研究者詳細 - 倉田　博之

論文 - 倉田　博之

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A genetic algorithm-based ensemble model for efficiently identifying interleukin 6 inducing peptides 査読有り国際誌

Harun-Or-Roshid M., Kurata H.

Scientific Reports 15 ( 1 ) 2025年12月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Interleukin-6 (IL-6) is a cytokine with diverse biological activities that contribute to a variety of physiologic and immune responses. IL-6–inducing peptides are the short protein fragments that are critical for playing a contributing role in biological processes. Extensive research has advanced the development of IL-6–inducing peptides, but identifying these peptides experimentally remains time-consuming, labor-intensive, and costly. Therefore, computational prediction has gained attention as an alternative method. Meanwhile, some computational methods have already been developed, but they suffer from insufficient accuracy and inadequate feature engineering. In this study, we developed PredIL6, an advanced ensemble learning model that precisely identifies IL-6–inducing peptides by combining probability scores from 148 baseline machine learning and deep learning models, using a genetic algorithm–based meta-classifier. A forward feature selection method was used to construct the ensemble model, which consists of 20 baseline or single-feature models, including AAINDEX, BLOSUM62, and language models (ESM-2 and word2vec). PredIL6 outperformed existing state-of-the-art methods, achieving accuracy values of 0.934 and 0.899 on the training and test sets, respectively. Thus, PredIL6 is a powerful tool for expediting the identification of IL-6–inducing peptides. A freely available web application and a standalone PredIL6 program are provided.

DOI： 10.1038/s41598-025-05491-2

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105009714401&origin=inward

PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides 査読有り国際誌

Kurata H., Harun-Or-Roshid M., Tsukiyama S., Maeda K.

Plos One 19 ( 8 ) 2024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）

Interleukin (IL)-13 has emerged as one of the recently identified cytokine. Since IL-13 causes the severity of COVID-19 and alters crucial biological processes, it is urgent to explore novel molecules or peptides capable of including IL-13. Computational prediction has received attention as a complementary method to in-vivo and in-vitro experimental identification of IL-13 inducing peptides, because experimental identification is time-consuming, laborious, and expensive. A few computational tools have been presented, including the IL13Pred and iIL13Pred. To increase prediction capability, we have developed PredIL13, a cutting-edge ensemble learning method with the latest ESM-2 protein language model. This method stacked the probability scores outputted by 168 single-feature machine/deep learning models, and then trained a logistic regression-based meta-classifier with the stacked probability score vectors. The key technology was to implement ESM-2 and to select the optimal single-feature models according to their absolute weight coefficient for logistic regression (AWCLR), an indicator of the importance of each single-feature model. Especially, the sequential deletion of single-feature models based on the iterative AWCLR ranking (SDIWC) method constructed the meta-classifier consisting of the top 16 single-feature models, named PredIL13, while considering the model’s accuracy. The PredIL13 greatly outperformed the-state-of-the-art predictors, thus is an invaluable tool for accelerating the detection of IL13-inducing peptide within the human genome.

DOI： 10.1371/journal.pone.0309078

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85202002186&origin=inward

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models 査読有り国際誌

Kurata H., Harun-Or-Roshid M., Mehedi Hasan M., Tsukiyama S., Maeda K., Manavalan B.

Methods 227 37 - 47 2024年07月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

DOI： 10.1016/j.ymeth.2024.05.004

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85192860237&origin=inward

Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 20-O-methylation sites in human RNA 査読有り国際誌

Harun-Or-Roshid M., Pham N.T., Manavalan B., Kurata H.

Plos One 19 ( 6 June ) 2024年06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）

2<sup>0</sup>-O-methylation (2-OM or Nm) is a widespread RNA modification observed in various RNA types like tRNA, mRNA, rRNA, miRNA, piRNA, and snRNA, which plays a crucial role in several biological functional mechanisms and innate immunity. To comprehend its modification mechanisms and potential epigenetic regulation, it is necessary to accurately identify 2-OM sites. However, biological experiments can be tedious, time-consuming, and expensive. Furthermore, currently available computational methods face challenges due to inadequate datasets and limited classification capabilities. To address these challenges, we proposed Meta-2OM, a cutting-edge predictor that can accurately identify 2-OM sites in human RNA. In brief, we applied a meta-learning approach that considered eight conventional machine learning algorithms, including tree-based classifiers and decision boundary-based classifiers, and eighteen different feature encoding algorithms that cover physicochemical, compositional, position-specific and natural language processing information. The predicted probabilities of 2-OM sites from the baseline models are then combined and trained using logistic regression to generate the final prediction. Consequently, Meta-2OM achieved excellent performance in both 5-fold cross-validation training and independent testing, outperforming all existing state-of-the-art methods. Specifically, on the independent test set, Meta-2OM achieved an overall accuracy of 0.870, sensitivity of 0.836, specificity of 0.904, and Matthew’s correlation coefficient of 0.743. To facilitate its use, a user-friendly web server and standalone program have been developed and freely available at http://kurata35.bio.kyutech.ac.jp/Meta-2OM and https://github.com/kuratahiroyuki/Meta-2OM.

