Mizuguchi Laboratory
National Institutes of Biomedical Innovation, Health and Nutrition
Institute for Protein Research

Preprint

  1. Shinobu A., Re S., Sugita Y. (2022). The impact of inhibitor size and flexibility on the binding pathways to c-Src kinase bioRxiv, https://www.biorxiv.org/content/10.1101/2022.10.25.513784v1
  2. Natsume-Kitatani Y, Itoh M., Takeda Y., Kuroda M., Hirata H., Miyake K., Shiroyama T., Shirai Y., Noda Y., Adachi Y., Enomoto T., Amiya S., Adachi J., Narumi R., Muraoka S., Tomonaga T., Kurohashi S., Cheng F., Tanaka R, Yada S, Aramaki E., Wakiyama S., Chen Y-A., Higuchi C., Nojima Y., Fujiwara T., Nagao C., Matsumura Y., Mizuguchi K., Kumanogoh A., Ueda N. (2022). Data-driven patient stratification and drug target discovery by using medical information and serum proteome data of idiopathic pulmonary fibrosis patients Research Square. -, -. https://www.researchsquare.com/article/rs-405195/v3

2023

  1. Nojima Y., Aoki M., Re S., Hirano H., Abe Y., Narumi R., Muraoka S., Shoji H., Honda K., Tomonaga T., Mizuguchi K., Boku N., Adachi J. (2023). Integration of pharmacoproteomic and computational approaches reveals the cellular signal transduction pathways affected by apatinib in gastric cancer cell lines Computational and Structural Biotechnology Journal, 21. doi
  2. Maruyama S., Matsuoka T., Hosomi K., Park J., Nishimura M., Murakami H., Konishi K., Miyachi M., Kawashima H., Mizuguchi K., Kobayashi T., Ooka T., Yamagata Z., Kunisawa J. (2023). Characteristic Gut Bacteria in High Barley Consuming Japanese Individuals without Hypertension microorganisms, 11(5):1246. doi
  3. Martin , Watanabe R., Hashimoto K., Higashisaka K., Haga Y., Tsutsumi Y., Mizuguchi K. (2023). Evidence-Based Prediction of Cellular Toxicity for Amorphous Silica Nanoparticles ACS Nano, . doi
  4. Alarabi A., Mohsen A., Taleb Z., Mizuguchi K., Alshbool F., Khasawneh F. (2023). Predicting thrombotic cardiovascular outcomes induced by waterpipe-associated chemicals using comparative toxicogenomic database: Genes, phenotypes, and pathways Life Sciences, 323. doi
  5. Nagano N., Tokunaga N., Ikeda M., Inoura H., Khoa D., Miwa M., Sohrab M., Topić G., Nogami-Itoh M., Takamura H. (2023). A novel corpus of molecular to higher-order events that facilitates the understanding of the pathogenic mechanisms of idiopathic pulmonary fibrosis Scientific Reports, 13(1):5986. https://www.nature.com/articles/s41598-023-32915-8

2022

  1. Hosoe Y., Miyanoiri Y., Re S., Ochi S., Asahina Y., Kawakami T., Kuroda M., Mizuguchi K., Oda M. (2022). Structural dynamics of the N-terminal SH2 domain of PI3K in its free and CD28-bound states The FEBS Journal, 290 (9): 2366-2378. doi
  2. Futami Y., Takeda Y., Koba T., Narumi R., Nojima Y., Ito M., Nakayama M., Ishida M., Yoshimura H., Naito Y., Fukushima K., Takimoto T., Edahiro R., Matsuki T., Nojima S., Hirata H., Koyama S., Iwahori K., Nagatomo I., Shirai Y., Suga Y., Satoh S., Futami S., Miyake K., Shiroyama T., Inoue Y., Adachi J., Tomonaga T., Ueda K., Kumanogoh A. (2022). Identification of CD14 and lipopolysaccharide-binding protein as novel biomarkers for sarcoidosis using proteomics of serum extracellular vesicles International Immunology, 34(6):327-340. doi
  3. Sohrab M., Duong K., Masami I., Topić G., Natsume-Kitatani Y., Kuroda M., Itoh M., Takamura H. (2022). BiomedCurator: Data Curation for Biomedical Literature Association for Computational Linguistics, Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations:63-71. https://aclanthology.org/2022.aacl-demo.8
  4. 長尾知生子 , 鎌田真由美 , 中津井雅彦 , 深川明子 , 片山俊明 , 川島秀一 , 水口 賢司 , 安倍理加 (2022). 医薬品関連文書の利活用に向けたインタビューフォームの構造化の提案 医薬品情報学, 24(4).
  5. Murakami Y., Mizuguchi K. (2022). Recent developments of sequence-based prediction of protein–protein interactions. Biophysical Reviews., 14(6):1393-1411. doi
  6. Watanabe R., Kawata T., Ueda S., Shinbo T., Higashimori M., Natsume-Kitatani Y., Mizuguchi K. (2022). Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information. Molecular Pharmaceutics., -doi
  7. Nagata Y., Watanabe R., Eichhorn C., Ohno S., Aiba T., Ishikawa T., Nakano Y., Aizawa Y., Hayashi K., Murakoshi N., Nakajima T., Yagihara N., Mishima H., Sudo T., Higuchi C., Takahashi A., Sekine A., Makiyama T., Tanaka Y., Watanabe A., Tachibana M., Morita H., Yoshiura K., Tsunoda T., Watanabe H., Kurabayashi M., Nogami A., Kihara Y., Horie M., Shimizu W., Makita N., Tanaka T. (2022). Targeted deep sequencing analyses of long QT syndrome in a Japanese population. PLOS ONE., 17-12:e0277242 . doi
  8. Iiyama M., Hantani Y., Wink R., Kuroda M., Oda M. (2022). Role of Cys residues of C-terminal SH2 domain of phosphoinositide 3-kinase in its conformational stability and CD28-binding ability. Chemical Thermodynamics and Thermal Analysis., 8:100080 . doi
  9. Kawasaki T., Takeda Y., Edahiro R., Shirai Y., Nogami-Itoh M., Matsuki T., Kida H., Enomoto T., Hara R., Noda Y., Adachi Y., Niitsu T., Amiya S., Yamaguchi Y., Murakami T., Kato Y., Morita T., Yoshimura H., Yamamoto M., Nakatsubo D., Miyake K., Shiroyama T., Hirata H., Adachi J., Okada Y., Kumanogoh A. (2022). Next-generation proteomics of serum extracellular vesicles combined with single-cell RNA sequencing identifies MACROH2A1 associated with refractory COVID-19. Inflammation and Regeneration., 42-1:53 . doi
  10. Kuroda M., Watanabe R., Esaki T., Kawashima H., Ohashi R., Sato T., Honma T., Komura H., Mizuguchi K. (2022). Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters. Drug Discovery Today., 27-11:103339 . doi
  11. Sawane K., Hosomi K., Park J., Ookoshi K., Nanri H., Nakagata T., Chen Y., Mohsen A., Kawashima H., Mizuguchi K., Miyachi M., Kunisawa J. (2022). Identification of Human Gut Microbiome Associated with Enterolignan Production Microorganisms. 10-11:2169, -doi
  12. Otaki M., Hirane N., Natsume-Kitatani Y., Nogami Itoh M., Shindo M., Kurebayashi Y., Nishimura S. (2022). Mouse tissue glycome atlas 2022 highlights inter-organ variation in major N-glycan profiles. Scientific Reports., 12-1:17804 . doi
  13. Hosomi K., Saito M., Park J., Murakami H., Shibata N., Ando M., Nagatake T., Konishi K., Ohno H., Tanisawa K., Mohsen A., Chen Y., Kawashima H., Natsume-Kitatani Y., Oka Y., Shimizu H., Furuta M., Tojima Y., Sawane K., Saika A., Kondo S., Yonejima Y., Takeyama H., Matsutani A., Mizuguchi K., Miyachi M., Kunisawa J. (2022). Oral administration of Blautia wexlerae ameliorates obesity and type 2 diabetes via metabolic remodeling of the gut microbiota. Nature Communications., 13:4477. doi
  14. Chen,Y.-A, Allendes Osorio,R.S., Mizuguchi K. (2022). TargetMine 2022: A new vision into drug target analysis. Bioinformatics., -doi
  15. Gupta S., Vundavilli H., Allendes Osorio,R.S., Itoh M., Mohsen A., Datta A., Mizuguchi K., Tripathi L. (2022). Integrative Network Modeling Highlights the Crucial Roles of Rho-GDI Signaling Pathway in the Progression of Non-Small Cell Lung Cancer.IEEE Journal of Biomedical and Health Informatics.1-8 doi
  16. Mohsen A., Chen,Y.-A., Allendes Osorio,R.S., Higuchi C., Mizuguchi K. (2022). Snaq: A Dynamic Snakemake Pipeline for Microbiome data analysis with QIIME2. Frontiers in Bioinformatics., -doi
  17. Hioki K., Hayashi T., Natsume-Kitatani Y., Kouji K., Burcu T., Hideo N., Hitomi K., Hiroyuki F., Etsushi K., Cevayir C., Nobuo K., Ken J. I. (2022). Machine Learning-Assisted Screening of Herbal Medicine Extracts as Vaccine Adjuvants. Frontiers in Immunology , -doi
  18. Park J., Hosomi K., Kawashima H., Chen Y., Mohsen A., Ohno H., Konishi K., Tanisawa K., Kifushi M., Kogawa M., Takeyama H., Murakami H., Kubota T., Miyachi M., Kunisawa J., Mizuguchi K. (2022). Dietary Vitamin B1 Intake Influences Gut Microbial Community and the Consequent Production of Short-Chain Fatty Acids. Nutrients., 14(10):2078. doi
  19. Ikubo Y., Sanada T., Hosomi K., Park J., Naito A., Shoji H., Misawa T., Suda R., Sekine A., Sugiura T., Shigeta A., Nanri H., Sakao S., Tanabe N., Mizuguchi K., Kunisawa J., Suzuki T., Tatsumi K. (2022). Altered gut microbiota and its association with inflammation in patients with chronic thromboembolic pulmonary hypertension: a single-center observational study in Japan. BMC Pulmonary Medicine., 22(1):138. doi
  20. Shinobu A., Re S., Sugita Y. (2022). Practical Protocols for efficient sampling of kinase-inhibitor binding pathways using two-dimensional replica-exchange molecular dynamics. Frontiers in Molecular Biosciences., -doi
  21. Alarabi A., Mohsen A., Mizuguchi K., Alshbool F., Khasawneh F. (2022). Co-expression analysis to identify key modules and hub genes associated with COVID19 in Platelets. BMC Medical Genomics., 15(83). doi
  22. Dokainish H., Re S., Mori T., Kobayashi C., Jung J., Sugita Y. (2022). The inherent flexibility of receptor binding domains in SARS-CoV-2 spike protein. eLife., 11:e75720. doi
  23. Yamane D., Onitsuka S., Re S., Isogai H., Hamada R., Hiramoto T., Kawanishi E., Mizuguchi K., Shindo N., Ojida A. (2022). Selective covalent targeting of SARS-CoV-2 main protease by enantiopure chlorofluoroacetamide. Chemical Science., 13(10):3027-3034. doi
  24. Maruyama S., Matsuoka T., Hosomi K., Park J., Nishimura M., Murakami H., Konishi K., Miyachi M., Kawashima H., Mizuguchi K., Kobayashi T., Ooka T., Yamagata Z., Kunisawa J. (2022). Classification of the Occurrence of Dyslipidemia Based on Gut Bacteria Related to Barley Intake. Frontiers in Nutrition., 9. doi
  25. Hirano H., Abe Y., Nojima Y., Aoki M., Shoji H., Isoyama J., Honda K., Boku N., Mizuguchi K., Tomonaga T., Adachi J. (2022). Temporal dynamics from phosphoproteomics using endoscopic biopsy specimens provides new therapeutic targets in stage IV gastric cancer. Scientific Reports., 12(1):4419. doi
  26. Otoshi T., Nagano T., Park J., Hosomi K., Yamashita T., Tachihara M., Tabata T., Sekiya R., Tanaka Y., Kobayashi K., Mizuguchi K., Itoh T., Maniwa Y., Kunisawa J., Nishimura Y. (2022). The Gut Microbiome as a Biomarker of Cancer Progression Among Female Never-smokers With Lung Adenocarcinoma. Anticancer Research., 42(3):1589-1598. doi
  27. Tsuji T., Hashiguchi K., Yoshida M., Ikeda T., Koga Y., Honda Y., Tanaka T., Re S., Mizuguchi K., Takahashi D., Yazaki R., Ohshima T. (2022). α-Amino acid and peptide synthesis using catalytic cross-dehydrogenative coupling. Nature Synthesis., -doi
  28. Matsuoka T., Hosomi K., Park J., Goto Y., Nishimura M., Maruyama S., Murakami H., Konishi K., Miyachi M., Kawashima H., Mizuguchi K., Kobayashi T., Yokomichi H., Kunisawa J., Yamagata Z. (2022). Relationships between barley consumption and gut microbiome characteristics in a healthy Japanese population: a cross-sectional study. BMC Nutrition., 8(1):23 . doi
  29. Arakawa M., Tabata K., Ishida K., Kobayashi M., Arai A., Ishikawa T., Suzuki R., Takeuchi H., Tripathi L., Mizuguchi K., Morita E. (2022). Flavivirus recruits the valosin-containing protein–NPL4 complex to induce stress granule disassembly for efficient viral genome replication. Journal of Biological Chemistry., 3(298):101597 . doi
  30. Yagi K., Re S., Mori T., Sugita Y. (2022). Weight average approaches for predicting dynamical properties of biomolecules. Current Opinion in Structural Biology., 72:88-94. doi
  31. Miki Y., Taketomi Y., Kidoguchi Y., Yamamoto K., Muramatsu K., Nishito Y., Park J., Hosomi K., Mizuguchi K., Kunisawa J., Soga T., Boilard E., Gowda S., Ikeda K., Arita M., Murakami M. (2022). Group IIA secreted phospholipase A2 controls skin carcinogenesis and psoriasis by shaping the gut microbiota. JCI Insight., 2(7) . doi
  32. Takegawa-Araki T., Kumagai S., Yasukawa K., Kuroda M., Sasaki T., Obika S. (2022). Structure–Activity Relationships of Anti-microRNA Oligonucleotides Containing Cationic Guanidine-Modified Nucleic Acids. Journal of Medicinal Chemistry., -doi

