研究者詳細 - 倉田　博之

論文 - 倉田　博之

分割表示252 件中 41 - 60 件目／全件表示 >>

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network 査読有り国際誌

Maeda K., Westerhoff H., Kurata H., Boogerd F.

npj Systems Biology and Applications 5 ( 1 ) 2019年12月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2019, The Author(s). The complex ammonium transport and assimilation network of E. coli involves the ammonium transporter AmtB, the regulatory proteins GlnK and GlnB, and the central N-assimilating enzymes together with their highly complex interactions. The engineering and modelling of such a complex network seem impossible because functioning depends critically on a gamut of data known at patchy accuracy. We developed a way out of this predicament, which employs: (i) a constrained optimization-based technology for the simultaneous fitting of models to heterogeneous experimental data sets gathered through diverse experimental set-ups, (ii) a ‘rubber band method’ to deal with different degrees of uncertainty, both in experimentally determined or estimated parameter values and in measured transient or steady-state variables (training data sets), (iii) integration of human expertise to decide on accuracies of both parameters and variables, (iv) massive computation employing a fast algorithm and a supercomputer, (v) an objective way of quantifying the plausibility of models, which makes it possible to decide which model is the best and how much better that model is than the others. We applied the new technology to the ammonium transport and assimilation network, integrating recent and older data of various accuracies, from different expert laboratories. The kinetic model objectively ranked best, has E. coli's AmtB as an active transporter of ammonia to be assimilated with GlnK minimizing the futile cycling that is an inevitable consequence of intracellular ammonium accumulation. It is 130 times better than a model with facilitated passive transport of ammonia.

DOI： 10.1038/s41540-019-0091-6

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Self-replenishment cycles generate a threshold response 査読有り国際誌

Kurata H.

Scientific Reports 9 ( 1 ) 2019年12月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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 査読有り国際誌

Khatun S., Hasan M., Kurata H.

FEBS Letters 593 ( 21 ) 3029 - 3039 2019年11月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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 招待有り査読有り国際誌

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

Cells 8 ( 2 ) 2019年01月

担当区分：筆頭著者,　最終著者,　責任著者記述言語：英語掲載種別：記事・総説・解説・論説等（学術雑誌）

Lysine succinylation is a form of posttranslational modification of the proteins that play an essential functional role in every aspect of cell metabolism in both prokaryotes and eukaryotes. Aside from experimental identification of succinylation sites, there has been an intense effort geared towards the development of sequence-based prediction through machine learning, due to its promising and essential properties of being highly accurate, robust and cost-effective. In spite of these advantages, there are several problems that are in need of attention in the design and development of succinylation site predictors. Notwithstanding of many studies on the employment of machine learning approaches, few articles have examined this bioinformatics field in a systematic manner. Thus, we review the advancements regarding the current state-of-the-art prediction models, datasets, and online resources and illustrate the challenges and limitations to present a useful guideline for developing powerful succinylation site prediction tools.

DOI： 10.3390/cells8020095

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PreAIP: Computational prediction of anti-inflammatory peptides by integrating multiple complementary features 査読有り国際誌

Khatun M., Hasan M., Kurata H.

Frontiers in Genetics 10 ( MAR ) 2019年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066147396&origin=inward

i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome 査読有り国際誌

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

International Journal of Biological Macromolecules 2019年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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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Prediction of: S -nitrosylation sites by integrating support vector machines and random forest 査読有り国際誌

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

Molecular Omics 15 ( 6 ) 451 - 458 2019年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85075958587&origin=inward

SIPMA: A systematic identification of protein-protein interactions in Zea mays using autocorrelation features in a machine-learning framework 査読有り国際誌

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

Proceedings - 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering, BIBE 2018 122 - 125 2018年12月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 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 査読有り国際誌

Hasan M., Kurata H.

Proceedings - 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering, BIBE 2018 356 - 359 2018年12月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 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 査読有り国際誌

Hasan M., Kurata H.

PLoS ONE 13 ( 10 ) 2018年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85050480229&origin=inward

Computational modeling of lysine post-translational modification: an overview. 招待有り査読有り国際誌

Md. Mehedi Hasan, Mst. Shamima Khatun, Hiroyuki Kurata

Curr Synthetic Sys Biol 6 1 2018年04月

A novel isolation method for cancer prognostic factors via the p53 pathway by a combination of in vitro and in silico analyses 査読有り国際誌

Yohei Kamijo, Kohichi Kawahara, Takuma Yoshinaga, Hiroyuki Kurata, Kazunari Arima, Tatsuhiko Furukawa

Oncoscience ( Impact Journals ) 5 88 - 98 2018年04月

Long negative feedback loop enhances period tunability of biological oscillators 査読有り

Maeda K., Kurata H.