DOI： 10.1371/journal.pone.0305406

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85197180424&origin=inward

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach 査読有り国際誌

Harun-Or-Roshid M., Maeda K., Phan L.T., Manavalan B., Kurata H.

Computers in Biology and Medicine 169 2024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）

Dihydrouridine (DHU, D) is one of the most abundant post-transcriptional uridine modifications found in tRNA, mRNA, and snoRNA, closely associated with disease pathogenesis and various biological processes in eukaryotes. Identifying D sites is important for understanding the modification mechanisms and/or epigenetic regulation. However, biological experiments for detecting D sites are time-consuming and expensive. Given these challenges, computational methods have been developed for accurately identifying the D sites in genome-wide datasets. However, existing methods have some limitations, and their prediction performance needs to be improved. In this work, we have developed a new computational predictor for accurately identifying D sites called Stack-DHUpred. Briefly, we trained 66 baseline models or single-feature models by connecting six machine learning classifiers with eleven different feature encoding methods and stacked different baseline models to build stacked ensemble learning models. Subsequently, the optimal combination of the baseline models was identified for the construction of the final stacked model. Remarkably, the Stack-DHUpred outperformed the existing predictors on our new independent dataset, indicating that the stacking approach significantly improved the prediction performance. We have made Stack-DHUpred available to the public through a web server (http://kurata35.bio.kyutech.ac.jp/Stack-DHUpred) and a standalone program (https://github.com/kuratahiroyuki/Stack-DHUpred). We believe that Stack-DHUpred will be a valuable tool for accelerating the discovery of D modifications and understanding their role in post-transcriptional regulation.

DOI： 10.1016/j.compbiomed.2023.107848

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181135204&origin=inward

Elucidating Key Characteristics of PFAS Binding to Human Peroxisome Proliferator-Activated Receptor Alpha: An Explainable Machine Learning Approach 査読有り国際誌

Maeda K., Hirano M., Hayashi T., Iida M., Kurata H., Ishibashi H.

Environmental Science and Technology 58 ( 1 ) 488 - 497 2024年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

Per- and polyfluoroalkyl substances (PFAS) are widely employed anthropogenic fluorinated chemicals known to disrupt hepatic lipid metabolism by binding to human peroxisome proliferator-activated receptor alpha (PPARα). Therefore, screening for PFAS that bind to PPARα is of critical importance. Machine learning approaches are promising techniques for rapid screening of PFAS. However, traditional machine learning approaches lack interpretability, posing challenges in investigating the relationship between molecular descriptors and PPARα binding. In this study, we aimed to develop a novel, explainable machine learning approach to rapidly screen for PFAS that bind to PPARα. We calculated the PPARα-PFAS binding score and 206 molecular descriptors for PFAS. Through systematic and objective selection of important molecular descriptors, we developed a machine learning model with good predictive performance using only three descriptors. The molecular size (b_single) and electrostatic properties (BCUT_PEOE_3 and PEOE_VSA_PPOS) are important for PPARα-PFAS binding. Alternative PFAS are considered safer than their legacy predecessors. However, we found that alternative PFAS with many carbon atoms and ether groups exhibited a higher affinity for PPARα. Therefore, confirming the toxicity of these alternative PFAS compounds with such characteristics through biological experiments is important.

DOI： 10.1021/acs.est.3c06561

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181562547&origin=inward

CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site prediction 査読有り国際誌

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

Computational and Structural Biotechnology Journal 21 644 - 654 2023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：研究論文（学術雑誌）

N6-methyladenine (6mA) plays a critical role in various epigenetic processing including DNA replication, DNA repair, silencing, transcription, and diseases such as cancer. To understand such epigenetic mechanisms, 6 mA has been detected by high-throughput technologies on a genome-wide scale at single-base resolution, together with conventional methods such as immunoprecipitation, mass spectrometry and capillary electrophoresis, but these experimental approaches are time-consuming and laborious. To complement these problems, we have developed a CNN-based 6 mA site predictor, named CNN6mA, which proposed two new architectures: a position-specific 1-D convolutional layer and a cross-interactive network. In the position-specific 1-D convolutional layer, position-specific filters with different window sizes were applied to an inquiry sequence instead of sharing the same filters over all positions in order to extract the position-specific features at different levels. The cross-interactive network explored the relationships between all the nucleotide patterns within the inquiry sequence. Consequently, CNN6mA outperformed the existing state-of-the-art models in many species and created the contribution score vector that intelligibly interpret the prediction mechanism. The source codes and web application in CNN6mA are freely accessible at https://github.com/kuratahiroyuki/CNN6mA.git and http://kurata35.bio.kyutech.ac.jp/CNN6mA/, respectively.