2021

  1. 尾嶋拓.,李秀栄.,新津藍., 杉田有治 (2021). 分子動力学ソフトウェアGENESISを用いたタンパク質―リガンド結合の自由エネルギー計算, 日本シミュレーション学会学会誌「シミュレーション」. , 40(1),22-28.
  2. 夏目やよい (2021). 機械学習によって加速される次世代アジュバント開発, 医学のあゆみ. , 279 .
  3. 陳怡安.,李秀栄.,水口賢司 (2021). TargetMineによる生物学的知識の発見, 医学のあゆみ. , 278,641-645 .
  4. Sakib S., Fouda M. M., Al-Mahdawi M., Mohsen A., Oogane M., Ando Y., Fadlullah Md. Z., (2021). Deep Learning Models for Magnetic Cardiography Edge Sensors Implementing Noise Processing and Diagnostics IEEE Access. , 10,2656-2668 . https://ieeexplore.ieee.org/document/9663379
  5. Kageyama S., Inoue R., Hosomi K., Park J., Yumioka H., Suka T., Kurohashi Y., Teramoto K., Syauki A., Doi M., Sakaue H., Mizuguchi K., Kunisawa J., Irie Y. (2021). Effects of Malted Rice Amazake on Constipation Symptoms and Gut Microbiota in Children and Adults with Severe Motor and Intellectual Disabilities: A Pilot Study, Nutrients. , 13(12),4466 . https://www.mdpi.com/2072-6643/13/12/4466/htm
  6. Mohsen A., Lokesh P. Tripathi., Mizuguchi K. (2021). Deep Learning Prediction of Adverse Drug Reactions In Drug Discovery Using Open TG–GATEs and FAERS Databases. Frontiers in Drug Discovery. , -https://www.frontiersin.org/articles/10.3389/fddsv.2021.768792/abstract?fbclid=…
  7. Ueta M., Hosomi K., Park J., Mizuguchi K., Sotozono C., Kinoshita S., Kunisawa J. (2021). Categorization of the Ocular Microbiome in Japanese Stevens-Johnson Syndrome Patients With Severe Ocular Complications. Frontiers in Cellular and Infection Microbiology. , 11,1130 . https://www.frontiersin.org/article/10.3389/fcimb.2021.741654
  8. Tomizawa R., Park J., Hosomi K., Matsumoto N., Kawashima H., Mizuguchi K., Kunisawa J., Honda C. (2021). Relationship between Human Gut Microbiota and Nutrition Intake in Hypertensive Discordant Monozygotic Twins. Journal of Hypertension. , 10(8) . https://www.hilarispublisher.com/open-access/relationship-between-human-gut-mic…
  9. Fujiyama K., Kato N., Re S., Kinugasa K., Watanabe K., Takita R., Nogawa T., Hino T., Osada H., Sugita Y., Takahashi S., Nagano S. (2021). Molecular Basis for Two Stereoselective Diels–Alderases that Produce Decalin Skeletons. Angewandte Chemie International Edition. , 60,2-12 . doi
  10. Kasahara K., Re S., Nawrocki G., Oshima H., Mishima-Tsumagari C., Miyata-Yabuki Y., Kukimoto-Niino M., Yu I., Shirouzu M., Feig M., Sugita Y. (2021). Reduced efficacy of a Src kinase inhibitor in crowded protein solution. Nature Communications. , 12(1),4099 . doi
  11. Park J., Kato K., Murakami H., Hosomi K., Tanisawa K., Nakagata T., Ohno H., Konishi K., Kawashima H., Chen Y., Mohsen A., Xiao J., Odamaki T., Kunisawa J., Mizuguchi K., Miyachi M. (2021). Comprehensive analysis of gut microbiota of a healthy population and covariates affecting microbial variation in two large Japanese cohorts. BMC Microbiology. , 21(1),151 . doi
  12. Lee J., Mohsen A., Banerjee A., McCullough L., Mizuguchi K., Shimaoka M., Kiyono H., Park E. (2021). Distinct Age-Specific miRegulome Profiling of Isolated Small and Large Intestinal Epithelial Cells in Mice. International Journal of Molecular Sciences. , 22(7),3544 . doi
  13. Koba T., Takeda Y., Narumi R.,Shiromizu T., Nojima Y., Ito M., Kuroyama M., Futami Y. , Takimoto T., Matsuki T., Edahiro R., Nojima S., Hayama Y., Fukushima K., Hirata H., Koyama S., Iwahori K., Nagatomo I., Suzuki M., Shirai Y., Murakami T., Nakanishi K., Nakatani T., Suga Y., Miyake K., Shiroyama T , Kida H., Sasaki T., Ueda K., Mizuguchi K., Adachi J., Tomonaga T., Kumanogoh A. (2021). Proteomics of serum extracellular vesicles identifies a novel COPD biomarker, fibulin-3 from elastic fibres. ERJ open research. , -doi
  14. Matsumoto N., Park J., Tomizawa R., Kawashima H., Hosomi K., Mizuguchi K., Honda C., Ozaki R., Iwatani Y., Watanabe M., Kunisawa J. (2021). Relationship between Nutrient Intake and Human Gut Microbiota in Monozygotic Twins. Medicina, -doi
  15. Vundavilli H., Tripathi L., Datta A., Mizuguchi K. (2021). Network Modeling and Inference of Peroxisome Proliferator-Activated Receptor Pathway in High fat diet-linked Obesity. Journal of Theoretical Biology . , -doi
  16. Watanabe R., Esaki T., Ohashi R., Kuroda M., Kawashima H., Komura H., Natsume-Kitatani Y., Mizuguchi K. (2021). Development of an In Silico Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration. Journal of Medicinal Chemistry., -doi
  17. Re S., Mizuguchi K. (2021). Glycan Cluster Shielding and Antibody Epitopes on Lassa Virus Envelop Protein The Journal of Physical Chemistry B. , 125(8) . doi
  18. Chyży P., Kulik M., Re S., Sugita Y., Trylska J. (2021). Mutations of N1 Riboswitch Affect its Dynamics and Recognition by Neomycin Through Conformational Selection. Frontiers in Molecular Biosciences. , -doi
  19. Komura H., Watanabe R., Kawashima H., Ohashi R., Kuroda M., Sato T., Honma T., Mizuguchi K. (2021). A public–private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties. Drug Discovery Today., -doi
  20. Mori T., Jung J., Kobayashi C., Dokainish H., Re S., Sugita Y. (2021). Elucidation of interactions regulating conformational stability and dynamics of SARS-CoV-2 S-protein. Biophysical Journal., -. doi

