研究者詳細 - 千見寺　浄慈

論文 - 千見寺　浄慈

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Protein superfolds are characterised as frustration-free topologies: A case study of pure parallel <i>β</i>-sheet topologies 査読有り

Murata, H; Toko, K; Chikenji, G

PLOS COMPUTATIONAL BIOLOGY 20 巻 ( 8 ) 2024年8月

Exploration of novel & alpha;& beta;-protein folds through de novo design 査読有り国際誌

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

NATURE STRUCTURAL & MOLECULAR BIOLOGY 30 巻 ( 8 ) 頁： 1132 - + 2023年8月

The Structural Rule Distinguishing a Superfold: A Case Study of Ferredoxin Fold and the Reverse Ferredoxin Fold 招待有り査読有り

Nishina Takumi, Nakajima Megumi, Sasai Masaki, Chikenji George

MOLECULES 27 巻 ( 11 ) 2022年6月

The register shift rules for beta alpha beta-motifs for de novo protein design 査読有り

Murata Hiroto, Imakawa Hayao, Koga Nobuyasu, Chikenji George

PLOS ONE 16 巻 ( 8 ) 頁： e0256895 2021年

MICAN-SQ: a sequential protein structure alignment program that is applicable to monomers and all types of oligomers 査読有り

Minami Shintaro, Sawada Kengo, Ota Motonori, Chikenji George

BIOINFORMATICS 34 巻 ( 19 ) 頁： 3324 - 3331 2018年10月

Exploring hydrophilic sequence space to search for uncharted foldable proteins by AlphaFold2 査読有り

Naoki Tomita, Hiroki Onoda, Leonard M. G. Chavas, George Chikenji

Biophysics and Physicobiology 22 巻 2025年2月

The cavity method to protein design problem 査読有り

Tomoei Takahashi, George Chikenji, Kei Tokita

Journal of Statistical Mechanics 2022 巻頁： 103403 2022年10月

Lattice protein design using Bayesian learning 査読有り

Takahashi Tomoei, Chikenji George, Tokita Kei

PHYSICAL REVIEW E 104 巻 ( 1 ) 2021年7月

Exploration of Novel Alpha-Beta Protein Folds by <i>De Novo</i> Design

Minami, S; Koga, R; Chikenji, G; Sugiki, T; Kobayashi, N; Koga, N

BIOPHYSICAL JOURNAL 118 巻 ( 3 ) 頁： 43A - 43A 2020年2月

A prospective compound screening contest identified broader inhibitors for Sirtuin 1 査読有り国際共著

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 9 巻 ( 1 ) 頁： 19585 2019年12月

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 9 巻 2019年12月

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

Minami Shintaro, Chikenji George, Ota Motonori

PROTEIN SCIENCE 26 巻 ( 11 ) 頁： 2257 - 2267 2017年11月

An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes 査読有り

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 7 巻頁： s41598-017-10275-4 2017年9月

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 7 巻 2017年9月

Rules for connectivity of secondary structure elements in protein: two-layer αβ sandwiches 査読有り

Shintaro Minami, George Chikenji, and Motonori Ota

Protein Science 26 巻 ( 11 ) 頁： 2257-2267 2017年8月

Importance of consensus region of multiple-ligand templates in a virtual screening method 査読有り

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

Biophysics and Physicobiology 13 巻頁： 149-156 2016年7月

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. 査読有り

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 5 巻頁： 17209 2015年11月

VS-APPLE: A Virtual Screening Algorithm Using Promiscuous Protein-Ligand Complexes 査読有り

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

Journal of chemical information and modeling 55 巻 ( 6 ) 頁： 1108-1119 2015年6月

How a Spatial Arrangement of Secondary Structure Elements Is Dispersed in the Universe of Protein Folds 招待有り査読有り

Shintaro Minami, Kengo Sawada, George Chikenji

9 巻 ( 9 ) 頁： e107959 2014年9月

MICAN : a protein structure alignment algorithm that can handle Multiple-chains, Inverse alignments, Ca only models, Alternative alignments, and Non-sequential alignments 査読有り

Shintaro Minami, Kengo Sawada, George Chikenji

BMC Bioinformatics 14 巻頁： 24 2013年1月

記述言語：英語掲載種別：研究論文（学術雑誌）

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

Roles of DNA Looping in Enhancer-Blocking Activity 査読有り

Naoko Tokuda, Masaki Sasai, George Chikenji

Biophysical Journal 100 巻 ( 1 ) 頁： 126-134 2011年1月

*Folding energy landscape and network dynamics of small globular proteins 査読有り

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

106 巻 ( 1 ) 頁： 73-78 2009年1月

記述言語：英語掲載種別：研究論文（学術雑誌）

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.

*In silico chaperonin-like cycle helps folding of proteins for structure prediction 査読有り

Tadaomi Furuta, Yoshimi Fujitsuka, George Chikenji and Shoji Takada

Biophys. J. 94 巻 ( 7 ) 頁： 2558-2565 2008年4月

記述言語：英語掲載種別：研究論文（学術雑誌）

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.

*Shaping up the protein folding funnel by local interaction: lesson from a structure prediction study 査読有り

George Chikenji, Yoshimi Fujitsuka and Shoji Takada

Proc. Natl. Acad. Sci. USA 103 巻 ( 9 ) 頁： 3141-3146 2006年2月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（学術雑誌）

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.

*SimFold energy function for de novo protein structure prediction: Consensus with Rosetta 査読有り

Yoshimi Fujitsuka, George Chikenji, and Shoji Takada

Proteins: Struct. Funct. Bioinfo. 62 巻 ( 2 ) 頁： 381-398 2006年2月

記述言語：英語掲載種別：研究論文（学術雑誌）

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.

*Protein folding mechanisms and energy landscape of src SH3 domain studied by a structure prediction toolbox 査読有り

George Chikenji, Yoshimi Fujitsuka, Shoji Takada

CHEMICAL PHYSICS 307 巻 ( 2-3 ) 頁： 157-162 2004年12月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（学術雑誌）

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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