Faculty Profiles - KURATA Hiroyuki

Papers - KURATA Hiroyuki

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Self-replenishment cycles generate a threshold response Reviewed International journal

Kurata H.

Scientific Reports 9 ( 1 ) 2019.12

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

© 2019, The Author(s). Many metabolic cycles, including the tricarboxylic acid cycle, glyoxylate cycle, Calvin cycle, urea cycle, coenzyme recycling, and substrate cycles, are well known to catabolize and anabolize different metabolites for efficient energy and mass conversion. In terms of stoichiometric structure, this study explicitly identifies two types of metabolic cycles. One is the well-known, elementary cycle that converts multiple substrates into different products and recycles one of the products as a substrate, where the recycled substrate is supplied from the outside to run the cycle. The other is the self-replenishment cycle that merges multiple substrates into two or multiple identical products and reuses one of the products as a substrate. The substrates are autonomously supplied within the cycle. This study first defines the self-replenishment cycles that many scientists have overlooked despite its functional importance. Theoretical analysis has revealed the design principle of the self-replenishment cycle that presents a threshold response without any bistability nor cooperativity. To verify the principle, three detailed kinetic models of self-replenishment cycles embedded in an E. coli metabolic system were simulated. They presented the threshold response or digital switch-like function that steeply shift metabolic status.

DOI： 10.1038/s41598-019-53589-1

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Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties Reviewed International journal

Khatun S., Hasan M., Kurata H.

FEBS Letters 593 ( 21 ) 3029 - 3039 2019.11

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

© 2019 Federation of European Biochemical Societies Tuberculosis (TB) is a leading killer caused by Mycobacterium tuberculosis. Recently, anti-TB peptides have provided an alternative approach to combat antibiotic tolerance. We have developed an effective computational predictor, identification of antitubercular peptides (iAntiTB), by the integration of multiple feature vectors deriving from the amino acid sequences via random forest (RF) and support vector machine (SVM) classifiers. The iAntiTB combines the RF and SVM scores via linear regression to enhance the prediction accuracy. To make a robust and accurate predictor, we prepared the two datasets with different types of negative samples. The iAntiTB achieved area under the ROC curve values of 0.896 and 0.946 on the training datasets of the first and second datasets, respectively. The iAntiTB outperformed the other existing predictors.

DOI： 10.1002/1873-3468.13536

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Large-scale assessment of bioinformatics tools for lysine succinylation sites Invited Reviewed International journal

Hasan M.M., Khatun M.S., Kurata H.

Cells 8 ( 2 ) 2019.01

Prediction of: S -nitrosylation sites by integrating support vector machines and random forest Reviewed International journal

Hasan M., Manavalan B., Khatun M., Kurata H.

Molecular Omics 15 ( 6 ) 451 - 458 2019.01

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

© 2019 The Royal Society of Chemistry. Cysteine S-nitrosylation is a type of reversible post-translational modification of proteins, which controls diverse biological processes. It is associated with redox-based cellular signaling to protect against oxidative stress. The identification of S-nitrosylation sites is an important step to reveal the function of proteins; however, experimental identification of S-nitrosylation is expensive and time-consuming work. Hence, sequence-based computational prediction of potential S-nitrosylation sites is highly sought before experimentation. Herein, a novel predictor PreSNO has been developed that integrates multiple encoding schemes by the support vector machine and random forest algorithms. The PreSNO achieved an accuracy and Matthews correlation coefficient value of 0.752 and 0.252 respectively in classifying between SNO and non-SNO sites when evaluated on the independent dataset, outperforming the existing methods. The web application of the PreSNO and its associated datasets are freely available at http://kurata14.bio.kyutech.ac.jp/PreSNO/.

DOI： 10.1039/c9mo00098d

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i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome Reviewed International journal

Hasan M., Manavalan B., Khatun M., Kurata H.

