1
|
Ariji Y, Hayashi T, Ideta R, Koga R, Murai S, Naka T, Ifuku R, Towatari F, Sakai H, Kurata H, Maeda T. Identification of a reliable sacral-sparing examination to assess the ASIA impairment scale in patients with traumatic spinal cord injury. J Spinal Cord Med 2024; 47:286-292. [PMID: 35352975 PMCID: PMC10885764 DOI: 10.1080/10790268.2022.2047548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Yuto Ariji
- Department of Rehabilitation Medicine, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
| | - Tetsuo Hayashi
- Department of Rehabilitation Medicine, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
- Department of Orthopedic Surgery, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
| | - Ryosuke Ideta
- Department of Rehabilitation Medicine, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
| | - Ryuichiro Koga
- Department of Rehabilitation Medicine, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
| | - Satoshi Murai
- Department of Rehabilitation Medicine, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
| | - Tomoki Naka
- Department of Rehabilitation Medicine, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
| | - Ryusei Ifuku
- Department of Rehabilitation Medicine, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
| | - Fumihiro Towatari
- Department of Rehabilitation Medicine, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
| | - Hiroaki Sakai
- Department of Orthopedic Surgery, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology - Iizuka Campus, Fukuoka, Japan
| | - Takeshi Maeda
- Department of Orthopedic Surgery, Japan Organization of Occupational Health and Safety, Spinal Injuries Center, Fukuoka, Japan
| |
Collapse
|
2
|
Harun-Or-Roshid M, Maeda K, Phan LT, Manavalan B, Kurata H. Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach. Comput Biol Med 2024; 169:107848. [PMID: 38145601 DOI: 10.1016/j.compbiomed.2023.107848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 11/14/2023] [Accepted: 12/11/2023] [Indexed: 12/27/2023]
Abstract
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.
Collapse
Affiliation(s)
- Md Harun-Or-Roshid
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Kazuhiro Maeda
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Le Thi Phan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
| |
Collapse
|
3
|
Maeda K, Hirano M, Hayashi T, Iida M, Kurata H, Ishibashi H. Elucidating Key Characteristics of PFAS Binding to Human Peroxisome Proliferator-Activated Receptor Alpha: An Explainable Machine Learning Approach. Environ Sci Technol 2024; 58:488-497. [PMID: 38134352 DOI: 10.1021/acs.est.3c06561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
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.
Collapse
Affiliation(s)
- Kazuhiro Maeda
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan
| | - Masashi Hirano
- Department of Food and Life Sciences, School of Agriculture, Tokai University, 9-1-1 Toroku, Higashi-ku, Kumamoto-City 862-8652, Kumamoto, Japan
| | - Taka Hayashi
- Graduate School of Agriculture, Ehime University, 3-5-7 Tarumi, Matsuyama 790-8566, Japan
| | - Midori Iida
- Department of Physics and Information Technology, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan
| | - Hiroshi Ishibashi
- Graduate School of Agriculture, Ehime University, 3-5-7 Tarumi, Matsuyama 790-8566, Japan
| |
Collapse
|
4
|
Maeda K, Kurata H. Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT. Int J Mol Sci 2023; 24:ijms24087296. [PMID: 37108453 PMCID: PMC10138937 DOI: 10.3390/ijms24087296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/05/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Kazuhiro Maeda
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan
| |
Collapse
|
5
|
Hara M, Takeshita T, Kurata H, Kimura T. Decomposition of Unpolarized Fluorescence Spectrum of Uniaxially Oriented 1,3,5-Triphenylbenzene Microcrystals Into Polarized Fluorescence Spectra. J Fluoresc 2023:10.1007/s10895-023-03163-w. [PMID: 36787039 DOI: 10.1007/s10895-023-03163-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 01/30/2023] [Indexed: 02/15/2023]
Abstract
Luminescence from solids such as crystals and aggregates is of growing academic and industrial interest. In this study, we report decomposition of the unpolarized fluorescence spectrum of uniaxially oriented 1,3,5-triphenylbenzene (TPB) microcrystals into four polarized spectra measured with polarizer (V: vertical and H: horizontal) and analyser (V: vertical and H: horizontal), where V and H indicate perpendicular and parallel to the layer of TPB molecules in the crystal, respectively. Resolved spectra were interpreted in terms of the molecular and excimer like (J- and H-dimer) emissions. The origin of the excimer like emissions was discussed in relation to the molecular packing in the crystal. It was shown that polarized crystal fluorescence can provide insight into the excitation/emission process in the crystal. Although preliminary, this study demonstrates the potential of polarized fluorescence to elucidate the luminescent mechanism.