2020

  1. Iiyama M., Numoto N., Ogawa S., Kuroda M., Morii H., Abe R., Ito N., Oda M. (2020). Molecular interactions of the CTLA-4 cytoplasmic region with the phosphoinositide 3-kinase SH2 domains. Molecular Immunology, 131. doi
  2. Vos, R. A., Katayama, T., Mishima, H., Kawano, S., Kawashima, S., Kim, J.-D., Moriya, Y., Tokimatsu, T., Yamaguchi, A., Yamamoto, Y., Wu, H., Amstutz, P., Antezana, E., Aoki, N. P., Arakawa, K., Bolleman, J. T., Bolton, E., Bonnal, R. J. P., Bono, H., … Takagi, T. (2020). BioHackathon 2015: Semantics of data for life sciences and reproducible research. F1000Research, 9, 136. doi
  3. Tripathi, L. P., Itoh, M. N., Takeda, Y., Tsujino, K., Kondo, Y., Kumanogoh, A., & Mizuguchi, K. (2020). Integrative Analysis Reveals Common and Unique Roles of Tetraspanins in Fibrosis and Emphysema. Frontiers in Genetics, 11. doi
  4. Tokunaga, M., Miyamoto, Y., Suzuki, T., Otani, M., Inuki, S., Esaki, T., Nagao, C., Mizuguchi, K., Ohno, H., Yoneda, Y., Okamoto, T., Oka, M., & Matsuura, Y. (2020). Novel anti-flavivirus drugs targeting the nucleolar distribution of core protein. Virology, 541, 41–51. doi
  5. Takayuki Jujo Sanada, Koji Hosomi, Hiroki Shoji, Jonguk Park, Akira Naito, Yumiko Ikubo, Asako Yanagisawa, Takayuki Kobayashi, Hideki Miwa, Rika Suda, Seiichiro Sakao, Kenji Mizuguchi, Jun Kunisawa, Nobuhiro Tanabe, & Koichiro Tatsumi. (2020). Gut microbiota modification suppresses the development of pulmonary arterial hypertension in a Su/Hx rat model. Pulmonary Circulation, 10(4). doi
  6. Tabata, T., Yamashita, T., Hosomi, K., Park, J., Hayashi, T., Yoshida, N., Saito, Y., Fukuzawa, K., Konishi, K., Murakami, H., Kawashima, H., Mizuguchi, K., Miyachi, M., Kunisawa, J., & Hirata, K. (2020). Gut microbial composition in patients with atrial fibrillation: Effects of diet and drugs. Heart and Vessels. , -. doi
  7. Nojima, Y., Takeda, Y., Maeda, Y., Bamba, T., Fukusaki, E., Itoh, M. N., Mizuguchi, K., & Kumanogoh, A. (2020). Metabolomic analysis of fibrotic mice combined with public RNA-Seq human lung data reveal potential diagnostic biomarker candidates for lung fibrosis. FEBS Open Bio, 10(11), 2427–2436. doi
  8. Saito A., Tsuchiya D., Satoh S., Okamoto A., Murakami Y., Mizuguchi K., Toh H., & Nemoto W. (n.d.). (2020). Update of the GRIP web service. JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION, 40(4), 348-356. doi
  9. Mohsen, A., Tripathi, L. P., & Mizuguchi, K. (2020). Deep Learning Prediction of Adverse Drug Reactions Using Open TG-GATEs and FAERS Databases. ArXiv:, 2010.05411 [q-Bio]. arxiv
  10. Mohsen, A., & Alarabi, A. (2020). The impact of community containment implementation timing on the spread of COVID-19: A simulation study. F1000Research, 9, 452. doi
  11. Esaki, T., Kumazawa, K., Takahashi, K., Watanabe, R., Masuda, T., Watanabe, H., Shimizu, Y., Okada, A., Takimoto, S., Shimada, T., & Ikeda, K. (2020). Open Innovation Platform using Cloud-based Applications and Collaborative Space: A Case Study of Solubility Prediction Model Development. Chem-Bio Informatics Journal, 20, 5–18. doi
  12. Esaki, T., Horinouchi, T., Natsume-Kitatani, Y., Nojima, Y., Sakane, I., & Matsui, H. (2020). Estimation of relationships between chemical substructures and antibiotic resistance-related gene expression in bacteria: Adapting a canonical correlation analysis for small sample data of gathered features using consensus clustering. Chem-Bio Informatics Journal, 20, 58–61. doi
  13. Chen, Y.-A., Park, J., Natsume-Kitatani, Y., Kawashima, H., Mohsen, A., Hosomi, K., Tanisawa, K., Ohno, H., Konishi, K., Murakami, H., Miyachi, M., Kunisawa, J., & Mizuguchi, K. (2020). MANTA, an integrative database and analysis platform that relates microbiome and phenotypic data. PLOS ONE, 15(12), e0243609. doi
  14. Allendes Osorio, R. S., Nyström-Persson, J. T., Nojima, Y., Kosugi, Y., Mizuguchi, K., & Natsume-Kitatani, Y. (2020). Panomicon: A web-based environment for interactive, visual analysis of multi-omics data. Heliyon, 6(8), e04618. doi
  15. Afanaseva A., Nagao C., Mizuguchi K. (2020). Developing a kinase-specific target selection method using a structure-based deep learning approach. JCUP IX, OpenEye Scientific Software Meeting., -.
  16. Teranishi Y, Kuwahara H, Ueda M, Takemura T, Kusumoto M, Nakamura K, Sakai J, Kimura T, Furutani Y, Kawashima M, Imokawa G, Nogami-Itoh M. (2020). Sphingomyelin Deacylase, the Enzyme Involved in the Pathogenesis of Atopic Dermatitis, Is Identical to the β-Subunit of Acid Ceramidase. Int J Mol Sci., 21(22)8789. doi
  17. Takayuki Jujo Sanada, Koji Hosomi, Hiroki Shoji, Jonguk Park, Akira Naito, Yumiko Ikubo, Asako Yanagisawa, Takayuki Kobayashi, Hideki Miwa, Rika Suda, Seiichiro Sakao, Kenji Mizuguchi, Jun Kunisawa, Nobuhiro Tanabe, Koichiro Tatsumi (2020). Gut microbiota modification suppresses the development of pulmonary arterial hypertension in a Su/Hx rat model. Pulmonary Circulation. , -doi

