研究者詳細 - 倉田　博之

論文 - 倉田　博之

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NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning 査読有り

Hasan M.M., Alam M.A., Shoombuatong W., Deng H.W., Manavalan B., Kurata H.

Briefings in bioinformatics 22 ( 6 ) 2021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）

Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.

DOI： 10.1093/bib/bbab167

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108972351&origin=inward

LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec 査読有り国際誌

Tsukiyama S., Hasan M.M., Fujii S., Kurata H.

Briefings in bioinformatics 22 ( 6 ) 2021年11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）

Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding of viral coat proteins to host membrane receptors to the hijacking of host transcription machinery. However, few interspecies PPIs have been identified, because experimental methods including mass spectrometry are time-consuming and expensive, and molecular dynamic simulation is limited only to the proteins whose 3D structures are solved. Sequence-based machine learning methods are expected to overcome these problems. We have first developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. The LSTM-PHV effectively learnt the training data with a highly imbalanced ratio of positive to negative samples and achieved AUCs of 0.976 and 0.973 and accuracies of 0.984 and 0.985 on the training and independent datasets, respectively. In predicting PPIs between human and unknown or new virus, the LSTM-PHV learned greatly outperformed the existing state-of-the-art PPI predictors. Interestingly, learning of only sequence contexts as words is sufficient for PPI prediction. Use of uniform manifold approximation and projection demonstrated that the LSTM-PHV clearly distinguished the positive PPI samples from the negative ones. We presented the LSTM-PHV online web server and support data that are freely available at http://kurata35.bio.kyutech.ac.jp/LSTM-PHV.

DOI： 10.1093/bib/bbab228

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85121953622&origin=inward

Critical evaluation of web-based DNA N6-methyladenine site prediction tools 招待有り査読有り

Hasan M.M., Shoombuatong W., Kurata H., Manavalan B.

Briefings in Functional Genomics 20 ( 4 ) 258 - 272 2021年07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）

Methylation of DNA N6-methyladenosine (6mA) is a type of epigenetic modification that plays pivotal roles in various biological processes. The accurate genome-wide identification of 6mA is a challenging task that leads to understanding the biological functions. For the last 5 years, a number of bioinformatics approaches and tools for 6mA site prediction have been established, and some of them are easily accessible as web application. Nevertheless, the accurate genome-wide identification of 6mA is still one of the challenging works that lead to understanding the biological functions. Especially in practical applications, these tools have implemented diverse encoding schemes, machine learning algorithms and feature selection methods, whereas few systematic performance comparisons of 6mA site predictors have been reported. In this review, 11 publicly available 6mA predictors evaluated with seven different species-specific datasets (Arabidopsis thaliana, Tolypocladium, Diospyros lotus, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans and Escherichia coli). Of those, few species are close homologs, and the remaining datasets are distant sequences. Our independent, validation tests demonstrated that Meta-i6mA and MM-6mAPred models for A. thaliana, Tolypocladium, S. cerevisiae and D. melanogaster achieved excellent overall performance when compared with their counterparts. However, none of the existing methods were suitable for E. coli, C. elegans and D. lotus. A feasibility of the existing predictors is also discussed for the seven species. Our evaluation provides useful guidelines for the development of 6mA site predictors and helps biologists selecting suitable prediction tools.

DOI： 10.1093/bfgp/elaa028

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Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework 査読有り

Hasan M.M., Basith S., Khatun M.S., Lee G., Manavalan B., Kurata H.

Briefings in bioinformatics 22 ( 3 ) 2021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）

DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one of the challenging tasks in genome analysis, which leads to an understanding of their biological functions. To date, several species-specific machine learning (ML)-based models have been proposed, but majority of them did not test their model to other species. Hence, their practical application to other plant species is quite limited. In this study, we explored 10 different feature encoding schemes, with the goal of capturing key characteristics around 6mA sites. We selected five feature encoding schemes based on physicochemical and position-specific information that possesses high discriminative capability. The resultant feature sets were inputted to six commonly used ML methods (random forest, support vector machine, extremely randomized tree, logistic regression, naïve Bayes and AdaBoost). The Rosaceae genome was employed to train the above classifiers, which generated 30 baseline models. To integrate their individual strength, Meta-i6mA was proposed that combined the baseline models using the meta-predictor approach. In extensive independent test, Meta-i6mA showed high Matthews correlation coefficient values of 0.918, 0.827 and 0.635 on Rosaceae, rice and Arabidopsis thaliana, respectively and outperformed the existing predictors. We anticipate that the Meta-i6mA can be applied across different plant species. Furthermore, we developed an online user-friendly web server, which is available at http://kurata14.bio.kyutech.ac.jp/Meta-i6mA/.