Collapse
Affiliation(s)
- Michihiro Hara
- Department of Applied Chemistry and Food Sciences, Fukui University of Technology, 3-6-1 Gakuen, Fukui, 910-8505, Japan.
| | - Tatsuya Takeshita
- Department of Applied Chemistry and Food Sciences, Fukui University of Technology, 3-6-1 Gakuen, Fukui, 910-8505, Japan
| | - Hiroyuki Kurata
- Department of Applied Chemistry and Food Sciences, Fukui University of Technology, 3-6-1 Gakuen, Fukui, 910-8505, Japan
| | - Tsunehisa Kimura
- Department of Applied Chemistry and Food Sciences, Fukui University of Technology, 3-6-1 Gakuen, Fukui, 910-8505, Japan
| |
Collapse
|
6
|
Tsukiyama S, Hasan MM, Kurata H. CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site prediction. Comput Struct Biotechnol J 2022; 21:644-654. [PMID: 36659917 PMCID: PMC9826936 DOI: 10.1016/j.csbj.2022.12.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/26/2022] [Accepted: 12/27/2022] [Indexed: 12/29/2022] Open
Abstract
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.
Collapse
Key Words
- 6mA, N6-methyladenine
- AUCs, Area under the curves
- BERT, Bidirectional Encoder Representations from Transformers
- CNN
- CNN, Convolutional neural network
- DNA modification
- Deep learning
- Interpretable prediction
- LSTM, Long short-term memory
- MCC, Matthews correlation coefficient
- Machine learning
- N6-methyladenine
- RF, Random forest
- SMRT, Single-molecule real-time
- SN, Sensitivity
- SP, Specificity
- UMAP, Uniform manifold approximation and projection
- t-SNE, t-distributed stochastic neighbor embedding
Collapse
Affiliation(s)
- Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680–4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Md Mehedi Hasan
- Tulane Center for Aging and Department of Medicine, Tulane University Health Sciences Center, New Orleans, LA 70112, USA
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680–4 Kawazu, Iizuka, Fukuoka 820-8502, Japan,Corresponding author.
| |
Collapse
|
7
|
Maeda K, Hatae A, Sakai Y, Boogerd FC, Kurata H. MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling. BMC Bioinformatics 2022; 23:455. [PMID: 36319952 PMCID: PMC9624028 DOI: 10.1186/s12859-022-05009-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/26/2022] [Indexed: 01/25/2023] Open
Abstract
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 (Km), 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 Km estimation of kinetic modeling. First, we use a machine learning-based Km 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 Km values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping Km values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated Km values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated Km 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 Km 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.
Collapse
Affiliation(s)
- Kazuhiro Maeda
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
| | - Aoi Hatae
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
| | - Yukie Sakai
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
| | - Fred C. Boogerd
- grid.12380.380000 0004 1754 9227Department of Molecular Cell Biology, Faculty of Science, VU University Amsterdam, O
- 2 Building, Amsterdam, The Netherlands
| | - Hiroyuki Kurata
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
| |
Collapse
|
8
|
Kurata H, Tsukiyama S. ICAN: Interpretable cross-attention network for identifying drug and target protein interactions. PLoS One 2022; 17:e0276609. [PMID: 36279284 PMCID: PMC9591068 DOI: 10.1371/journal.pone.0276609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/10/2022] [Indexed: 11/18/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
- * E-mail:
| | - Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| |
Collapse
|
9
|
Kurata H, Tsukiyama S, Manavalan B. iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model. Brief Bioinform 2022; 23:6623727. [PMID: 35772910 DOI: 10.1093/bib/bbac265] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/23/2022] [Accepted: 06/06/2022] [Indexed: 01/22/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| |
Collapse
|
10
|
Hasan MM, Tsukiyama S, Cho JY, Kurata H, Alam MA, Liu X, Manavalan B, Deng HW. Deepm5C: A deep learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy. Mol Ther 2022; 30:2856-2867. [PMID: 35526094 PMCID: PMC9372321 DOI: 10.1016/j.ymthe.2022.05.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 11/30/2022] Open
Abstract
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 to identify RNA m5C sites in the throughout 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. Afterwards, 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 1-D convolutional neural network. As a result, the Deepm5C predictor achieved excellent performance during cross-validation with a Matthews correlation coefficient and 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 formulate the novel testable biological hypothesis.