International Journal of Biological Macromolecules 2019.01

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

© 2018 Elsevier B.V. One of the most important epigenetic modifications is N4-methylcytosine, which regulates many biological processes including DNA replication and chromosome stability. Identification of N4-methylcytosine sites is pivotal to understand specific biological functions. Herein, we developed the first bioinformatics tool called i4mC-ROSE for identifying N4-methylcytosine sites in the genomes of Fragaria vesca and Rosa chinensis in the Rosaceae, which utilizes a random forest classifier with six encoding methods that cover various aspects of DNA sequence information. The i4mC-ROSE predictor achieves area under the curve scores of 0.883 and 0.889 for the two genomes during cross-validation. Moreover, the i4mC-ROSE outperforms other classifiers tested in this study when objectively evaluated on the independent datasets. The proposed i4mC-ROSE tool can serve users' demand for the prediction of 4mC sites in the Rosaceae genome. The i4mC-ROSE predictor and utilized datasets are publicly accessible at http://kurata14.bio.kyutech.ac.jp/i4mC-ROSE/.

DOI： 10.1016/j.ijbiomac.2019.12.009

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PreAIP: Computational prediction of anti-inflammatory peptides by integrating multiple complementary features Reviewed International journal

Khatun M., Hasan M., Kurata H.

Frontiers in Genetics 10 ( MAR ) 2019.01

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

© 2019 Khatun, Hasan and Kurata. Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory peptides via biological experiments is often a time-consuming and expensive task. The development of novel in silico predictors is desired to classify potential anti-inflammatory peptides prior to in vitro investigation. Herein, an accurate predictor, called PreAIP (Predictor of Anti-Inflammatory Peptides) was developed by integrating multiple complementary features. We systematically investigated different types of features including primary sequence, evolutionary and structural information through a random forest classifier. The final PreAIP model achieved an AUC value of 0.833 in the training dataset via 10-fold cross-validation test, which was better than that of existing models. Moreover, we assessed the performance of the PreAIP with an AUC value of 0.840 on a test dataset to demonstrate that the proposed method outperformed the two existing methods. These results indicated that the PreAIP is an accurate predictor for identifying AIPs and contributes to the development of AIPs therapeutics and biomedical research. The curated datasets and the PreAIP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/.

DOI： 10.3389/fgene.2019.00129

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SIPMA: A systematic identification of protein-protein interactions in Zea mays using autocorrelation features in a machine-learning framework Reviewed International journal

Khatun M., Hasan M., Mollah M., Kurata H.

Proceedings - 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering, BIBE 2018 122 - 125 2018.12

Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings)

© 2018 IEEE. Zea mays (maize) is one of the most vital crops which are grown widely in the world. To understand the molecular structures and functions of maize, the identification of protein-protein interaction (PPI) is very important. PPI identification by wet lab experiments is time-consuming, expensive and laborious. These days in silico methods that accurately predict potential PPIs based on protein sequence information are highly demanded. Research on PPI prediction in maize is currently very limited, and no dedicated bioinformatics schemes are available. In this work, we proposed a novel approach, termed SIPMA (Systematic Identification of PPI in Maize using Autocorrelation). A machine learning random forest classifier was trained with autocorrelation features to build the prediction model. The SIPMA, which was tested by the experimentally verified PPI dataset of maize, yielded a prediction accuracy of 0.899 when the specificity was 0.969 on the training set. The SIPMA achieved promising performances on the test datasets. Compared with different sequence-based encoding and statistical learning methods, the SIPMA was a powerful computational resource for identifying PPIs in maize.

DOI： 10.1109/BIBE.2018.00030

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iLMS, computational identification of lysine-malonylation sites by combining multiple sequence features Reviewed International journal

Hasan M., Kurata H.

Proceedings - 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering, BIBE 2018 356 - 359 2018.12

Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings)

© 2018 IEEE. Lysine malonylation is a newly discovered post-translational modification of proteins, which plays an important role in regulating many cellular fonctions. Several approaches are available to identify malonylation proteins and its malonylation sites, however; experimental identification of malonylation sites is often laborious and costly. Therefore, computational schemes are needed to identify potential malonylation sites prior to in vitro experimentation. In this paper, a novel computational scheme iLMS (Identification of Lysine-Malonylation Sites) has been developed by combining primary sequences and evolutionary features via a support vector machine classifier. The final iLMS scheme achieved a robust performance in cross-validation test in both human and mouse datasets. For the mouse data, the iLMS predictor outperformed other existing implementations. The iLMS is a promising computational scheme for the prediction of malonylation sites.