2019

  1. 水口賢司. (2019). 創薬の初期研究におけるデータベース構築とモデリング. 学術会議叢書25 IT・ビッグデータと薬学, 25–31.
  2. 野島陽水, & 水口賢司. (2019). 人工知能による創薬ターゲットの同定－“新薬創出を加速する人工知能の開発”プロジェクトの現状と課題. 医学のあゆみ, 268, 988–991.
  3. 夏目やよい, 相崎健一, 北嶋聡, GOSH Samik, 北野宏明, 水口賢司, & 菅野純. (2019). Garudaプラットフォームによる多角的毒性予測. Journal of Toxicological Sciences, 44 (Supplement), S132.
  4. 藤原大, & 水口賢司. (2019). コンピュータサイエンスの応用 異なるデータベースの有機的統合と医療への応用. Lung Perspectives, 27, 205-208.
  5. Chen, Y.-A., Tripathi, L. P., Fujiwara, T., Kameyama, T., Itoh, M. N., & Mizuguchi, K. (2019). The TargetMine Data Warehouse: Enhancement and Updates. Frontiers in Genetics, 10, 934. doi
  6. 江崎剛史, 渡邉怜子, 夏目やよい, 伊藤眞里, 長尾知生子, 川島和, & 水口賢司. (2019). AI活用による薬物動態予測システムの開発. 人と共生するAI革命～活用事例からみる生活･産業･社会の未来展望～, エヌ・ティー・エス,237–242.
  7. Yamada, T., Habara, O., Yoshii, Y., Matsushita, R., Kubo, H., Nojima, Y., & Nishimura, T. (2019). The role of glycogen in development and adult fitness in Drosophila. Development, 146(8), dev176149. doi
  8. Tokunaga, M., Miyamoto, Y., Suzuki, T., Otani, M., Inuki, S., Esaki, T., Nagao, C., Mizuguchi, K., Ohno, H., Yoneda, Y., Okamoto, T., Oka, M., & Matsuura, Y. (2020). Novel anti-flavivirus drugs targeting the nucleolar distribution of core protein. Virology, 541, 41–51. doi
  9. Afanasyeva, A., Nagao, C., & Mizuguchi, K. (2019). Prediction of the secondary structure of short DNA aptamers. Biophysics and Physicobiology, 16, 287–294. doi
  10. Kataoka, Y., Fujita, H., Afanaseva, A., Nagao, C., Mizuguchi, K., Kasahara, Y., Obika, S., & Kuwahara, M. (2019). High-Contrast Facile Imaging with Target-Directing Fluorescent Molecular Rotors, the N3-Modified Thioflavin T Derivatives. Biochemistry, 58(6), 493–498. doi
  11. Mohsen, A., Park, J., Chen, Y.-A., Kawashima, H., & Mizuguchi, K. (2019). Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks. BMC Bioinformatics, 20(1), 581. doi . doi
  12. Watanabe, R., Ohashi, R., Esaki, T., Kawashima, H., Natsume-Kitatani, Y., Nagao, C., & Mizuguchi, K. (2019). Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor. Scientific Reports, 9(1), 18782. doi
  13. Ohashi, R., Watanabe, R., Esaki, T., Taniguchi, T., Torimoto-Katori, N., Watanabe, T., Ogasawara, Y., Takahashi, T., Tsukimoto, M., & Mizuguchi, K. (2019). Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein. Molecular Pharmaceutics, 16(5), 1851–1863. doi
  14. Esaki, T., Ohashi, R., Watanabe, R., Natsume-Kitatani, Y., Kawashima, H., Nagao, C., Komura, H., & Mizuguchi, K. (2019). Constructing an In Silico Three-Class Predictor of Human Intestinal Absorption With Caco-2 Permeability and Dried-DMSO Solubility. Journal of Pharmaceutical Sciences, 108(11), 3630–3639. doi
  15. Chen, Y.-A., Tripathi, L. P., & Mizuguchi, K. (2019). Data Warehousing with TargetMine for Omics Data Analysis. Methods in Molecular Biology (Clifton, N.J.), 1986, 35–64. doi
  16. Esaki, T., Ohashi, R., Watanabe, R., Natsume-Kitatani, Y., Kawashima, H., Nagao, C., & Mizuguchi, K. (2019). Computational Model To Predict the Fraction of Unbound Drug in the Brain. Journal of Chemical Information and Modeling, 59(7), 3251–3261. doi
  17. Allendes Osorio, R. S., Tripathi, L. P., & Mizuguchi, K. (2019). CLINE: a web-tool for the comparison of biological dendrogram structures. BMC Bioinformatics, 20(1), 528. doi
  18. Lee, M. S. J., Natsume‐Kitatani, Y., Temizoz, B., Fujita, Y., Konishi, A., Matsuda, K., Igari, Y., Tsukui, T., Kobiyama, K., Kuroda, E., Onishi, M., Marichal, T., Ise, W., Inoue, T., Kurosaki, T., Mizuguchi, K., Akira, S., Ishii, K. J., & Coban, C. (2019). B cell-intrinsic MyD88 signaling controls IFN-γ-mediated early IgG2c class switching in mice in response to a particulate adjuvant. European Journal of Immunology, 49(9), 1433-1440. doi
  19. Takahashi, Y., Park, J., Hosomi, K., Yamada, T., Kobayashi, A., Yamaguchi, Y., Iketani, S., Kunisawa, J., Mizuguchi, K., Nobuko, M., & Ohshima, T. (2019). Analysis of oral microbiota in Japanese oral cancer patients using 16S rRNA sequencing. Journal of Oral Biosciences, 61(2), 120-128. doi
  20. Chen, Y.-A., Yogo, E., Kurihara, N., Ohno, T., Higuchi, C., Rokushima, M., & Mizuguchi, K. (2019). Assessing drug target suitability using TargetMine. F1000Research, 8, 233. doi
  21. Miyake, K., Sakane, A., Tsuchiya, Y., Sagawa, I., Tomida, Y., Kasahara, J., Imoto, I., Watanabe, S., Higo, D., Mizuguchi, K., & Sasaki, T. (2019). Actin Cytoskeletal Reorganization Function of JRAB/MICAL-L2 Is Fine-tuned by Intramolecular Interaction between First LIM Zinc Finger and C-terminal Coiled-coil Domains. Scientific Reports, 9(1), 12794. doi
  22. Chiba S., Ohue M., Gryniukova A., Borysko P., Zozulya S., Yasuo N., Yoshino R., Ikeda K., Shin WH., Kihara D., Iwadate M., Umeyama H., Ichikawa T., Teramoto R., Hsin KY., Gupta V., Kitano H., Sakamoto M., Higuchi A., Miura N., Yura K., Mochizuki M., Ramakrishnan C., Thangakani AM., Velmurugan D., Gromiha MM., Nakane I., Uchida N., Hakariya H., Tan M., Nakamura HK., Suzuki SD., Ito T., Kawatani M., Kudoh K., Takashina S., Yamamoto KZ., Moriwaki Y., Oda K., Kobayashi D., Okuno T., Minami S., Chikenji G., Prathipati P., Nagao C., Mohsen A., Ito M., Mizuguchi K., Honma T., Ishida T., Hirokawa T., Akiyama Y., Sekijima M. (2019). A prospective compound screening contest identified broader inhibitors for Sirtuin 1. Scientific Reports, 9(1), 19585. doi

2018

  1. Kikuchi, A., Nasir, F. B. M., Inami, A., Mohsen, A., Watanuki, S., Miyake, M., Takeda, K., Koike, D., Ito, T., Sasakawa, J., Matsuda, R., Hiraoka, K., Maurer, M., Yanai, K., Watabe, H., & Tashiro, M. (2018). Effects of levocetirizine and diphenhydramine on regional glucose metabolic changes and hemodynamic responses in the human prefrontal cortex during cognitive tasks. Human Psychopharmacology, 33(2), e2655. doi
  2. Park, J., Li, P.-F., Ichijo, T., Nasu, M., & Yamaguchi, N. (2018). Effects of Asian dust events on atmospheric bacterial communities at different distances downwind of the source region. Journal of Environmental Sciences, 72,133-139. doi
  3. 水口 賢司. (2017). 『創薬の初期研究におけるデータベース構築とモデリング』. 学術会議叢書, 25, 25–31. doi
  4. Fujiwara T., Kamada M., & Okuno Y. (2018). Artificial Intelligence in Drug Discovery. Gan to Kagaku Ryoho, 4, 593–596.
  5. 藤原大, & 水口賢司. (2018). 『創薬とファーマコゲノミクス』. 小児内科, 1号, 103–106.
  6. 渡邉怜子, 江崎剛史, 夏目やよい, 佐藤朋広, 長尾知生子, 川島和, & 水口賢司. (2018). 『薬物動態・毒性予測のためのデータベースと創薬』. マテリアルズ・インフォマティクスによる材料開発と活用集　～データベースの構築、記述子の設計法、モデル作成～ 技術情報協会, -.
  7. 長尾知生子, & 種石慶. (2018). 『データベース・計算環境～知識データベース・AI基礎』. 医薬ジャーナル, 54巻9号, 2063–2067.
  8. 夏目やよい. (2018). 『４．バイオメディカル・基礎から臨床への開発プロセス（２）１）トランスレーショナルリサーチと機械学習』. 医薬ジャーナル, 9号, 2049–2053.
  9. 伊藤眞里. (2018). 『４．バイオメディカル・基礎から臨床への開発プロセス（1）～人工知能を用いる創薬テーマ創出』. 医薬ジャーナル, 9号, 2029–2032.
  10. 奥野恭史, 水口賢司, & 本間光貴. (2018). 『１．序文　～LINCの設立とAI創薬～』. 医薬ジャーナル, 9号, 2015–2017.
  11. 長尾 知生子, 夏目 やよい, & 水口 賢司. (2018). 『創薬における計算機の果たす役割　～　プレシジョンメディシンに向けて』. Presicion Medicine, 1, 28–31.
  12. 夏目 やよい, 相㟢 健一, 北嶋 聡, 陳 怡安, 水口 賢司, & 菅野 純. (2018). 『TargetMineによる標的予測』. 日本毒性学会学術年会, 45.1, S11-4. doi
  13. Tripathi, L. P., Chen, Y.-A., Mizuguchi, K., & Morita, E. (2019b). Network-Based Analysis of Host-Pathogen Interactions. Encyclopedia of Bioinformatics and Computational Biology, 932–937. doi
  14. Afanasyeva, A., Bockwoldt, M., Cooney, C. R., Heiland, I., & Gossmann, T. I. (2018). Human long intrinsically disordered protein regions are frequent targets of positive selection. Genome Research, 28(7), 975–982. doi
  15. Tripathi, L. P., Esaki, T., N. Itoh, M., Chen, Y.-A., & Mizuguchi, K. (2018). Integrative Analysis of Multi-Omics Data. Encyclopedia of Bioinformatics and Computational Biology, 3,194-199. doi
  16. Watanabe, R., Esaki, T., Kawashima, H., Natsume-Kitatani, Y., Nagao, C., Ohashi, R., & Mizuguchi, K. (2018). Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges. Molecular Pharmaceutics, 15(11), 5302–5311. doi
  17. Tripathi, L. P., Chen, Y.-A., Mizuguchi, K., & Morita, E. (2019a). Network-Based Analysis of Host-Pathogen Interactions. Encyclopedia of Bioinformatics and Computational Biology, 3, 932-937. doi
  18. Tripathi, L. P., Murakami, Y., Chen, Y.-A., & Mizuguchi, K. (2018). Network-Based Analysis for Biological Discovery. Encyclopedia of Bioinformatics and Computational Biology, 3,283-291. doi
  19. Jin, Y., Takeda, Y., Kondo, Y., Tripathi, L. P., Kang, S., Takeshita, H., Kuhara, H., Maeda, Y., Higashiguchi, M., Miyake, K., Morimura, O., Koba, T., Hayama, Y., Koyama, S., Nakanishi, K., Iwasaki, T., Tetsumoto, S., Tsujino, K., Kuroyama, M., Iwahori, K., Hirata, H., Takimoto, T., Suzuki, M., Nagatomo, I., Sugimoto, K., Fujii, Y., Kida, H., Mizuguchi, K., Ito, M., Kijima, T., Rakugi, H., Mekada, E., Tachibana, I., & Kumanogoh, A. (2018). Double deletion of tetraspanins CD9 and CD81 in mice leads to a syndrome resembling accelerated aging. Scientific Reports, 8(1), 5145. doi
  20. Masuta, Y., Yamamoto, T., Natsume-Kitatani, Y., Kanuma, T., Moriishi, E., Kobiyama, K., Mizuguchi, K., Yasutomi, Y., & Ishii, K. J. (2018). An Antigen-Free, Plasmacytoid Dendritic Cell–Targeting Immunotherapy To Bolster Memory CD8+ T Cells in Nonhuman Primates. The Journal of Immunology, 200(6), 2067–2075. doi
  21. Ahmad, S., Prathipati, P., Tripathi, L. P., Chen, Y.-A., Arya, A., Murakami, Y., & Mizuguchi, K. (2018). Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism. Nucleic Acids Research, 46(1), 54–70. doi