DOI： 10.1016/j.csbj.2022.12.043

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85145978830&origin=inward

MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling 査読有り国際誌

Maeda K., Hatae A., Sakai Y., Boogerd F.C., Kurata H.

BMC Bioinformatics 23 ( 1 ) 2022年12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: Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (K<inf>m</inf>), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem). Results: To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for K<inf>m</inf> estimation of kinetic modeling. First, we use a machine learning-based K<inf>m</inf> predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted K<inf>m</inf> values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R<sup>2</sup> = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping K<inf>m</inf> values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated K<inf>m</inf> values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated K<inf>m</inf> values, which were close to the measured values. Conclusions: MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based K<inf>m</inf> predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps, which helps modelers perform MLAGO on their own parameter estimation tasks.

DOI： 10.1186/s12859-022-05009-x

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85141099893&origin=inward

ICAN: Interpretable cross-attention network for identifying drug and target protein interactions 査読有り国際誌

Kurata H., Tsukiyama S.

Plos One 17 ( 10 October ) 2022年10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）

Drug–target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. However, improvements are needed for enhancing prediction performance and DTI mechanism elucidation. To address these problems, we developed an attention-based method designated the interpretable cross-attention network (ICAN), which predicts DTIs using the Simplified Molecular Input Line Entry System of drugs and amino acid sequences of target proteins. We optimized the attention mechanism architecture by exploring the cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention mechanism. We found that a plain attention mechanism that decodes drug-related protein context features without any protein-related drug context features effectively achieved high performance. The ICAN outperformed state-of-the-art methods in several metrics on the DAVIS dataset and first revealed with statistical significance that some weighted sites in the cross-attention weight matrix represent experimental binding sites, thus demonstrating the high interpretability of the results. The program is freely available at https://github.com/kuratahiroyuki/ICAN.

DOI： 10.1371/journal.pone.0276609

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85140658867&origin=inward

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy 査読有り国際誌

Hasan M.M., Tsukiyama S., Cho J.Y., Kurata H., Alam M.A., Liu X., Manavalan B., Deng H.W.

Molecular Therapy 30 ( 8 ) 2856 - 2867 2022年08月

記述言語：英語掲載種別：研究論文（学術雑誌）

As one of the most prevalent post-transcriptional epigenetic modifications, N5-methylcytosine (m5C) plays an essential role in various cellular processes and disease pathogenesis. Therefore, it is important accurately identify m5C modifications in order to gain a deeper understanding of cellular processes and other possible functional mechanisms. Although a few computational methods have been proposed, their respective models have been developed using small training datasets. Hence, their practical application is quite limited in genome-wide detection. To overcome the existing limitations, we propose Deepm5C, a bioinformatics method for identifying RNA m5C sites throughout the human genome. To develop Deepm5C, we constructed a novel benchmarking dataset and investigated a mixture of three conventional feature-encoding algorithms and a feature derived from word-embedding approaches. Afterward, four variants of deep-learning classifiers and four commonly used conventional classifiers were employed and trained with the four encodings, ultimately obtaining 32 baseline models. A stacking strategy is effectively utilized by integrating the predicted output of the optimal baseline models and trained with a one-dimensional (1D) convolutional neural network. As a result, the Deepm5C predictor achieved excellent performance during cross-validation with a Matthews correlation coefficient and an accuracy of 0.697 and 0.855, respectively. The corresponding metrics during the independent test were 0.691 and 0.852, respectively. Overall, Deepm5C achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, Deepm5C is expected to assist community-wide efforts in identifying putative m5Cs and to formulate the novel testable biological hypothesis.

DOI： 10.1016/j.ymthe.2022.05.001

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85130448494&origin=inward

IACVP: Markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model 査読有り国際誌

Kurata H., Tsukiyama S., Manavalan B.

Briefings in Bioinformatics 23 ( 4 ) 2022年07月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-The-Art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP.

