2020年Original Article

  • 2020)
    • Kawaguchi C, Shintani N, Hayata-Takano A, Hatanaka M, Kuromi A, Nakamura R, Yamano Y, Shintani Y, Nagai K, Tsuchiya S, Sugimoto Y, Ichikawa A, Okuno Y, Urade Y, Hirai H, Nagata KY, Nakamura M, Narumiya S, Nakazawa T, Kasai A, Ago Y, Takuma K, Baba A, Hashimoto H. “Lipocalin-type prostaglandin D synthase regulates light-induced phase advance of the central circadian rhythm in mice” Commun Biol. 2020;3(1):557. Available from: http://doi.org/10.1038/s42003-020-01281-w
    • Hagihara H, Ienaga N, Enomoto D, Takahata S, Ishihara H, Noda H, Tsuda K, Terayama K. “Computer Vision–Based Approach for Quantifying Occupational Therapists’ Qualitative Evaluations of Postural Control” Occupational Therapy International. 2020:8542191. Available from: http://doi.org/10.1155/2020/8542191
    • Shuntaro Chiba, Aki Tanabe, Makoto Nakakido, Yasushi Okuno, Kouhei Tsumoto, Masateru Ohta. “Structure-based design and discovery of novel anti-tissue factor antibodies with cooperative double-point mutations, using interaction analysis” Sci Rep. 2020;10:17590. Available from: http://doi.org/10.1038/s41598-020-74545-4
    • Hiroaki Iwata, Naoto Kanda, Mitsugu Araki, Yukari Sagae, Katsuyoshi Masuda, Yasushi Okuno. “Discovery of Natural TRPA1 Activators through Pharmacophore-based Virtual Screening and a Biological Assay” Bioorganic & Medicinal Chemistry Letters. 2020:127639. Available from: https://doi.org/10.1016/j.bmcl.2020.127639
    • Mitsugu Araki, Naoto Kanda, Hiroaki Iwata, Yukari Sagae, Katsuyoshi Masuda, Yasushi Okuno. “Identification of a New Class of Non-Electrophilic TRPA1 Agonists by a Structure-Based Virtual Screening Approach. Bioorganic & Medicinal Chemistry Letters. 2020;30(11):127142. Available from: https://doi.org/10.1016/j.bmcl.2020.127142
    • Gert-Jan Bekker, Mitsugu Araki, Kanji Oshima, Yasushi Okuno, Narutoshi Kamiya. “Exhaustive Search of the Configurational Space of Heat-Shock Protein 90 With Its Inhibitor by Multicanonical Molecular Dynamics Based Dynamic Docking” Journal of Computational Chemistry. 2020;41(17):1606-1615. Available from: https://doi.org/10.1002/jcc.26203
    • Ryosuke Shibukawa, Shoichi Ishida, Kazuki Yoshizoe, Kunihiro Wasa, Kiyosei Takasu, Yasushi Okuno, Kei Terayama, Koji Tsuda. “CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration” Journal of Cheminformatics. 2020;12:52. Available from: https://doi.org/10.1186/s13321-020-00452-5
    • Uchino E, Suzuki K, Sato N, Kojima R, Tamada Y, Hiragi S, Yokoi H, Yugami N, Minamiguchi S, Haga H, Yanagita M, Okuno Y. “Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach” Int. J. Med. Inform. 2020;141:104231. Available from: https://doi.org/10.1016/j.ijmedinf.2020.104231
    • Matsumoto S, Araki M, Isaka Y, Ono F, Hirohashi K, Ohashi S, Muto M, Okuno Y. “E487K-Induced Disorder in Functionally Relevant Dynamics of Mitochondrial Aldehyde Dehydrogenase 2” Biophys. J. 2020 Jul 10; Available from: https://doi.org/10.1016/j.bpj.2020.07.002
    • Araki M, Kanegawa N, Iwata H, Sagae Y, Ito K, Masuda K, Okuno Y. “Hydrophobic interactions at subsite S1′ of human dipeptidyl peptidase IV contribute significantly to the inhibitory effect of tripeptides” Heliyon. 2020;6(6):e04227. Available from: https://doi.org/10.1016/j.heliyon.2020.e04227
    • Kato K, Masuda T, Watanabe C, Miyagawa N, Mizuochi H, Nagase S, Kamisaka K, Oshima K, Ono S, Ueda H, Tokuhisa A, Kanada R, Ohta M, Ikeguchi M, Okuno Y, Fukuzawa K, Honma T. ” High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning” J. Chem. Inf. Model. 2020. Available from: https://doi.org/10.1021/acs.jcim.0c00273