2017

  1. Murakami Y., & Mizuguchi K. (2017). PSOPIA: Toward more reliable protein-protein interaction prediction from sequence information. 2017 International Conference on Intelligent Informatics and Biomedical Sciences, Okinawa. , -. Web
  2. Murakami Y., & Mizuguchi K. (2017). Making protein-protein interaction prediction more reliable with a large-scale dataset at the proteome level – ASCSPub. Journal of Bioinformatics and Neuroscience. , - . Web
  3. Tanaka, M., Kobiyama, K., Honda, T., Uchio-Yamada, K., Natsume-Kitatani, Y., Mizuguchi, K., Kabashima, K., & Ishii, K. J. (2018). Essential Role of CARD14 in Murine Experimental Psoriasis. The Journal of Immunology, 200(1), 71–81. doi
  4. Wijaya, E., Igarashi, Y., Nakatsu, N., Haseda, Y., Billaud, J., Chen, Y.-A., Mizuguchi, K., Yamada, H., Ishii, K., & Aoshi, T. (2017). Quantifying the relative immune cell activation from whole tissue/organ-derived differentially expressed gene data. Scientific Reports, 7(1), 12847. doi
  5. Mizuguchi, K. (2017). Database Development and Computational Modelling in Early-Stage Drug Discovery. Trends in the Sciences, 22(7), 7_62-7_65. doi
  6. Esaki, T., Watanabe, R., Kawashima, H., Natsume-Kitatani, Y., & Mizuguchi, K. (2017). Development of a Drug Discovery Informatics System: An Integrated Platform for Pharmacokinetic and Toxicological Modelling. Journal of Pharmaceutical Science and Technology, Japan, 77(4), 211–215. doi
  7. Hosomi, K., Ohno, H., Murakami, H., Natsume-Kitatani, Y., Tanisawa, K., Hirata, S., Suzuki, H., Nagatake, T., Nishino, T., Mizuguchi, K., Miyachi, M., & Kunisawa, J. (2017). Method for preparing DNA from feces in guanidine thiocyanate solution affects 16S rRNA-based profiling of human microbiota diversity. Scientific Reports, 7(1), 4339. doi
  8. Sowdhamini, R., & Mizuguchi, K. (2017). Editorial—Sequences and topology. Current Opinion in Structural Biology, 44, vii–viii. doi
  9. Andrabi, M., Hutchins, A. P., Miranda-Saavedra, D., Kono, H., Nussinov, R., Mizuguchi, K., & Ahmad, S. (2017). Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences. Scientific Reports, 7(1), 4071. doi
  10. Yoshimaru, T., Ono, M., Bando, Y., Chen, Y.-A., Mizuguchi, K., Shima, H., Komatsu, M., Imoto, I., Izumi, K., Honda, J., Miyoshi, Y., Sasa, M., & Katagiri, T. (2017). A-kinase anchoring protein BIG3 coordinates oestrogen signalling in breast cancer cells. Nature Communications, 8, 15427. doi
  11. Nyström-Persson, J., Natsume-Kitatani, Y., Igarashi, Y., Satoh, D., & Mizuguchi, K. (2017). Interactive Toxicogenomics: Gene set discovery, clustering and analysis in Toxygates. Scientific Reports, 7(1), 1390. doi
  12. Murakami, Y., Tripathi, L. P., Prathipati, P., & Mizuguchi, K. (2017). Network analysis and in silico prediction of protein–protein interactions with applications in drug discovery. Current Opinion in Structural Biology, 44, 134–142. doi
  13. Hamano, Y., Kida, H., Ihara, S., Murakami, A., Yanagawa, M., Ueda, K., Honda, O., Tripathi, L. P., Arai, T., Hirose, M., Hamasaki, T., Yano, Y., Kimura, T., Kato, Y., Takamatsu, H., Otsuka, T., Minami, T., Hirata, H., Inoue, K., Nagatomo, I., Takeda, Y., Mori, M., Nishikawa, H., Mizuguchi, K., Kijima, T., Kitaichi, M., Tomiyama, N., Inoue, Y., & Kumanogoh, A. (2017). Classification of idiopathic interstitial pneumonias using anti–myxovirus resistance-protein 1 autoantibody. Scientific Reports, 7. doi
  14. Natsume-Kitatani Y., & Mizuguchi K. (2017). Computational systems biology for drug discovery: from molecules, structures to networks. Folia Pharmacologica Japonica, 149(2), 91–95. doi
  15. Natsume-Kitatani, Y., Nyström-Persson, J., Igarashi, Y., Satoh, D., & Mizuguchi, K. (2017). Integrated toxicogenomics analysis with Toxygates for inferring molecular mechanisms. Genomics and Computational Biology, 3(1), e37–e37. doi

2016

  1. Tsuchiya, Y., & Mizuguchi, K. (2016). The diversity of H3 loops determines the antigen‐binding tendencies of antibody CDR loops. Protein Science : A Publication of the Protein Society, 25(4), 815–825. doi
  2. Takashima, S., Oka, Y., Fujiki, F., Morimoto, S., Nakajima, H., Nakae, Y., Nakata, J., Nishida, S., Hosen, N., Tatsumi, N., Mizuguchi, K., Hashimoto, N., Oji, Y., Tsuboi, A., Kumanogoh, A., & Sugiyama, H. (2016). Syndecan-4 as a biomarker to predict clinical outcome for glioblastoma multiforme treated with WT1 peptide vaccine. Future Science OA, 2(4). doi
  3. Sequence and Structural Determinants of Antigen Binding in Antibody CDR Loops. (2016). Asia Pacific Biotech News – Featuring News, Interviews Information in APAC. , . Web
  4. Murakami, Y., Omori, S., & Kinoshita, K. (2016). NLDB: a database for 3D protein–ligand interactions in enzymatic reactions. Journal of Structural and Functional Genomics, 17(4), 101–110. doi
  5. Tsuchiya, Y., Jounai, N., Takeshita, F., Ishii, K. J., & Mizuguchi, K. (2016). Ligand-induced Ordering of the C-terminal Tail Primes STING for Phosphorylation by TBK1. EBioMedicine, 9, 87–96. doi
  6. Prathipati, P., Nagao, C., Ahmad, S., & Mizuguchi, K. (2016). Improved pose and affinity predictions using different protocols tailored on the basis of data availability. Journal of Computer-Aided Molecular Design, 30(9), 817–828. doi
  7. Sakane, A., Yoshizawa, S., Nishimura, M., Tsuchiya, Y., Matsushita, N., Miyake, K., Horikawa, K., Imoto, I., Mizuguchi, C., Saito, H., Ueno, T., Matsushita, S., Haga, H., Deguchi, S., Mizuguchi, K., Yokota, H., & Sasaki, T. (2016). Conformational plasticity of JRAB/MICAL-L2 provides “law and order” in collective cell migration. Molecular Biology of the Cell, 27(20), 3095–3108. doi
  8. Yotsukura, S., duVerle, D., Hancock, T., Natsume-Kitatani, Y., & Mamitsuka, H. (2017). Computational recognition for long non-coding RNA (lncRNA): Software and databases. Briefings in Bioinformatics, 18(1), 9–27. doi
  9. Natsume-Kitatani, Y., & Mamitsuka, H. (2016). Classification of Promoters Based on the Combination of Core Promoter Elements Exhibits Different Histone Modification Patterns. PLoS ONE, 11(3). doi
  10. Chen, Y.-A., Tripathi, L. P., & Mizuguchi, K. (2016). An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework. Database: The Journal of Biological Databases and Curation, 2016. doi

2015

  1. Nakae, Y., Oka, Y., Fujiki, F., Morimoto, S., Kamiya, T., Takashima, S., Nakata, J., Nishida, S., Nakajima, H., Hosen, N., Tsuboi, A., Kyo, T., Oji, Y., Mizuguchi, K., Kumanogoh, A., & Sugiyama, H. (2015). Two distinct effector memory cell populations of WT1 (Wilms’ tumor gene 1)-specific cytotoxic T lymphocytes in acute myeloid leukemia patients. Cancer Immunology, Immunotherapy; Heidelberg, 64(7), 791–804. Web
  2. Philip Prathipati, Kenji Mizuguchi (2016). Systems Biology Approaches to a Rational Drug Discovery Paradigm. Current Topics in Medicinal Chemistry., . doi
  3. Koo, C. X., Kobiyama, K., Shen, Y. J., LeBert, N., Ahmad, S., Khatoo, M., Aoshi, T., Gasser, S., & Ishii, K. J. (2015). RNA Polymerase III Regulates Cytosolic RNA:DNA Hybrids and Intracellular MicroRNA Expression. Journal of Biological Chemistry, 290(12), 7463–7473. doi
  4. Tsujii, A., Miyamoto, Y., Moriyama, T., Tsuchiya, Y., Obuse, C., Mizuguchi, K., Oka, M., & Yoneda, Y. (2015). Retinoblastoma-binding Protein 4-regulated Classical Nuclear Transport Is Involved in Cellular Senescence. The Journal of Biological Chemistry, 290(49), 29375–29388. doi
  5. Lee, J., Park, E. J., Yuki, Y., Ahmad, S., Mizuguchi, K., Ishii, K. J., Shimaoka, M., & Kiyono, H. (2015). Profiles of microRNA networks in intestinal epithelial cells in a mouse model of colitis. Scientific Reports, 5, 18174. doi
  6. Camargo, L. M., Zhang, X. D., Loerch, P., Caceres, R. M., Marine, S. D., Uva, P., Ferrer, M., Rinaldis, E. de, Stone, D. J., Majercak, J., Ray, W. J., Yi-An, C., Shearman, M. S., & Mizuguchi, K. (2015). Pathway-Based Analysis of Genome-Wide siRNA Screens Reveals the Regulatory Landscape of App Processing. PLOS ONE, 10(2), e0115369. doi
  7. Ito, M., Nakagawa, S., Mizuguchi, K., & Okumura, T. (2015). Integration of Disease Entries Across OMIM, Orphanet, and a Proprietary Knowledge Base. Current Approaches in Applied Artificial Intelligence, 120–130. doi
  8. Chiba, S., Ikeda, K., Ishida, T., Gromiha, M. M., Taguchi, Y. -h, Iwadate, M., Umeyama, H., Hsin, K.-Y., Kitano, H., Yamamoto, K., Sugaya, N., Kato, K., Okuno, T., Chikenji, G., Mochizuki, M., Yasuo, N., Yoshino, R., Yanagisawa, K., Ban, T., Teramoto, R., Ramakrishnan, C., Thangakani, A. M., Velmurugan, D., Prathipati, P., Ito, J., Tsuchiya, Y., Mizuguchi, K., Honma, T., Hirokawa, T., Akiyama, Y., & Sekijima, M. (2015). Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. Scientific Reports, 5, 17209. doi
  9. Kim, N.-H., Yoshimaru, T., Chen, Y.-A., Matsuo, T., Komatsu, M., Miyoshi, Y., Tanaka, E., Sasa, M., Mizuguchi, K., & Katagiri, T. (2015). BIG3 Inhibits the Estrogen-Dependent Nuclear Translocation of PHB2 via Multiple Karyopherin-Alpha Proteins in Breast Cancer Cells. PLoS ONE, 10(6). doi