DOI： 10.1109/BIBE.2018.00077

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GPSuc: Global prediction of generic and species-specific succinylation sites by aggregating multiple sequence features Reviewed International journal

Hasan M., Kurata H.

PLoS ONE 13 ( 10 ) 2018.10

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

© 2018 Hasan, Kurata. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Lysine succinylation is one of the dominant post-translational modification of the protein that contributes to many biological processes including cell cycle, growth and signal transduction pathways. Identification of succinylation sites is an important step for understanding the function of proteins. The complicated sequence patterns of protein succinylation revealed by proteomic studies highlight the necessity of developing effective species-specific in silico strategies for global prediction succinylation sites. Here we have developed the generic and nine speciesspecific succinylation site classifiers through aggregating multiple complementary features. We optimized the consecutive features using the Wilcoxon-rank feature selection scheme. The final feature vectors were trained by a random forest (RF) classifier. With an integration of RF scores via logistic regression, the resulting predictor termed GPSuc achieved better performance than other existing generic and species-specific succinylation site predictors. To reveal the mechanism of succinylation and assist hypothesis-driven experimental design, our predictor serves as a valuable resource. To provide a promising performance in large-scale datasets, a web application was developed at http://kurata14.bio.kyutech.ac.jp/GPSuc/.

DOI： 10.1371/journal.pone.0200283

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Computational modeling of lysine post-translational modification: an overview. Invited Reviewed International journal

Curr Synthetic Sys Biol 6 1 2018.04

A novel isolation method for cancer prognostic factors via the p53 pathway by a combination of in vitro and in silico analyses Reviewed International journal

Oncoscience ( Impact Journals ) 5 88 - 98 2018.04

Long negative feedback loop enhances period tunability of biological oscillators Reviewed

Maeda K., Kurata H.

Journal of Theoretical Biology 440 21 - 31 2018.03

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

© 2017 Elsevier Ltd Oscillatory phenomena play a major role in organisms. In some biological oscillations such as cell cycles and heartbeats, the period can be tuned without significant changes in the amplitude. This property is called (period) tunability, one of the prominent features of biological oscillations. However, how biological oscillators produce tunable oscillations remains largely unexplored. We tackle this question using computational experiments. It has been reported that positive-plus-negative feedback oscillators produce tunable oscillations through the hysteresis-based mechanism. First, in this study, we confirmed that positive-plus-negative feedback oscillators generate tunable oscillations. Second, we found that tunability is positively correlated with the dynamic range of oscillations. Third, we showed that long negative feedback oscillators without any additional positive feedback loops can produce tunable oscillations. Finally, we computationally demonstrated that by lengthening the negative feedback loop, the Repressilator, known as a non-tunable synthetic gene oscillator, can be converted into a tunable oscillator. This work provides synthetic biologists with clues to design tunable gene oscillators.

DOI： 10.1016/j.jtbi.2017.12.014

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Improved kinetic model of Escherichia coli central carbon metabolism in batch and continuous cultures Reviewed International journal

Kurata H., Sugimoto Y.

Journal of Bioscience and Bioengineering 125 ( 2 ) 251 - 257 2018.02

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

© 2017 The Society for Biotechnology, Japan Many kinetic models of Escherichia coli central metabolism have been built, but few models accurately reproduced the dynamic behaviors of wild type and multiple genetic mutants. In 2016, our latest kinetic model improved problems of existing models to reproduce the cell growth and glucose uptake of wild type, ΔpykA:pykF and Δpgi in a batch culture, while it overestimated the glucose uptake and cell growth rates of Δppc and hardly captured the typical characteristics of the glyoxylate and TCA cycle fluxes for Δpgi and Δppc. Such discrepancies between the simulated and experimental data suggested biological complexity. In this study, we overcame these problems by assuming critical mechanisms regarding the OAA-regulated isocitrate dehydrogenase activity, aceBAK gene regulation and growth suppression. The present model accurately predicts the extracellular and intracellular dynamics of wild type and many gene knockout mutants in batch and continuous cultures. It is now the most accurate, detailed kinetic model of E. coli central carbon metabolism and will contribute to advances in mathematical modeling of cell factories.