DOI： 10.1093/bib/bbac265

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85134721597&origin=inward

BERT6mA: Prediction of DNA N6-methyladenine site using deep learning-based approaches 査読有り国際誌

Tsukiyama S., Hasan M.M., Deng H.W., Kurata H.

Briefings in Bioinformatics 23 ( 2 ) 2022年03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）

N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are costly and time-consuming. To detect the 6mA and complement these shortcomings of experimental methods, we proposed a novel, deep leaning approach called BERT6mA. To compare the BERT6mA with other deep learning approaches, we used the benchmark datasets including 11 species. The BERT6mA presented the highest AUCs in eight species in independent tests. Furthermore, BERT6mA showed higher and comparable performance with the state-of-the-art models while the BERT6mA showed poor performances in a few species with a small sample size. To overcome this issue, pretraining and fine-tuning between two species were applied to the BERT6mA. The pretrained and fine-tuned models on specific species presented higher performances than other models even for the species with a small sample size. In addition to the prediction, we analyzed the attention weights generated by BERT6mA to reveal how the BERT6mA model extracts critical features responsible for the 6mA prediction. To facilitate biological sciences, the BERT6mA online web server and its source codes are freely accessible at https://github.com/kuratahiroyuki/BERT6mA.git, respectively.

DOI： 10.1093/bib/bbac053

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85127538705&origin=inward

TOPICS 医用工学・医療情報学世界初バーチャルヒト全身代謝モデルの開発と糖尿病への応用査読有り

倉田博之

医学のあゆみ ( 医歯薬出版 ) 280 ( 9 ) 948 - 949 2022年02月

Identification of a reliable sacral-sparing examination to assess the ASIA impairment scale in patients with traumatic spinal cord injury 査読有り国際誌

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

Journal of Spinal Cord Medicine 2022年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

Objectives: We evaluated the time course of the American Spinal Cord Injury Association (ASIA) impairment scale (AIS) for up to three months in participants within 72 h after traumatic spinal cord injury (TSCI) with complete paralysis. We aimed to determine the most useful sacral-sparing examination (deep anal pressure [DAP], voluntary anal contraction [VAC], S4-5 light touch [LT], or pin prick [PP] sensation) in determining AIS grades. Design: Retrospective cohort study. Setting: Spinal Injuries Center, Fukuoka, Japan. Participants: Among 668 TSCI participants registered in the Japan Single Center study for Spinal Cord Injury Database (JSSCI-DB) between January 2012 and May 2020, we extracted the data of 80 patients with AIS grade A within 72 h after injury and neurological level of injury (NLI) at T12 or higher. Interventions: None. Outcome measures: The sacral-sparing examination at the time of the change to incomplete paralysis was compared to the AIS determination using a standard algorithm and with each assessment including the VAC, DAP, S4-5LT, and S4-5PP examinations at the time of AIS functional change. Agreement among assessments was evaluated using weighted kappa coefficients. The relationship was evaluated using Spearman’s rank correlation coefficients. Results: Fifteen participants (18.8%) improved to incomplete paralysis (AIS B to D) within three months after injury. The single assessment among the sacral-sparing examinations with the highest agreement and strongest correlation with AIS determination was the S4-5LT examination (k = 0.89, P < 0.01, r = 0.84, P < 0.01). Conclusions: The S4-5LT examination is key in determining complete or incomplete paralysis due to its high discriminatory power.

DOI： 10.1080/10790268.2022.2047548

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85128102446&origin=inward

IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations 査読有り国際誌

Hasan M.M., Alam M.A., Shoombuatong W., Kurata H.

Journal of Computer Aided Molecular Design 35 ( 3 ) 315 - 323 2021年03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）

Redox-sensitive cysteine (RSC) thiol contributes to many biological processes. The identification of RSC plays an important role in clarifying some mechanisms of redox-sensitive factors; nonetheless, experimental investigation of RSCs is expensive and time-consuming. The computational approaches that quickly and accurately identify candidate RSCs using the sequence information are urgently needed. Herein, an improved and robust computational predictor named IRC-Fuse was developed to identify the RSC by fusing of multiple feature representations. To enhance the performance of our model, we integrated the probability scores evaluated by the random forest models implementing different encoding schemes. Cross-validation results exhibited that the IRC-Fuse achieved accuracy and AUC of 0.741 and 0.807, respectively. The IRC-Fuse outperformed exiting methods with improvement of 10% and 13% on accuracy and MCC, respectively, over independent test data. Comparative analysis suggested that the IRC-Fuse was more effective and promising than the existing predictors. For the convenience of experimental scientists, the IRC-Fuse online web server was implemented and publicly accessible at http://kurata14.bio.kyutech.ac.jp/IRC-Fuse/.