Collapse
Affiliation(s)
- Md Mehedi Hasan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA.
| | - Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Jae Youl Cho
- Molecular Immunology Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | - Xiaowen Liu
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea.
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA.
| |
Collapse
|
11
|
Tsukiyama S, Hasan MM, Deng HW, Kurata H. BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches. Brief Bioinform 2022; 23:6539171. [PMID: 35225328 PMCID: PMC8921755 DOI: 10.1093/bib/bbac053] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/28/2022] [Accepted: 01/31/2022] [Indexed: 01/29/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Md Mehedi Hasan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Hiroyuki Kurata
- Corresponding author: Hiroyuki Kurata, Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan. Tel: 81-948-29-7828; E-mail:
| |
Collapse
|
12
|
Takahara M, Imai Y, Miyashita F, Uchio Y, Makino M, Uchihashi M, Kaimoto S, Hadase M, Kurata H, Nakamura T. Late onset of coronary ostial stenosis following surgical aortic valve replacement in a patient with anomalous origin of the right coronary artery. J Cardiol Cases 2022; 25:83-86. [PMID: 35079304 DOI: 10.1016/j.jccase.2021.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 06/09/2021] [Accepted: 07/02/2021] [Indexed: 10/20/2022] Open
Abstract
Surgical aortic valve replacement (SAVR) in patients with anomalous origination of a coronary artery from the opposite sinus is associated with risk for myocardial ischemia during the perioperative period. [1] However, iatrogenic coronary ostial stenosis (ICOS) generally occurs within the first 6 months after SAVR. We present an unusual case of a 74-year-old man with anomalous origination of the right coronary artery from the left coronary sinus, who developed effort angina due to ICOS 19 months following SAVR and ascending aorta replacement. Angiography and computed tomography were utilized to perform a comparison before and after the procedure. From the results, it was evident that the flattened mild stenosis preoperatively was caused by anomalous origination of a coronary artery from the opposite sinus and progressed to severe stenosis by ICOS after the procedure. The patient was successfully treated with percutaneous coronary intervention. <Learning objective: Surgical aortic valve replacement in patients with anomalous origination of a coronary artery from the opposite sinus may lead to coronary ischemic events due to iatrogenic coronary ostial stenosis in the long term. Percutaneous coronary intervention is a reasonable treatment option for the patients with iatrogenic coronary ostial stenosis after surgical aortic valve replacement.>.