DOI： 10.1093/bib/bbaa202

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85107088405&origin=inward

Prednts: Improved and robust prediction of nitrotyrosine sites by integrating multiple sequence features 査読有り

Nilamyani A.N., Auliah F.N., Moni M.A., Shoombuatong W., Hasan M.M., Kurata H.

International Journal of Molecular Sciences 22 ( 5 ) 1 - 11 2021年03月

記述言語：英語掲載種別：研究論文（学術雑誌）

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyro-sine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

DOI： 10.3390/ijms22052704

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102048754&origin=inward

Pup-fuse: Prediction of protein pupylation sites by integrating multiple sequence representations 査読有り

Auliah F.N., Nilamyani A.N., Shoombuatong W., Alam M.A., Hasan M.M., Kurata H.

International Journal of Molecular Sciences 22 ( 4 ) 1 - 12 2021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）

Pupylation is a type of reversible post-translational modification of proteins, which plays a key role in the cellular function of microbial organisms. Several proteomics methods have been developed for the prediction and analysis of pupylated proteins and pupylation sites. However, the traditional experimental methods are laborious and time-consuming. Hence, computational algorithms are highly needed that can predict potential pupylation sites using sequence features. In this research, a new prediction model, PUP-Fuse, has been developed for pupylation site prediction by integrating multiple sequence representations. Meanwhile, we explored the five types of feature encoding approaches and three machine learning (ML) algorithms. In the final model, we integrated the successive ML scores using a linear regression model. The PUP-Fuse achieved a Mathew correlation value of 0.768 by a 10-fold cross-validation test. It also outperformed existing predictors in an independent test. The web server of the PUP-Fuse with curated datasets is freely available.

DOI： 10.3390/ijms22042120

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85100933219&origin=inward

iLBE for Computational Identification of Linear B-cell Epitopes by Integrating Sequence and Evolutionary Features 査読有り

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

Genomics, Proteomics and Bioinformatics 2021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）

Linear B-cell epitopes are critically important for immunological applications, such as vaccine design, immunodiagnostic test, and antibody production, as well as disease diagnosis and therapy. The accurate identification of linear B-cell epitopes remains challenging despite several decades of research. In this work, we have developed a novel predictor, Identification of B-Cell Epitope (iLBE), by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon-rank sum test. Then the random forest (RF) algorithm using the optimal consecutive feature vectors was applied to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. iLBE yielded an area under curve score of 0.809 on the training dataset and outperformed other prediction models on a comprehensive independent dataset. iLBE is a powerful computational tool to identify the linear B-cell epitopes and would help to develop penetrating diagnostic tests. A web application with curated datasets for iLBE is freely accessible at http://kurata14.bio.kyutech.ac.jp/iLBE/.

DOI： 10.1016/j.gpb.2019.04.004

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RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models 査読有り

Maeda Kazuhiro, Boogerd Fred C., Kurata Hiroyuki

IPSJ Transactions on Bioinformatics ( 一般社団法人情報処理学会 ) 14 ( 0 ) 30 - 35 2021年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

<p>Kinetic modeling is essential for understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in kinetic modeling. Although several software tools have been developed to address this issue, they are meant to be used by experts, and their lack of user-friendliness hampers their general usage by capable yet inexperienced scientists. One of the difficulties is the lack of graphical user interfaces (GUIs), which means that users must learn how to write scripts for parameter estimation. In this study, we present RCGAToolbox, a user-friendly parameter estimation software that provides real-coded genetic algorithms (RCGAs). The RCGAToolbox has two RCGAs: 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). To facilitate parameter estimation, RCGAToolbox offers straightforward access to powerful RCGAs, such as GUIs, an easy-to-use installer, and a comprehensive user guide. Moreover, the RCGAToolbox supports systems biology standards for better usability and interoperability. The RCGAToolbox is available at https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, along with the user guide and application examples. The RCGAToolbox runs on MATLAB (R2016a or later) in Windows, Linux, and Mac.</p>

DOI： 10.2197/ipsjtbio.14.30

その他リンク： https://ci.nii.ac.jp/naid/130008120842

RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models 査読有り

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

IPSJ Transactions on Bioinformatics 14 30 - 35 2021年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

Kinetic modeling is essential for understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in kinetic modeling. Although several software tools have been developed to address this issue, they are meant to be used by experts, and their lack of user-friendliness hampers their general usage by capable yet inexperienced scientists. One of the difficulties is the lack of graphical user interfaces (GUIs), which means that users must learn how to write scripts for parameter estimation. In this study, we present RCGAToolbox, a user-friendly parameter estimation software that provides real-coded genetic algorithms (RCGAs). The RCGAToolbox has two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and the real-coded ensemble crossover star with just generation gap (REXstar/JGG). To facilitate parameter estimation, RCGAToolbox offers straightforward access to powerful RCGAs, such as GUIs, an easy-to-use installer, and a comprehensive user guide. Moreover, the RCGAToolbox supports systems biology standards for better usability and interoperability. The RCGAToolbox is available at https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, along with the user guide and application examples. The RCGAToolbox runs on MATLAB (R2016a or later) in Windows, Linux, and Mac.