DOI： 10.1016/j.jbiosc.2017.09.005

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libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models Reviewed International journal

Maeda Kazuhiro, Boogerd Fred C., Kurata Hiroyuki

IPSJ Transactions on Bioinformatics 11 ( 0 ) 31 - 40 2018.01

A comprehensive review of in silico analysis for protein s-sulfenylation sites Invited Reviewed International journal

Hasan M., Khatun M., Kurata H.

Protein and Peptide Letters 25 ( 9 ) 815 - 821 2018.01

Authorship：Corresponding author Language：English Publishing type：Article, review, commentary, editorial, etc. (scientific journal)

© 2018 Bentham Science Publishers. Background: Cysteine S-sulfenylation is a major type of dynamic post-translational modification of the protein that plays an important role in regulating many biological processes in both of prokaryotic and eukaryotic species. To understand the function of S-sulfenylated proteins, identification of S-sulfenylation sites is an essential step. Due to numerous restrictions of experimental methods, computational prediction of the potential S-sulfenylation sites becomes popular. In this review, we discuss the recent development and challenges in protein S-sulfenylation site prediction from the available datasets, algorithms and accessible services. We also demonstrate the encountered limitation and future perspective of the computational prediction tools. Conclusion: The development of S-sulfenylation site prediction and their application is an emerging field of protein bioinformatics research. Accurate predictors are expected to identify general and species-specific S-sulfenylation sites when more experimental annotation data are available. Combining experimental and computational technologies will definitely accelerate an understanding of protein S-sulfenylation, discovering regulatory networks in living organisms.

DOI： 10.2174/0929866525666180905110619

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Modeling and simulation of the redox regulation of the metabolism in Escherichia coli at different oxygen concentrations Reviewed International journal

Matsuoka Y., Kurata H.

Biotechnology for Biofuels 10 ( 1 ) 183 2017.08

Mathematical comparison of memory functions between mutual activation and repression networks in a stochastic environment Reviewed International journal

Hasan A., Kurata H.

Journal of Theoretical Biology 427 28 - 40 2017.08

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

© 2017 Elsevier LtdBiological memory is a ubiquitous function that can generate a sustained response to a transient inductive stimulus. To better understand this function, we must consider the mechanisms by which different structures of genetic networks achieve memory. Here, we investigated two competitive gene regulatory network models: the regulated mutual activation network (MAN) and the regulated mutual repression network (MRN). Stochasticity deteriorated the persistence of memory of both the MAN and the MRN. Mathematical comparison by simulation and theoretical analysis identified functional differences in the stochastic memory between the competitive models: specifically, the MAN provided much more robust, persistent memory than the MRN. The stochastic persistent memory pattern of the MAN can be adjusted by changing the binding strength of the activators, whereas the MRN required highly cooperative and strong binding repressors for robust memory.

DOI： 10.1016/j.jtbi.2017.05.036

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Robustness analysis of the detailed kinetic model of an ErbB signaling network by using dynamic sensitivity Reviewed International journal

Masunaga H., Sugimoto Y., Magi S., Itasaki R., Okada-Hatakeyama M., Kurata H.

