βストランド間を繋ぐループの形状によって頻出するレジスタシフトが異なること、など)の発見した。
    次いで、ペプチドの全原子モデルを用いた網羅的構造探索によって、それらの経験則が進化による偶然であることを否定し、物理的な帰結であることを示す事ができた。さらに、これらのルールを用いると、全てのルールを満たす事ができるフラストレーションのないフォールドと、全てのルールを満足する事ができないフラストレーションのあるフォールドがある事がわかった。
    興味深いことに、データベース中で頻出するフォールドは全てフラストレーションのないものであり、データベース中で存在しないもの、あるいはレアなものはフラストレーションがある事がわかった。このことから、タンパク質フォールドのデザインしやすさはフラストレーションで決定されるという仮説を立てた。
    この仮説の妥当性を検証するために、4本βストランドからなる全てのフォールドの中から、データベース中に存在しないが、フラストレーションのないフォールドを全て特定し、実際に合理的デザインおよび実験的検証を行った。予備的な実験結果として、いくつかのターゲットに対しては、目標通りの構造をもつ人工タンパク質が設計できたことを支持するデータが得られた。
    令和元年度に到達目標である平行βシートタンパク質に対するデザイン可能性を判定する理論を構築することができた。また、この理論に基づき物理的に設計可能は新規βシートフォールドを予測し、そのいくつかに対して予備的な実験を行なうことができた。
    今後は、前年度の研究で明らかとなった物理的にデザイン可能なフォールドの条件に基づき、物理的にデザイン可能な新規フォールドを特定し、実際に合理的デザイン、および合成・実験し、構造決定までを試みる。
    具体的には、4本βストランドからなるβシートフォールドの理論的に可能な全てのパターン96個の中から、タンパク質立体構造データベースに存在しないものを特定する。次いで、その中から我々の理論で物理的にデザイン可能なものを特定する。それら全てに対して、実験的に構造決定するところまでを目指す。また、実験結果を理論にフィードバックし、より完成度の高い理論構築を行う。

  3. 既知フォールドの再配線による新規フォールド予測法の開発

    2016.4 - 2019.3

    科学研究費補助金  基盤研究(C)

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    Authorship:Principal investigator 

  4. フラグメントアセンブリ法の逆発想による新規フォールド予測法の開発

    2011.4 - 2014.3

    科学研究費補助金  若手研究(B)

    千見寺浄慈

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    Authorship:Principal investigator 

  5. 局所配列の物理化学に着目したタンパク質の立体構造予測の研究

    2008

    科学研究費補助金  若手研究(B),課題番号:20770120

    千見寺 浄慈

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    Authorship:Principal investigator 

 

Teaching Experience (On-campus) 15

  1. Exercises in Applied Physics 4

    2022

  2. Physical Science and Engineering Tutorial 4a

    2021

  3. Physical Science and Engineering Tutorial 3b

    2021

  4. 物理工学演習4a

    2020

  5. 応用物理学演習第4

    2018

  6. 応用物理学演習第2

    2017

  7. 応用物理学演習第4

    2017

  8. 応用物理学演習第2

    2016

  9. 応用物理学演習第4

    2016

  10. 応用物理学演習第4

    2015

  11. 応用物理学演習第2

    2015

  12. 応用物理学演習第2

    2014

  13. 応用物理学演習第4

    2014

  14. 応用物理学演習第4

    2013

  15. 応用物理学演習第1

    2013

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