    • Tokuhisa A, Kanada R, Chiba S, Terayama K, Isaka Y, Ma B, Kamiya N, Okuno Y. “Coarse-Grained Diffraction Template Matching Model to Retrieve Multiconformational Models for Biomolecule Structures from Noisy Diffraction Patterns” J. Chem. Inf. Model. 2020;60(6):2803–2818. Available from: https://doi.org/10.1021/acs.jcim.0c00131
    • Yamanaka M, Iwata H, Masuda K, Araki M, Okuno Y, Okamura M, Koiwa J, Tanaka T. “A novel orexin antagonist from a natural plant was discovered using zebrafish behavioural analysis” Eur Rev Med Pharmacol Sci. 2020;24(9):5127-5139. Available from: https://doi.org/10.26355/eurrev_202005_21207
    • Kojima R, Ishida S, Ohta M, Iwata H, Honma T, Okuno Y. “kGCN: a graph-based deep learning framework for chemical structures” J Cheminform [Internet]. 2020 May 12;12(1):32. Available from: https://doi.org/10.1186/s13321-020-00435-6
    • Masuda N, Murakami K, Kita Y, Hamada A, Kamada M, Teramoto Y, Sakatani T, Matsumoto K, Sano T, Saito R, Okuno Y, Ogawa O, Kobayashi T. “Trp53 mutation in Krt5-expressing basal cells facilitates the development of basal squamous-like invasive bladder cancer in the chemical carcinogenesis of mouse bladder” Am J Pathol. 2020 Apr 24. Available from: https://doi.org/10.1016/j.ajpath.2020.04.005
    • Kanada R, Tokuhisa A, Tsuda K, Okuno Y, Terayama K. “Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning” Biomolecules. 2020;10(3):482. Available from: https://doi.org/10.3390/biom10030482
    • Saito Y, Koya J, Araki M, Kogure Y, Shingaki S, Tabata M, McClure M, Yoshifuji K, Matsumoto S, Isaka Y, Tanaka H, Kanai T, Miyano S, Shiraishi Y, Okuno Y, Kataoka K. . “Landscape and function of multiple mutations within individual oncogenes” Nature. 2020. Available from: https://doi.org/10.1038/s41586-020-2175-2
    • Araki M, Kanda M, Iwata H, Sagae Y, Masuda K, Okuno Y. “Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach” Bioorg Med Chem Lett. 2020;30(11):127142. Available from: https://doi.org/10.1016/j.bmcl.2020.127142
    • Sato N, Kakuta M, Uchino E, Hasegawa T, Kojima R, Kobayashi W, Sawada K, Tamura Y, Tokuda I, Imoto S, Nakaji S, Murashita K, Yanagita M,Okuno Y. “The relationship between cigarette smoking and the tongue microbiome in an East Asian population” J. Oral Microbiol [Internet]. 2020;12(1):1–9. Available from: https://doi.org/10.1080/20002297.2020.1742527
    • Sato N, Kakuta M, Hasegawa T, Yamaguchi R, Uchino E, Kobayashi W, Sawada K, Tamura Y, Tokuda I, Murashita K, Nakaji S, Imoto S, Yanagita M, Okuno Y. “Metagenomic analysis of bacterial species in tongue microbiome of current and never smokers” npj Biofilms Microbiomes [Internet]. 2020;6(1):1–9. Available from: http://dx.doi.org/10.1038/s41522-020-0121-6
    • Ono F, Chiba S, Isaka Y, Matsumoto S, Ma B, Katayama R, Araki M, Okuno Y. “Improvement in predicting drug sensitivity changes associated with protein mutations using a molecular dynamics based alchemical mutation method” Sci Rep [Internet]. 2020;10(1):2161. Available from: https://doi.org/10.1038/s41598-020-58877-9
    • Tanaka Y, Tamada Y, Ikeguchi M, Yamashita F, Okuno Y. “System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork”  Biomolecules. 2020;10(2):306. Available from:https://doi.org/10.3390/biom10020306
    • Hatae R, Chamoto K, Kim YH, Sonomura K, Taneishi K, Kawaguchi S, Yoshida H, Ozasa H, Sakamori Y, Akrami M, Fagarasan S, Masuda I, Okuno Y, Matsuda F, Hirai T, Honjo T. “Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy” JCI Insight. 2020;5(2):e133501. Aveilable from: https://doi.org/10.1172/jci.insight.133501
    • Kawai R, Chiba S, Okuwaki K, Kanada R, Doi H, Ono M, Mochizuki Y, Okuno Y. “Stabilization Mechanism for a Nonfibrillar Amyloid β Oligomer Based on Formation of a Hydrophobic Core Determined by Dissipative Particle Dynamics” ACS Chem Neurosci. 2020;11:385–394. Available from: https://doi.org/10.1021/acschemneuro.9b00602