PLoS ONE 12 ( 5 ) 2017.05

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

© 2017 Masunaga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The ErbB receptor signaling pathway plays an important role in the regulation of cellular proliferation, survival and differentiation, and dysregulation of the pathway is linked to various types of human cancer. Mathematical models have been developed as a practical complementary approach to deciphering the complexity of ErbB receptor signaling and elucidating how the pathways discriminate between ligands to induce different cell fates. In this study, we developed a simulator to accurately calculate the dynamic sensitivity of extracellular-signal-regulated kinase (ERK) activity (ERK∗) and Akt activity (Akt∗), downstream of the ErbB receptors stimulated with epidermal growth factor (EGF) and heregulin (HRG). To demonstrate the feasibility of this simulator, we estimated how the reactions critically responsible for ERK∗ and Akt∗ change with time and in response to different doses of EGF and HRG, and predicted that only a small number of reactions determine ERK∗ and Akt∗. ERK∗ increased steeply with increasing HRG dose until saturation, while showing a gently rising response to EGF. Akt∗ had a gradual wide-range response to HRG and a blunt response to EGF. Akt∗ was sensitive to perturbations of intracellular kinetics, while ERK∗ was more robust due to multiple, negative feedback loops. Overall, the simulator predicted reactions that were critically responsible for ERK∗ and Akt∗ in response to the dose of EGF and HRG, illustrated the response characteristics of ERK∗ and Akt∗, and estimated mechanisms for generating robustness in the ErbB signaling network.

DOI： 10.1371/journal.pone.0178250

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Computational identification of protein S-sulfenylation sites by incorporating the multiple sequence features information Reviewed International journal

Hasan M., Guo D., Kurata H.

Molecular BioSystems 13 ( 12 ) 2545 - 2550 2017.01

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

© 2017 The Royal Society of Chemistry. Cysteine S-sulfenylation is a major type of posttranslational modification that contributes to protein structure and function regulation in many cellular processes. Experimental identification of S-sulfenylation sites is challenging, due to the low abundance of proteins and the inefficient experimental methods. Computational identification of S-sulfenylation sites is an alternative strategy to annotate the S-sulfenylated proteome. In this study, a novel computational predictor SulCysSite was developed for accurate prediction of S-sulfenylation sites based on multiple sequence features, including amino acid index properties, binary amino acid codes, position specific scoring matrix, and compositions of profile-based amino acids. To learn the prediction model of SulCysSite, a random forest classifier was applied. The final SulCysSite achieved an AUC value of 0.819 in a 10-fold cross-validation test. It also exhibited higher performance than other existing computational predictors. In addition, the hidden and complex mechanisms were extracted from the predictive model of SulCysSite to investigate the understandable rules (i.e. feature combination) of S-sulfenylation sites. The SulCysSite is a useful computational resource for prediction of S-sulfenylation sites. The online interface and datasets are publicly available at http://kurata14.bio.kyutech.ac.jp/SulCysSite/.

DOI： 10.1039/c7mb00491e

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In vitro evaluation of a combination treatment involving anticancer agents and an aurora kinase B inhibitor Reviewed International journal

Sakai S., Izumi H., Yoshiura Y., Nakayama Y., Yamaguchi T., Harada Y., Koi C., Kurata H., Morimoto Y.

Oncology Letters 12 ( 5 ) 4263 - 4269 2016.11

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

© 2016, Spandidos Publications. All rights reserved. Aurora kinase B (AURKB) inhibitors are regarded as potential molecular-targeting drugs for cancer therapy. The present study evaluated the cytotoxic effect of a combination of AZD1152-hQPA, an AURKB inhibitor, and various anticancer agents on the HeLa human cervical cancer cell line, as well as its cisplatin-resistant equivalent HCP4 cell line. It was demonstrated that AZD1152-hQPA had an antagonistic effect on the cytotoxicity of cisplatin, etoposide and doxorubicin, but had a synergistic effect on that of all-trans-retinoic acid (ATRA), Am80 and TAC-101, when tested on HeLa cells. Cisplatin, etoposide and doxorubicin were shown to increase the cellular expression of AURKB, while ATRA, Am80 and TAC-101 downregulated its expression. These results suggested that AURKB expression is regulated by these anticancer agents at the transcriptional level, and that the level of expression of AURKB may influence the cytotoxic effect of AZD1152-hQPA. Therefore, when using anticancer agents, decreasing the expression of AURKB using a molecular-targeting drug may be an optimal therapeutic strategy.

DOI： 10.3892/ol.2016.5156

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