Journal of Theoretical Biology 440 21 - 31 2018年03月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85038957457&origin=inward

Improved kinetic model of Escherichia coli central carbon metabolism in batch and continuous cultures 査読有り国際誌

Kurata H., Sugimoto Y.

Journal of Bioscience and Bioengineering 125 ( 2 ) 251 - 257 2018年02月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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 査読有り国際誌

Maeda Kazuhiro, Boogerd Fred C., Kurata Hiroyuki

IPSJ Transactions on Bioinformatics ( 一般社団法人情報処理学会 ) 11 ( 0 ) 31 - 40 2018年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

<p>Kinetic modeling is a powerful tool to understand how a biochemical system behaves as a whole. To develop a realistic and predictive model, kinetic parameters need to be estimated so that a model fits experimental data. However, parameter estimation remains a major bottleneck in kinetic modeling. To accelerate parameter estimation, we developed a C library for real-coded genetic algorithms (libRCGA). In libRCGA, two real-coded genetic algorithms (RCGAs), viz. the Unimodal Normal Distribution Crossover with Minimal Generation Gap (UNDX/MGG) and the Real-coded Ensemble Crossover star with Just Generation Gap (REX<sup> star</sup>/JGG), are implemented in C language and paralleled by Message Passing Interface (MPI). We designed libRCGA to take advantage of high-performance computing environments and thus to significantly accelerate parameter estimation. Constrained optimization formulation is useful to construct a realistic kinetic model that satisfies several biological constraints. libRCGA employs stochastic ranking to efficiently solve constrained optimization problems. In the present paper, we demonstrate the performance of libRCGA through benchmark problems and in realistic parameter estimation problems. libRCGA is freely available for academic usage at http://kurata21.bio.kyutech.ac.jp/maeda/index.html.</p>

DOI： 10.2197/ipsjtbio.11.31

その他リンク： https://ci.nii.ac.jp/naid/130007483197

A comprehensive review of in silico analysis for protein s-sulfenylation sites 招待有り査読有り国際誌

Hasan M., Khatun M., Kurata H.

Protein and Peptide Letters 25 ( 9 ) 815 - 821 2018年01月

担当区分：責任著者記述言語：英語掲載種別：記事・総説・解説・論説等（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85059495650&origin=inward

Modeling and simulation of the redox regualation of the metabolism in Escherichia coli at different oxygen concentrations 査読有り国際誌

Matsuoka Y. Kurata H.

Biotechnology for Biofules 10 ( 1 ) 183 2017年08月

担当区分：筆頭著者,　最終著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Background: Microbial production of biofuels and biochemicals from renewable feedstocks has received considerable recent attention from environmental protection and energy production perspectives. Many biofuels and biochemicals are produced by fermentation under oxygen-limited conditions following initiation of aerobic cultivation to enhance the cell growth rate. Thus, it is of significant interest to investigate the effect of dissolved oxygen concentration on redox regulation in Escherichia coli, a particularly popular cellular factory due to its high growth rate and well-characterized physiology. For this, the systems biology approach such as modeling is powerful for the analysis of the metabolism and for the design of microbial cellular factories. Results: Here, we developed a kinetic model that describes the dynamics of fermentation by taking into account transcription factors such as ArcA/B and Fnr, respiratory chain reactions and fermentative pathways, and catabolite regulation. The hallmark of the kinetic model is its ability to predict the dynamics of metabolism at different dissolved oxygen levels and facilitate the rational design of cultivation methods. The kinetic model was verified based on the experimental data for a wild-type E. coli strain. The model reasonably predicted the metabolic characteristics and molecular mechanisms of fnr and arcA gene-knockout mutants. Moreover, an aerobic-microaerobic dual-phase cultivation method for lactate production in a pfl-knockout mutant exhibited promising yield and productivity. Conclusions: It is quite important to understand metabolic regulation mechanisms from both scientific and engineering points of view. In particular, redox regulation in response to oxygen limitation is critically important in the practical production of biofuel and biochemical compounds. The developed model can thus be used as a platform for designing microbial factories to produce a variety of biofuels and biochemicals.

DOI： 10.1186/s13068-017-0867-0

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85025106733&origin=inward

Mathematical comparison of memory functions between mutual activation and repression networks in a stochastic environment 査読有り国際誌

Hasan A., Kurata H.

Journal of Theoretical Biology 427 28 - 40 2017年08月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85020256737&origin=inward

Robustness analysis of the detailed kinetic model of an ErbB signaling network by using dynamic sensitivity 査読有り国際誌

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

PLoS ONE 12 ( 5 ) 2017年05月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85019916756&origin=inward

Computational identification of protein S-sulfenylation sites by incorporating the multiple sequence features information 査読有り国際誌

Hasan M., Guo D., Kurata H.

Molecular BioSystems 13 ( 12 ) 2545 - 2550 2017年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85035040972&origin=inward

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