DOI： 10.2197/IPSJTBIO.14.30

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85122021287&origin=inward

Coupling protocol of interlocked feedback oscillators in circadian clocks 査読有り

Rashid M.M., Kurata H.

Journal of the Royal Society, Interface 17 ( 167 ) 2020年06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）

Circadian rhythms (approx. 24 h) show the robustness of key oscillatory features such as phase, period and amplitude against external and internal variations. The robustness of Drosophila circadian clocks can be generated by interlocked transcriptional-translational feedback loops, where two negative feedback loops are coupled through mutual activations. The mechanisms by which such coupling protocols have survived out of many possible protocols remain to be revealed. To address this question, we investigated two distinct coupling protocols: activator-coupled oscillators (ACO) and repressor-coupled oscillators (RCO). We focused on the two coupling parameters: coupling dissociation constant and coupling time-delay. Interestingly, the ACO was able to produce anti-phase or morning-evening cycles, whereas the RCO produced in-phase ones. Deterministic and stochastic analyses demonstrated that the anti-phase ACO provided greater fluctuations in amplitude not only with respect to changes in coupling parameters but also to random parameter perturbations than the in-phase RCO. Moreover, the ACO deteriorated the entrainability to the day-night master clock, whereas the RCO produced high entrainability. Considering that the real, interlocked feedback loops have evolved as the ACO, instead of the RCO, we first proposed a hypothesis that the morning-evening or anti-phase cycle is more essential for Drosophila than achieving robustness and entrainability.

DOI： 10.1098/rsif.2020.0287

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85085908915&origin=inward

Computer-Aided Rational Design of Efficient NADPH Production System by Escherichia coli pgi Mutant Using a Mixture of Glucose and Xylose 査読有り

Matsuoka Y., Kurata H.

Frontiers in Bioengineering and Biotechnology 8 2020年04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）

© Copyright © 2020 Matsuoka and Kurata. Lignocellulosic biomass can be hydrolyzed into two major sugars of glucose and xylose, and thus the strategy for the efficient consumption of both sugars is highly desirable. NADPH is the essential molecule for the production of industrially important value-added chemicals, and thus its availability is quite important. Escherichia coli mutant lacking the pgi gene encoding phosphoglucose isomerase (Pgi) has been preferentially used to overproduce the NADPH. However, there exists a disadvantage that the cell growth rate becomes low for the mutant grown on glucose. This limits the efficient NADPH production, and therefore, it is quite important to investigate how addition of different carbon source such as xylose (other than glucose) effectively improves the NADPH production. In this study, we have developed a kinetic model to propose an efficient NADPH production system using E. coli pgi-knockout mutant with a mixture of glucose and xylose. The proposed system adds xylose to glucose medium to recover the suppressed growth of the pgi mutant, and determines the xylose content to maximize the NADPH productivity. Finally, we have designed a mevalonate (MVA) production system by implementing ArcA overexpression into the pgi-knockout mutant using a mixture of glucose and xylose. In addition to NADPH overproduction, the accumulation of acetyl-CoA (AcCoA) is necessary for the efficient MVA production. In the present study, therefore, we considered to overexpress ArcA, where ArcA overexpression suppresses the TCA cycle, causing the overflow of AcCoA, a precursor of MVA. We predicted the xylose content that maximizes the MVA production. This approach demonstrates the possibility of a great progress in the computer-aided rational design of the microbial cell factories for useful metabolite production.

DOI： 10.3389/fbioe.2020.00277

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Recent development of machine learning methods in microbial phos-phorylation sites 査読有り

Rashid M.M., Shatabda S., Hasan M.M., Kurata H.