DOI： 10.1007/s10822-020-00368-0

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098553522&origin=inward

Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes 査読有り国際誌

Kurata H.

Iscience 24 ( 2 ) 2021年02月

Robust in-phase synchronization in repressor-based coupled gene oscillators 査読有り国際誌

Shamim Ul Hasan A.B.M., Dey S., Kurata H., Singh A.

IFAC Papersonline 54 ( 15 ) 574 - 579 2021年01月

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

Inside living cells, proteins or mRNA can show oscillations even without a periodic driving force. Such genetic oscillations are precise timekeepers for cell-cycle regulations, pattern formation during embryonic development in higher animals, and daily cycle maintenance in most organisms. The synchronization between oscillations in adjacent cells happens via intercellular coupling, which is essential for periodic segmentation formation in vertebrates and other biological processes. While molecular mechanisms of generating sustained oscillations are quite well understood, how do simple intercellular coupling produces robust synchronizations are still poorly understood? To address this question, we investigate two models of coupled gene oscillators - activator-based coupled oscillators (ACO) and repressor-based coupled oscillators (RCO) models. In our study, a single autonomous oscillator (that operates in a single cell) is based on a negative feedback, which is delayed by intracellular dynamics of an intermediate species. For the ACO model (RCO), the repressor protein of one cell activates (represses) the production of another protein in the neighbouring cell after a intercellular time delay. We investigate the coupled models in the presence of intrinsic noise due to the inherent stochasticity of the biochemical reactions. We analyze the collective oscillations from stochastic trajectories in the presence and absence of explicit coupling delay and make careful comparison between two models. Our results show no clear synchronizations in the ACO model when the coupling time delay is zero. However, a non-zero coupling delay can lead to anti-phase synchronizations in ACO. Interestingly, the RCO model shows robust in-phase synchronizations in the presence and absence of the coupling time delay. Our results suggest that the naturally occurring intercellular couplings might be based on repression rather than activation where in-phase synchronization is crucial.

DOI： 10.1016/j.ifacol.2021.10.318

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85120674527&origin=inward

Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT 査読有り国際誌

Maeda K., Kurata H.

International Journal of Molecular Sciences 24 ( 8 ) 2023年04月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein–protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.

DOI： 10.3390/ijms24087296

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85156246175&origin=inward

Recent Development of Bioinformatics Tools for microRNA Target Prediction 招待有り査読有り国際誌

Khatun M.S., Alam M.A., Shoombuatong W., Mollah M.N.H., Kurata H., Hasan M.M.

Current Medicinal Chemistry 29 ( 5 ) 865 - 880 2022年02月

担当区分：筆頭著者,　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）

MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies espe-cially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we dis-cuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

DOI： 10.2174/0929867328666210804090224

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124911775&origin=inward

Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention–based neural networks 査読有り国際誌

Tsukiyama S., Kurata H.

Computational and Structural Biotechnology Journal 20 5564 - 5573 2022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：研究論文（学術雑誌）

Viral infections represent a major health concern worldwide. The alarming rate at which SARS-CoV-2 spreads, for example, led to a worldwide pandemic. Viruses incorporate genetic material into the host genome to hijack host cell functions such as the cell cycle and apoptosis. In these viral processes, protein–protein interactions (PPIs) play critical roles. Therefore, the identification of PPIs between humans and viruses is crucial for understanding the infection mechanism and host immune responses to viral infections and for discovering effective drugs. Experimental methods including mass spectrometry-based proteomics and yeast two-hybrid assays are widely used to identify human-virus PPIs, but these experimental methods are time-consuming, expensive, and laborious. To overcome this problem, we developed a novel computational predictor, named cross-attention PHV, by implementing two key technologies of the cross-attention mechanism and a one-dimensional convolutional neural network (1D-CNN). The cross-attention mechanisms were very effective in enhancing prediction and generalization abilities. Application of 1D-CNN to the word2vec-generated feature matrices reduced computational costs, thus extending the allowable length of protein sequences to 9000 amino acid residues. Cross-attention PHV outperformed existing state-of-the-art models using a benchmark dataset and accurately predicted PPIs for unknown viruses. Cross-attention PHV also predicted human–SARS-CoV-2 PPIs with area under the curve values >0.95. The Cross-attention PHV web server and source codes are freely available at https://kurata35.bio.kyutech.ac.jp/Cross-attention_PHV/ and https://github.com/kuratahiroyuki/Cross-Attention_PHV, respectively.

DOI： 10.1016/j.csbj.2022.10.012

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85140037885&origin=inward

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