2014

  1. Nagao, C., Nagano, N., & Mizuguchi, K. (2014). Prediction of Detailed Enzyme Functions and Identification of Specificity Determining Residues by Random Forests. PLOS ONE, 9(1), e84623. doi
  2. Ito, J., Ikeda, K., Yamada, K., Mizuguchi, K., & Tomii, K. (2015). PoSSuM v.2.0: data update and a new function for investigating ligand analogs and target proteins of small-molecule drugs. Nucleic Acids Research, 43(Database issue), D392–D398. doi
  3. Yamashita, K., Ikeda, K., Amada, K., Liang, S., Tsuchiya, Y., Nakamura, H., Shirai, H., & Standley, D. M. (2014). Kotai Antibody Builder: automated high-resolution structural modeling of antibodies. Bioinformatics, 30(22), 3279–3280. doi
  4. Chen, Y.-A., Tripathi, L. P., Dessailly, B. H., Nyström-Persson, J., Ahmad, S., & Mizuguchi, K. (2014). Integrated Pathway Clusters with Coherent Biological Themes for Target Prioritisation. PLOS ONE, 9(6), e99030. doi
  5. Murakami, Y., & Mizuguchi, K. (2014). Homology-based prediction of interactions between proteins using Averaged One-Dependence Estimators. BMC Bioinformatics, 15, 213. doi
  6. Shirai, H., Ikeda, K., Yamashita, K., Tsuchiya, Y., Sarmiento, J., Liang, S., Morokata, T., Mizuguchi, K., Higo, J., Standley, D. M., & Nakamura, H. (2014). High-resolution modeling of antibody structures by a combination of bioinformatics, expert knowledge, and molecular simulations. Proteins: Structure, Function, and Bioinformatics, 82(8), 1624–1635. doi
  7. Yamada, H., Nagao, C., Haredy, A. M., Mori, Y., Mizuguchi, K., Yamanishi, K., & Okamoto, S. (2014). Dextran sulfate-resistant A/Puerto Rico/8/34 influenza virus is associated with the emergence of specific mutations in the neuraminidase glycoprotein. Antiviral Research, 111, 69–77. doi
  8. Fujita, J., Maeda, Y., Nagao, C., Tsuchiya, Y., Miyazaki, Y., Hirose, M., Mizohata, E., Matsumoto, Y., Inoue, T., Mizuguchi, K., & Matsumura, H. (2014). Crystal structure of FtsA from Staphylococcus aureus. FEBS Letters, 588(10), 1879–1885. doi
  9. Chen, Y.-A., Murakami, Y., Ahmad, S., Yoshimaru, T., Katagiri, T., & Mizuguchi, K. (2014). Brefeldin A-inhibited guanine nucleotide-exchange protein 3 (BIG3) is predicted to interact with its partner through an ARM-type α-helical structure. BMC Research Notes, 7, 435. doi
  10. Takemura, N., Kawasaki, T., Kunisawa, J., Sato, S., Lamichhane, A., Kobiyama, K., Aoshi, T., Ito, J., Mizuguchi, K., Karuppuchamy, T., Matsunaga, K., Miyatake, S., Mori, N., Tsujimura, T., Satoh, T., Kumagai, Y., Kawai, T., Standley, D. M., Ishii, K. J., Kiyono, H., Akira, S., & Uematsu, S. (2014). Blockade of TLR3 protects mice from lethal radiation-induced gastrointestinal syndrome. Nature Communications, 5, 3492. doi
  11. Lensink, M. F., Moal, I. H., Bates, P. A., Kastritis, P. L., Melquiond, A. S. J., Karaca, E., Schmitz, C., van Dijk, M., Bonvin, A. M. J. J., Eisenstein, M., Jiménez-García, B., Grosdidier, S., Solernou, A., Pérez-Cano, L., Pallara, C., Fernández-Recio, J., Xu, J., Muthu, P., Kilambi, K. P., Gray, J. J., Grudinin, S., Derevyanko, G., Mitchell, J. C., Wieting, J., Kanamori, E., Tsuchiya, Y., Murakami, Y., Sarmiento, J., Standley, D. M., Shirota, M., Kinoshita, K., Nakamura, H., Chavent, M., Ritchie, D. W., Park, H., Ko, J., Lee, H., Seok, C., Shen, Y., Kozakov, D., Vajda, S., Kundrotas, P. J., Vakser, I. A., Pierce, B. G., Hwang, H., Vreven, T., Weng, Z., Buch, I., Farkash, E., Wolfson, H. J., Zacharias, M., Qin, S., Zhou, H.-X., Huang, S.-Y., Zou, X., Wojdyla, J. A., Kleanthous, C., & Wodak, S. J. (2014). Blind Prediction of Interfacial Water Positions in CAPRI. Proteins, 82(4), 620–632. doi
  12. Katayama, T., Wilkinson, M. D., Aoki-Kinoshita, K. F., Kawashima, S., Yamamoto, Y., Yamaguchi, A., Okamoto, S., Kawano, S., Kim, J.-D., Wang, Y., Wu, H., Kano, Y., Ono, H., Bono, H., Kocbek, S., Aerts, J., Akune, Y., Antezana, E., Arakawa, K., Aranda, B., Baran, J., Bolleman, J., Bonnal, R. J., Buttigieg, P. L., Campbell, M. P., Chen, Y., Chiba, H., Cock, P. J., Cohen, K. B., Constantin, A., Duck, G., Dumontier, M., Fujisawa, T., Fujiwara, T., Goto, N., Hoehndorf, R., Igarashi, Y., Itaya, H., Ito, M., Iwasaki, W., Kalaš, M., Katoda, T., Kim, T., Kokubu, A., Komiyama, Y., Kotera, M., Laibe, C., Lapp, H., Lütteke, T., Marshall, M. S., Mori, T., Mori, H., Morita, M., Murakami, K., Nakao, M., Narimatsu, H., Nishide, H., Nishimura, Y., Nystrom-Persson, J., Ogishima, S., Okamura, Y., Okuda, S., Oshita, K., Packer, N. H., Prins, P., Ranzinger, R., Rocca-Serra, P., Sansone, S., Sawaki, H., Shin, S.-H., Splendiani, A., Strozzi, F., Tadaka, S., Toukach, P., Uchiyama, I., Umezaki, M., Vos, R., Whetzel, P. L., Yamada, I., Yamasaki, C., Yamashita, R., York, W. S., Zmasek, C. M., Kawamoto, S., & Takagi, T. (2014). BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains. Journal of Biomedical Semantics, 5, 5. doi
  13. Kumagai, A., Fujita, A., Yokoyama, T., Nonobe, Y., Hasaba, Y., Sasaki, T., Itoh, Y., Koura, M., Suzuki, O., Adachi, S., Ryo, H., Kohara, A., Tripathi, L. P., Sanosaka, M., Fukushima, T., Takahashi, H., Kitagawa, K., Nagaoka, Y., Kawahara, H., Mizuguchi, K., Nomura, T., Matsuda, J., Tabata, T., & Takemori, H. (2014). Altered Actions of Memantine and NMDA-Induced Currents in a New Grid2-Deleted Mouse Line. Genes, 5(4), 1095–1114. doi