Current Genomics 21 ( 3 ) 194 - 203 2020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）

© 2020 Bentham Science Publishers. A variety of protein post-translational modifications has been identified that control many cellular functions. Phosphorylation studies in mycobacterial organisms have shown critical importance in diverse biological processes, such as intercellular communication and cell division. Recent technical advances in high-precision mass spectrometry have determined a large number of microbial phosphorylated proteins and phosphorylation sites throughout the proteome analysis. Identification of phosphorylated proteins with specific modified residues through experimentation is often labor-intensive, costly and time-consuming. All these limitations could be overcome through the application of machine learning (ML) approaches. However, only a limited number of computational phosphory-lation site prediction tools have been developed so far. This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation. We cover a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, win-dow size, and software utility. Initially, we review the currently available phosphorylation site data-bases of the microbiome, the state-of-the-art ML approaches, working principles, and their perfor-mances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.

DOI： 10.2174/1389202921666200427210833

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85087840233&origin=inward

A prediction model of functional outcome at 6 months using clinical findings of a person with traumatic spinal cord injury at 1 month after injury 査読有り

Ariji Y., Hayashi T., Ideta R., Koga R., Murai S., Towatari F., Terashi Y., Sakai H., Kurata H., Maeda T.

Spinal Cord 2020年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2020, The Author(s), under exclusive licence to International Spinal Cord Society. Study design: Retrospective statistical analysis of database. Objectives: Prediction of the Spinal Cord Independence Measure version III Total Score (SCIM-TS) at 6 months after injury based on physical findings at 1 month after injury is an important index for rehabilitation approach in the recovery phase. Setting: Spinal Injuries Center, Fukuoka, Japan. Methods: The study participants were selected from patients with traumatic spinal cord injuries who were registered in the Japan Single Center Study for Spinal Cord Injury Data Base (JSSCI-DB) of the Japan Spinal Injuries Center specializing in spine and spinal cord injuries. Of the 534 participants registered with the JSSCI-DB between January 2012 and October 2018, we retrospectively extracted 137 participants for 6 months after injury, and these participants were included in this study. Results: According to multiple regression analysis, SCIM-TS at 6 months after injury could be predicted based on only six variables, i.e., age at injury, three key muscles (C6 wrist extensors, C8 finger flexors, and L3 knee extensors), and two mobility assessments (WISCI and SCIM−item13) (Adjusted R-Squared: 0.83). These six independent variables were significant factors reflecting SCIM-TS at 6 months. Conclusions: In rehabilitation after traumatic spinal cord injuries, a simple and reliable prognostic model can help accurately predict the achievable activity of daily living competency to set a goal. In addition, if the procedure is simple, evaluation can be completed in a short period of time, and the physical burden on both treating staff and patients can be reduced.

DOI： 10.1038/s41393-020-0488-5

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i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes 査読有り

Hasan M.M., Manavalan B., Shoombuatong W., Khatun M.S., Kurata H.

Computational and Structural Biotechnology Journal 18 906 - 912 2020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）

© 2020 The Authors N4-methylcytosine (4mC) is one of the most important DNA modifications and involved in regulating cell differentiations and gene expressions. The accurate identification of 4mC sites is necessary to understand various biological functions. In this work, we developed a new computational predictor called i4mC-Mouse to identify 4mC sites in the mouse genome. Herein, six encoding schemes of k-space nucleotide composition (KSNC), k-mer nucleotide composition (Kmer), mono nucleotide binary encoding (MBE), dinucleotide binary encoding, electron–ion interaction pseudo potentials (EIIP) and dinucleotide physicochemical composition were explored that cover different characteristics of DNA sequence information. Subsequently, we built six RF-based encoding models and then linearly combined their probability scores to construct the final predictor. Among the six RF-based models, the Kmer, KSNC, MBE, and EIIP encodings are sufficient, which contributed to 10%, 45%, 25%, and 20% of the prediction performance, respectively. On the independent test the i4mC-Mouse predicted the 4mC sites with accuracy and MCC of 0.816 and 0.633, respectively, which were approximately 2.5% and 5% higher than those of the existing method (4mCpred-EL). For experimental biologists, a freely available web application was implemented at http://kurata14.bio.kyutech.ac.jp/i4mC-Mouse/.

DOI： 10.1016/j.csbj.2020.04.001

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85083319627&origin=inward

ヒト全身代謝システムの数理モデル招待有り査読有り

倉田博之

細胞 52 376 - 379 2020年01月

i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation 査読有り

Hasan M.M., Manavalan B., Shoombuatong W., Khatun M.S., Kurata H.

