2018年Original Article

  • 2018)
    • Kou T, Kana M, Kamada M, Nakatsui M, Matsumoto S, Okuno Y, Muto M. “A Platform for Comprehensive Genomic Profiling in Human Cancers and Pharmacogenomics Therapy Selection” Methods in molecular biology (Clifton, N.J.). 2018;1825:413-424. Available from: https://doi.org/10.1007/978-1-4939-8639-2_14
    • Araki M, Okuno Y. “Molecular Mechanism of Resistance to Kinase Inhibitors Clarified by a Binding Free Energy Computation Method and Its Improvement by Incorporating Protein Flexibility” BIOPHYSICAL JOURNAL. 2018;114(3):56A. Available from: https://doi.org/10.1016/j.bpj.2017.11.361
    • Terayama K, Iwata H, Araki M, Okuno Y, Tsuda K. “Machine learning accelerates MD-based binding pose prediction between ligands and proteins” Bioinformatics. 2018;34(5):770-778. Available from: https://doi.org/10.1093/bioinformatics/btx638
    • Iwata Y, Katayama Y, Okuno Y, Wakabayashi S. “Novel inhibitor candidates of TRPV2 prevent damage of dystrophic myocytes and ameliorate against dilated cardiomyopathy in a hamster model” Oncotarget. 2018;9(18):14042-14057. Available from: https://doi.org/10.18632/oncotarget.24449
    • Kondo T, Kanai M, Kou T, Sakuma T, Mochizuki H, Kamada M, Nakatsui M, Uza N, Kodama Y, Masui T, Takaori K, Matsumoto S, Miyake H, Okuno Y, Muto M. “Association between homologous recombination repair gene mutations and response to oxaliplatin in pancreatic cancer” Oncotarget. 2018;9(28):19817-19825. Available from: https://doi.org/10.18632/oncotarget.24865
    • Noda Y, Kuzuya A, Tanigawa K, Araki M, Kawai R, Ma B, Sasakura Y, Maesako M, Tashiro Y, Miyamoto M, Uemura K, Okuno Y, Kinoshita A. “Fibronectin type Ⅲ domain-containing protein 5 interacts with APP and decreases amyloid β production in Alzheimer’s disease” Molecular Brain. 2018;11:61. Available from: https://doi.org/10.1186/s13041-018-0401-8
    • Araki M, Iwata H, Ma B, Fujita A, Terayama K, Sagae Y, Ono F, Tsuda K, Kamiya N, Okuno Y. “Improving the accuracy of protein-ligand binding mode prediction using a molecular dynamics-based pocket generation approach” J. Comput. Chem. 2018;39(32):2679-2689. Available from: https://doi.org/10.1002/jcc.25715
    • Terayama K, Yamashita T, Oguchi T, Tsuda K. “Fine-grained optimization method for crystal structure prediction” npj Computational Materials, 2018;4(32). Available from: https://doi.org/10.1038/s41524-018-0090-y
    • Tamada Y. “Memory Efficient Parallel Algorithm for Optimal DAG Structure Search using Direct Communication” Journal of Parallel and Distributed Computing”, 2018;119:27-35. Available from: https://doi.org/10.1016/j.jpdc.2018.03.011
    • Kawai R, Araki M, Yoshimura M, Kamiya N, Ono M, Saji H, Okuno Y. “Core Binding Site of a Thioflavin-T-Derived Imaging Probe on Amyloid β Fibrils Predicted by Computational Methods” ACS Chem. Neurosci 2018;9(5):957-966. Available from: https://doi.org/10.1021/acschemneuro.7b00389
    • Nakaoku T, Kohno T, Araki M, Niho S, Chauhan R, Knowles P.P,  Tsuchihara K, Matsumoto S, Shimada Y, Mimaki S, Ishii G, Ichikawa H, Nagatoishi S, Tsumoto K, Okuno Y, Yoh K, McDonald N.Q, Goto K. “A secondary RET mutation in the activation loop conferring resistance to vandetanib” Nature Communications, 2018;9(1):625. Available from: https://doi.org/10.1038/s41467-018-02994-7