2013

  1. Tripathi, L. P., Kambara, H., Chen, Y.-A., Nishimura, Y., Moriishi, K., Okamoto, T., Morita, E., Abe, T., Mori, Y., Matsuura, Y., & Mizuguchi, K. (2013). Understanding the Biological Context of NS5A–Host Interactions in HCV Infection: A Network-Based Approach. Journal of Proteome Research, 12(6), 2537–2551. doi
  2. Nyström-Persson, J., Igarashi, Y., Ito, M., Morita, M., Nakatsu, N., Yamada, H., & Mizuguchi, K. (2013). Toxygates: interactive toxicity analysis on a hybrid microarray and linked data platform. Bioinformatics, 29(23), 3080–3086. doi
  3. Tang, C. K., Aoshi, T., Jounai, N., Ito, J., Ohata, K., Kobiyama, K., Dessailly, B. H., Kuroda, E., Akira, S., Mizuguchi, K., Coban, C., & Ishii, K. J. (2013). The Chemotherapeutic Agent DMXAA as a Unique IRF3-Dependent Type-2 Vaccine Adjuvant. PLoS ONE, 8(3). doi
  4. Tiwari, P., Tripathi, L. P., Nishikawa-Matsumura, T., Ahmad, S., Song, S.-N. J., Isobe, T., Mizuguchi, K., & Yoshizaki, K. (2013). Prediction and experimental validation of a putative non-consensus binding site for transcription factor STAT3 in serum amyloid A gene promoter. Biochimica et Biophysica Acta (BBA) – General Subjects, 1830(6), 3650–3655. doi
  5. Yoshimaru, T., Komatsu, M., Matsuo, T., Chen, Y.-A., Murakami, Y., Mizuguchi, K., Mizohata, E., Inoue, T., Akiyama, M., Yamaguchi, R., Imoto, S., Miyano, S., Miyoshi, Y., Sasa, M., Nakamura, Y., & Katagiri, T. (2013). Targeting BIG3–PHB2 interaction to overcome tamoxifen resistance in breast cancer cells. Nature Communications, 4. doi
  6. Dessailly, B. H., Dawson, N. L., Mizuguchi, K., & Orengo, C. A. (2013). Functional site plasticity in domain superfamilies. Biochimica et Biophysica Acta, 1834(5), 874–889. doi
  7. Fujita, J., Miyazaki, Y., Hirose, M., Nagao, C., Mizohata, E., Matsumoto, Y., Mizuguchi, K., Inoue, T., & Matsumura, H. (2013). Expression, purification, crystallization and preliminary crystallographic study of FtsA from methicillin-resistant Staphylococcus aureus. Acta Crystallographica Section F: Structural Biology and Crystallization Communications, 69(Pt 8), 895–898. doi
  8. Hutchins, A. P., Diez, D., Takahashi, Y., Ahmad, S., Jauch, R., Tremblay, M. L., & Miranda-Saavedra, D. (2013). Distinct transcriptional regulatory modules underlie STAT3’s cell type-independent and cell type-specific functions. Nucleic Acids Research, 41(4), 2155–2170. doi
  9. Hobro, A. J., Standley, D. M., Ahmad, S., & Smith, N. I. (2013). Deconstructing RNA: optical measurement of composition and structure. Physical Chemistry Chemical Physics, 15(31), 13199–13208. doi
  10. Andrabi, M., Mizuguchi, K., & Ahmad, S. (2014). Conformational changes in DNA-binding proteins: Relationships with precomplex features and contributions to specificity and stability. Proteins: Structure, Function, and Bioinformatics, 82(5), 841–857. doi
  11. Moretti R., Fleishman SJ., Agius R., Torchala M., Bates PA., Kastritis PL., Rodrigues JP., Trellet M., Bonvin AM., Cui M., Rooman M., Gillis D., Dehouck Y., Moal I., Romero-Durana M., Perez-Cano L., Pallara C., Jimenez B., Fernandez-Recio J., Flores S.,Pacella M., Praneeth Kilambi K., Gray JJ., Popov P., Grudinin S., Esquivel-Rodríguez J., Kihara D., Zhao N., Korkin D., Zhu X., Demerdash ON., Mitchell JC., Kanamori E., Tsuchiya Y., Nakamura H., Lee H., Park H., Seok C., Sarmiento J., Liang S., Teraguchi S., Standley DM., Shimoyama H., Terashi G., Takeda-Shitaka M., Iwadate M., Umeyama H., Beglov D., Hall DR., Kozakov D., Vajda S., Pierce BG., Hwang H., Vreven T., Weng Z., Huang Y., Li H., Yang X., Ji X., Liu S., Xiao Y., Zacharias M., Qin S., Zhou HX., Huang SY., Zou X., Velankar S., Janin J., Wodak SJ., Baker D. (2013). Community-wide Evaluation of Methods for Predicting the Effect of Mutations on Protein-Protein Interactions. Proteins, 81(11), 1980–1987. doi

2012

  1. Morita, M., Igarashi, Y., Ito, M., Chen, Y.-A., Nagao, C., Sakaguchi, Y., Sakate, R., Masui, T., & Mizuguchi, K. (2012). Sagace: A web-based search engine for biomedical databases in Japan. BMC Research Notes, 5(1), 604. doi
  2. Tripathi, L. P., Kambara, H., Moriishi, K., Morita, E., Abe, T., Mori, Y., Chen, Y.-A., Matsuura, Y., & Mizuguchi, K. (2012). Proteomic Analysis of Hepatitis C Virus (HCV) Core Protein Transfection and Host Regulator PA28γ Knockout in HCV Pathogenesis: A Network-Based Study. Journal of Proteome Research, 11(7), 3664–3679. doi
  3. Blower, T. R., Short, F. L., Rao, F., Mizuguchi, K., Pei, X. Y., Fineran, P. C., Luisi, B. F., & Salmond, G. P. C. (2012). Identification and classification of bacterial Type III toxin–antitoxin systems encoded in chromosomal and plasmid genomes. Nucleic Acids Research, 40(13), 6158–6173. doi
  4. Ihara, S., Kida, H., Arase, H., Tripathi, L. P., Chen, Y.-A., Kimura, T., Yoshida, M., Kashiwa, Y., Hirata, H., Fukamizu, R., Inoue, R., Hasegawa, K., Goya, S., Takahashi, R., Minami, T., Tsujino, K., Suzuki, M., Kohmo, S., Inoue, K., Nagatomo, I., Takeda, Y., Kijima, T., Mizuguchi, K., Tachibana, I., & Kumanogoh, A. (2012). Inhibitory Roles of Signal Transducer and Activator of Transcription 3 in Antitumor Immunity during Carcinogen-Induced Lung Tumorigenesis. Cancer Research, 72(12), 2990–2999. doi
  5. Nagao, C., Izako, N., Soga, S., Khan, S. H., Kawabata, S., Shirai, H., & Mizuguchi, K. (2012). Computational design, construction, and characterization of a set of specificity determining residues in protein–protein interactions. Proteins: Structure, Function, and Bioinformatics, 80(10), 2426–2436. doi
  6. Keeble-Gagnère, G., Nyström-Persson, J., Bellgard, M. I., & Mizuguchi, K. (2012). An Open Framework for Extensible Multi-stage Bioinformatics Software. Pattern Recognition in Bioinformatics, 106–117. doi
  7. Tripathi, L. P., & Mizuguchi, K. (2012). A combined proteomics and computational approach provides a better understanding of HCV-induced liver disease. Expert Review of Proteomics, 9(5), 493–496. doi

2011

  1. Williams, S. G., Madan, R., Norris, M. G. S., Archer, J., Mizuguchi, K., Robertson, D. L., & Lovell, S. C. (2011). Using Knowledge of Protein Structural Constraints to Predict the Evolution of HIV-1. Journal of Molecular Biology, 410(5), 1023–1034. doi
  2. Chen, Y.-A., Tripathi, L. P., & Mizuguchi, K. (2011). TargetMine, an Integrated Data Warehouse for Candidate Gene Prioritisation and Target Discovery. PLOS ONE, 6(3), e17844. doi
  3. 平田 みつひ, シャンダー・ アハマド, 菅 三佳, 藤木 彩加, 松村 紘子, 若林 真理, 上田 直子, 劉 克紅, 林田 みどり, 平山 知子, 小原 有弘, 柳原 佳奈, 水口 賢司, & 古江－楠田 美保. (2011). 日本におけるヒトES、iPS細胞研究標準化：. 組織培養研究, 30(2+3+4), 145–157. doi
  4. Kahali, B., Ahmad, S., & Ghosh, T. C. (2011). Selective constraints in yeast genes with differential expressivity: Codon pair usage and mRNA stability perspectives. Gene, 481(2), 76–82. doi
  5. Fernandez, M., Kumagai, Y., Standley, D. M., Sarai, A., Mizuguchi, K., & Ahmad, S. (2011). Prediction of dinucleotide-specific RNA-binding sites in proteins. BMC Bioinformatics, 12(13), S5. doi
  6. Firoz, A., Malik, A., Joplin, K. H., Ahmad, Z., Jha, V., & Ahmad, S. (2011). Residue propensities, discrimination and binding site prediction of adenine and guanine phosphates. BMC Biochemistry, 12, 20. doi
  7. Ahmad, S., & Mizuguchi, K. (2011). Partner-Aware Prediction of Interacting Residues in Protein-Protein Complexes from Sequence Data. PLOS ONE, 6(12), e29104. doi
  8. Morita, M., Katta, A. M., Ahmad, S., Mori, T., Sugita, Y., & Mizuguchi, K. (2011). Lipid recognition propensities of amino acids in membrane proteins from atomic resolution data. BMC Biophysics, 4(1), 21. doi
  9. Schwarz, U. I., Meyer zu Schwabedissen, H. E., Tirona, R. G., Suzuki, A., Leake, B. F., Mokrab, Y., Mizuguchi, K., Ho, R. H., & Kim, R. B. (2011). Identification of novel functional Organic Anion-transporting Polypeptide 1B3 (OATP1B3) polymorphisms and assessment of substrate specificity. Pharmacogenetics and Genomics, 21(3), 103–114. doi
  10. Ahmad, S., & Sarai, A. (2011). Analysis of electric moments of RNA-binding proteins: implications for mechanism and prediction. BMC Structural Biology, 11(1), 8. doi