Plant Molecular Biology 2020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）

© 2020, Springer Nature B.V. DNA N6-methyladenine (6 mA) is one of the most vital epigenetic modifications and involved in controlling the various gene expression levels. With the avalanche of DNA sequences generated in numerous databases, the accurate identification of 6 mA plays an essential role for understanding molecular mechanisms. Because the experimental approaches are time-consuming and costly, it is desirable to develop a computation model for rapidly and accurately identifying 6 mA. To the best of our knowledge, we first proposed a computational model named i6mA-Fuse to predict 6 mA sites from the Rosaceae genomes, especially in Rosa chinensis and Fragaria vesca. We implemented the five encoding schemes, i.e., mononucleotide binary, dinucleotide binary, k-space spectral nucleotide, k-mer, and electron–ion interaction pseudo potential compositions, to build the five, single-encoding random forest (RF) models. The i6mA-Fuse uses a linear regression model to combine the predicted probability scores of the five, single encoding-based RF models. The resultant species-specific i6mA-Fuse achieved remarkably high performances with AUCs of 0.982 and 0.978 and with MCCs of 0.869 and 0.858 on the independent datasets of Rosa chinensis and Fragaria vesca, respectively. In the F. vesca-specific i6mA-Fuse, the MBE and EIIP contributed to 75% and 25% of the total prediction; in the R. chinensis-specific i6mA-Fuse, Kmer, MBE, and EIIP contribute to 15%, 65%, and 20% of the total prediction. To assist high-throughput prediction for DNA 6 mA identification, the i6mA-Fuse is publicly accessible at https://kurata14.bio.kyutech.ac.jp/i6mA-Fuse/.

DOI： 10.1007/s11103-020-00988-y

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85080933360&origin=inward

Evolution of sequence-based bioinformatics tools for protein-protein interaction prediction 査読有り

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

Current Genomics 21 ( 6 ) 454 - 463 2020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）

© 2020 Bentham Science Publishers. Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

DOI： 10.2174/1389202921999200625103936

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85089875758&origin=inward

ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations 査読有り

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

Journal of Computer-Aided Molecular Design 2020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）

© 2020, Springer Nature Switzerland AG. A proinflammatory peptide (PIP) is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate the probabilistic scores by using the random forest models employing eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by linearly combining the resultant eight probabilistic scores. Evaluated through independent test, the ProIn-Fuse yielded an accuracy of 0.746, which was 10% higher than those obtained by the state-of-the-art PIP predictors. The proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse.

DOI： 10.1007/s10822-020-00343-9

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Improvement of the memory function of a mutual repression network in a stochastic environment by negative autoregulation 査読有り国際誌

Hasan A., Kurata H., Pechmann S.

BMC Bioinformatics 20 ( 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). Background: Cellular memory is a ubiquitous function of biological systems. By generating a sustained response to a transient inductive stimulus, often due to bistability, memory is central to the robust control of many important biological processes. However, our understanding of the origins of cellular memory remains incomplete. Stochastic fluctuations that are inherent to most biological systems have been shown to hamper memory function. Yet, how stochasticity changes the behavior of genetic circuits is generally not clear from a deterministic analysis of the network alone. Here, we apply deterministic rate equations, stochastic simulations, and theoretical analyses of Fokker-Planck equations to investigate how intrinsic noise affects the memory function in a mutual repression network. Results: We find that the addition of negative autoregulation improves the persistence of memory in a small gene regulatory network by reducing stochastic fluctuations. Our theoretical analyses reveal that this improved memory function stems from an increased stability of the steady states of the system. Moreover, we show how the tuning of critical network parameters can further enhance memory. Conclusions: Our work illuminates the power of stochastic and theoretical approaches to understanding biological circuits, and the importance of considering stochasticity when designing synthetic circuits with memory function.

DOI： 10.1186/s12859-019-3315-2

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Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information 査読有り国際誌

Hasan M., Rashid M., Khatun M., 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). Protein phosphorylation on serine (S) and threonine (T) has emerged as a key device in the control of many biological processes. Recently phosphorylation in microbial organisms has attracted much attention for its critical roles in various cellular processes such as cell growth and cell division. Here a novel machine learning predictor, MPSite (Microbial Phosphorylation Site predictor), was developed to identify microbial phosphorylation sites using the enhanced characteristics of sequence features. The final feature vectors optimized via a Wilcoxon rank sum test. A random forest classifier was then trained using the optimum features to build the predictor. Benchmarking investigation using the 5-fold cross-validation and independent datasets test showed that the MPSite is able to achieve robust performance on the S- and T-phosphorylation site prediction. It also outperformed other existing methods on the comprehensive independent datasets. We anticipate that the MPSite is a powerful tool for proteome-wide prediction of microbial phosphorylation sites and facilitates hypothesis-driven functional interrogation of phosphorylation proteins. A web application with the curated datasets is freely available at http://kurata14.bio.kyutech.ac.jp/MPSite/.

DOI： 10.1038/s41598-019-44548-x

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066627531&origin=inward

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