2010

  1. Nagao, C., Nagano, N., & Mizuguchi, K. (2010). Relationships between functional subclasses and information contained in active-site and ligand-binding residues in diverse superfamilies. Proteins: Structure, Function, and Bioinformatics, 78(10), 2369–2384. doi
  2. Fernandez, M., Ahmad, S., & Sarai, A. (2010). Proteochemometric Recognition of Stable Kinase Inhibition Complexes Using Topological Autocorrelation and Support Vector Machines. Journal of Chemical Information and Modeling, 50(6), 1179–1188. doi
  3. Someya, S., Kakuta, M., Morita, M., Sumikoshi, K., Cao, W., Ge, Z., Hirose, O., Nakamura, S., Terada, T., & Shimizu, K. (2010). Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines. Advances in Bioinformatics, 2010. doi
  4. Murakami, Y., Spriggs, R. V., Nakamura, H., & Jones, S. (2010). PiRaNhA: a server for the computational prediction of RNA-binding residues in protein sequences. Nucleic Acids Research, 38(Web Server issue), W412–W416. doi
  5. Singh, Y. H., Andrabi, M., Kahali, B., Ghosh, T. C., Mizuguchi, K., Kochetov, A. V., & Ahmad, S. (2010). On nucleotide solvent accessibility in RNA structure. Gene, 463(1), 41–48. doi
  6. Tripathi, L. P., Kataoka, C., Taguwa, S., Moriishi, K., Mori, Y., Matsuura, Y., & Mizuguchi, K. (2010). Network based analysis of hepatitis C virus Core and NS4B protein interactions. Molecular BioSystems, 6(12), 2539–2553. doi
  7. Ahmad, S., Singh, Y. H., Paudel, Y., Mori, T., Sugita, Y., & Mizuguchi, K. (2010). Integrated prediction of one-dimensional structural features and their relationships with conformational flexibility in helical membrane proteins. BMC Bioinformatics, 11, 533. doi
  8. Mondal, S., Nagao, C., & Mizuguchi, K. (2010). Detecting subtle functional differences in ketopantoate reductase and related enzymes using a rule-based approach with sequence-structure homology recognition scores. Protein Engineering, Design and Selection, 23(11), 859–869. doi
  9. Yoshioka, Y., Watanabe, H., Morishige, T., Yao, X., Ikemizu, S., Nagao, C., Ahmad, S., Mizuguchi, K., Tsunoda, S., Tsutsumi, Y., Mukai, Y., Okada, N., & Nakagawa, S. (2010). Creation of lysine-deficient mutant lymphotoxin-α with receptor selectivity by using a phage display system. Biomaterials, 31(7), 1935–1943. doi
  10. Ahmad, S., Keskin, O., Mizuguchi, K., Sarai, A., & Nussinov, R.(2010). CCRXP: exploring clusters of conserved residues in protein structures.Nucleic Acids Research,38(Web Server issue), W398–W401.doi
  11. Murakami, Y., & Mizuguchi, K. (2010). Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein–protein interaction sites. Bioinformatics, 26(15), 1841–1848. doi
  12. Mokrab, Y., Stevens, T. J., & Mizuguchi, K. (2010). A structural dissection of amino acid substitutions in helical transmembrane proteins. Proteins: Structure, Function, and Bioinformatics, 78(14), 2895–2907. doi

2009

  1. Ahmad, S. (2009). Sequence-dependence and prediction of nucleotide solvent accessibility in double stranded DNA. Gene, 428(1), 25–30. doi
  2. Morita, M., Saito, S., Ikeda, K., Ohno, K., Sugawara, K., Suzuki, T., Togawa, T., & Sakuraba, H. (2009). Structural bases of GM1 gangliosidosis and Morquio B disease. Journal of Human Genetics, 54(9), 510–515. doi
  3. Spriggs, R. V., Murakami, Y., Nakamura, H., & Jones, S. (2009). Protein function annotation from sequence: prediction of residues interacting with RNA. Bioinformatics, 25(12), 1492–1497. doi
  4. Andrabi, M., Mizuguchi, K., Sarai, A., & Ahmad, S. (2009). Prediction of mono- and di-nucleotide-specific DNA-binding sites in proteins using neural networks. BMC Structural Biology, 9, 30. doi
  5. Tateishi, Y., Ariyoshi, M., Igarashi, R., Hara, H., Mizuguchi, K., Seto, A., Nakai, A., Kokubo, T., Tochio, H., & Shirakawa, M. (2009). Molecular Basis for SUMOylation-dependent Regulation of DNA Binding Activity of Heat Shock Factor 2. Journal of Biological Chemistry, 284(4), 2435–2447. doi
  6. Kahali, B., Ahmad, S., & Ghosh, T. C. (2009). Exploring the evolutionary rate differences of party hub and date hub proteins in Saccharomyces cerevisiae protein–protein interaction network. Gene, 429(1), 18–22. doi
  7. Singh, H., & Ahmad, S. (2009). Context dependent reference states of solvent accessibility derived from native protein structures and assessed by predictability analysis. BMC Structural Biology, 9, 25. doi
  8. Furusawa, H., Ozeki, T., Morita, M., & Okahata, Y. (2009). Added Mass Effect on Immobilizations of Proteins on a 27 MHz Quartz Crystal Microbalance in Aqueous Solution. Analytical Chemistry, 81(6), 2268–2273. doi
  9. Mondal, S., & Mizuguchi, K. (2009). Structural insights into the enzyme mechanism of a new family of d-2-hydroxyacid dehydrogenases, a close homolog of 2-ketopantoate reductase. Genome Informatics. International Conference on Genome Informatics, 23(1), 98–105. doi

2008

  1. Singh, R., Ali Dar, T., Ahmad, S., Moosavi-Movahedi, A. A., & Ahmad, F. (2008). A new method for determining the constant-pressure heat capacity change associated with the protein denaturation induced by guanidinium chloride (or urea). Biophysical Chemistry, 133(1), 81–89. doi
  2. Malik, A., Arija M Arif, S., Ahmad, S., & Elumalai, S. (2008). A molecular and in silico characterization of Hev b 4, a glycosylated latex allergen. International Journal of Biological Macromolecules, 42, 185–190. doi
  3. Hart, S. E., Howe, C. J., Mizuguchi, K., & Fernandez-Recio, J. (2008). Docking of cytochrome c6 and plastocyanin to the aa3-type cytochrome c oxidase in the cyanobacterium Phormidium laminosum. Protein Engineering, Design and Selection, 21(12), 689–698. doi
  4. Bailly, X., Vanin, S., Chabasse, C., Mizuguchi, K., & Vinogradov, S. N. (2008). A phylogenomic profile of hemerythrins, the nonheme diiron binding respiratory proteins. BMC Evolutionary Biology, 8(1), 244. doi
  5. Mokrab, Y., Stevens, T. J., & Mizuguchi, K. (2009). Lipophobicity and the residue environments of the transmembrane α-helical bundle. Proteins: Structure, Function, and Bioinformatics, 74(1), 32–49. doi
  6. Zhu, B., Pennack, J. A., McQuilton, P., Forero, M. G., Mizuguchi, K., Sutcliffe, B., Gu, C.-J., Fenton, J. C., & Hidalgo, A. (2008). Drosophila Neurotrophins Reveal a Common Mechanism for Nervous System Formation. PLOS Biology, 6(11), e284. doi
  7. Ali, R., Hussain, A., Raish, M., Noor, A., Mohammad, S., Sarin, R., Kukreti, H., Khan, N., Ahmad, S., V.S. Deo, S., Husain, S., Tazeen Pasha, S., Basir, S., & Shukla, nootan kumar. (2008). Specific 5′CpG Island Methylation Signatures of FHIT and p16 Genes and Their Potential Diagnostic Relevance in Indian Breast Cancer Patients. DNA and Cell Biology, 27, 517–525. doi
  8. Ahmad, S., Keskin, O., Sarai, A., & Nussinov, R. (2008). Protein–DNA interactions: structural, thermodynamic and clustering patterns of conserved residues in DNA-binding proteins. Nucleic Acids Research, 36(18), 5922–5932. doi
  9. Kochetov, A. V., Ahmad, S., Ivanisenko, V., Volkova, O. A., Kolchanov, N. A., & Sarai, A. (2008). uORFs, reinitiation and alternative translation start sites in human mRNAs. FEBS Letters, 582(9), 1293–1297. doi

2007

  1. Ahmad, S., Singh, Y. H., Araúzo‐Bravo, M. J., & Sarai, A. (2008). Sequence-Based Prediction of Residue-Level Properties in Proteins. Machine Learning in Bioinformatics, 157–187. doi
  2. Camargo, L. M., Collura, V., Rain, J.-C., Mizuguchi, K., Hermjakob, H., Kerrien, S., Bonnert, T. P., Whiting, P. J., & Brandon, N. J. (2007). Disrupted in Schizophrenia 1 Interactome: evidence for the close connectivity of risk genes and a potential synaptic basis for schizophrenia. Molecular Psychiatry, 12(1), 74–86. doi
  3. Mizuguchi, K., Sele, M., & Cubellis, M. (2007). Environment specific substitution tables for thermophilic proteins. BMC Bioinformatics, 8(Suppl 1), S15. doi
  4. Lyne, R., Smith, R., Rutherford, K., Wakeling, M., Varley, A., Guillier, F., Janssens, H., Ji, W., Mclaren, P., North, P., Rana, D., Riley, T., Sullivan, J., Watkins, X., Woodbridge, M., Lilley, K., Russell, S., Ashburner, M., Mizuguchi, K., & Micklem, G. (2007). FlyMine: an integrated database for Drosophila and Anopheles genomics. Genome Biology, 8(7), R129. doi
  5. Mokrab, Y., Bavro, V. N., Mizuguchi, K., Todorov, N. P., Martin, I. L., Dunn, S. M. J., Chan, S. L., & Chau, P.-L. (2007). Exploring ligand recognition and ion flow in comparative models of the human GABA type A receptor. Journal of Molecular Graphics and Modelling, 26(4), 760–774. doi
  6. Vinogradov, S. N., Hoogewijs, D., Bailly, X., Mizuguchi, K., Dewilde, S., Moens, L., & Vanfleteren, J. R. (2007). A model of globin evolution. Gene, 398(1), 132–142. doi
  7. Wang, J.-Y., Lee, H.-M., & Ahmad, S. (2007). SVM-Cabins: Prediction of solvent accessibility using accumulation cutoff set and support vector machine. Proteins: Structure, Function, and Bioinformatics, 68(1), 82–91. doi

2006

  1. Shiozawa, K., Goda, N., Shimizu, T., Mizuguchi, K., Kondo, N., Shimozawa, N., Shirakawa, M., & Hiroaki, H. (2006). The common phospholipid-binding activity of the N-terminal domains of PEX1 and VCP/p97. The FEBS Journal, 273(21), 4959–4971. doi
  2. Shirai, H., Mokrab, Y., & Mizuguchi, K. (2006). The guanidino-group modifying enzymes: Structural basis for their diversity and commonality. Proteins: Structure, Function, and Bioinformatics, 64(4), 1010–1023. doi
  3. Shiozawa, K., Goda, N., Shimizu, T., Mizuguchi, K., Kondo, N., Shimozawa, N., Shirakawa, M., & Hiroaki, H. (2006). The common phospholipid-binding activity of the N-terminal domains of PEX1 and VCP/p97. The FEBS Journal, 273(21), 4959–4971. doi

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