Collapse
Affiliation(s)
- Motoyoshi Takahara
- Department of Cardiology, Saiseikai Shiga Hospital, Ohashi 2-4-1, Ritto, Shiga 520-3046, Japan
| | - Yuta Imai
- Department of Cardiology, Saiseikai Shiga Hospital, Ohashi 2-4-1, Ritto, Shiga 520-3046, Japan
| | - Fumihiro Miyashita
- Department of Cardiovascular Surgery, Saiseikai Shiga Hospital, Ritto, Japan
| | - Yuki Uchio
- Department of Cardiology, Saiseikai Shiga Hospital, Ohashi 2-4-1, Ritto, Shiga 520-3046, Japan
| | - Masahiro Makino
- Department of Cardiology, Saiseikai Shiga Hospital, Ohashi 2-4-1, Ritto, Shiga 520-3046, Japan
| | - Motoki Uchihashi
- Department of Cardiology, Saiseikai Shiga Hospital, Ohashi 2-4-1, Ritto, Shiga 520-3046, Japan
| | - Satoshi Kaimoto
- Department of Cardiology, Saiseikai Shiga Hospital, Ohashi 2-4-1, Ritto, Shiga 520-3046, Japan
| | - Mitsuyoshi Hadase
- Department of Cardiology, Saiseikai Shiga Hospital, Ohashi 2-4-1, Ritto, Shiga 520-3046, Japan
| | - Hiroyuki Kurata
- Department of Cardiology, Saiseikai Shiga Hospital, Ohashi 2-4-1, Ritto, Shiga 520-3046, Japan
| | - Takashi Nakamura
- Department of Cardiology, Saiseikai Shiga Hospital, Ohashi 2-4-1, Ritto, Shiga 520-3046, Japan
| |
Collapse
|
13
|
Tsukiyama S, Hasan MM, Fujii S, Kurata H. LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec. Brief Bioinform 2021; 22:bbab228. [PMID: 34160596 PMCID: PMC8574953 DOI: 10.1093/bib/bbab228] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/27/2021] [Accepted: 05/25/2021] [Indexed: 12/30/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Sho Tsukiyama
- Department of Interdisciplinary Informatics in the Kyushu Institute of Technology, Japan
| | | | - Satoshi Fujii
- Department of Bioscience and Bioinformatics in the Kyushu Institute of Technology, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics in the Kyushu Institute of Technology, Japan
| |
Collapse
|
14
|
Khatun MS, Alam MA, Shoombuatong W, Mollah MNH, Kurata H, Hasan MM. Recent development of bioinformatics tools for microRNA target prediction. Curr Med Chem 2021; 29:865-880. [PMID: 34348604 DOI: 10.2174/0929867328666210804090224] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 06/10/2021] [Accepted: 06/15/2021] [Indexed: 11/22/2022]
Abstract
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 the 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 specially 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 discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.
Collapse
Affiliation(s)
- Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502. Japan
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112. United States
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700. Thailand
| | - Md Nurul Haque Mollah
- Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh. 5Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083. Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502. Japan
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502. Japan
| |
Collapse
|
15
|
Hara M, Umeda T, Kurata H. Fabrication and Characterisation of Organic EL Devices in the Presence of Cyclodextrin as an Interlayer. Sensors (Basel) 2021; 21:3666. [PMID: 34070319 PMCID: PMC8197484 DOI: 10.3390/s21113666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/18/2021] [Accepted: 05/21/2021] [Indexed: 11/17/2022]
Abstract
This study examined glass-based organic electroluminescence in the presence of a cyclodextrin polymer as an interlayer. Glass-based organic electroluminescence was achieved by the deposition of five layers of N,N'-Bis(3-methylphenyl)N,N'-bis(phenyl)-benzidine, cyclodextrin polymer (CDP), tris-(8-hydroxyquinolinato) aluminium LiF and Al on an indium tin oxide-coated glass substrate. The glass-based OEL exhibited green emission owing to the fluorescence of tris-(8-hydroxyquinolinato) aluminium. The highest luminance was 19,620 cd m-2. Moreover, the glass-based organic electroluminescence device showed green emission at 6 V in the curved state because of the inhibited aggregation of the cyclodextrin polymer. All organic molecules are insulating, but except CDP, they are standard molecules in conventional organic electroluminescence devices. In this device, the CDP layer contained pores that could allow conventional organic molecules to enter the pores and affect the organic electroluminescence interface. In particular, self-association was suppressed, efficiency was improved, and light emission was observed without the need for a high voltage. Overall, the glass-based organic electroluminescence device using CDP is an environmentally friendly device with a range of potential energy saving applications.
Collapse
Affiliation(s)
- Michihiro Hara
- Department of Applied Chemistry and Food Sciences, Faculty of Environmental and Information Sciences, Fukui University of Technology, Gakuen 3-6-1, Fukui 9108505, Japan; (T.U.); (H.K.)
| | | | | |
Collapse
|
16
|
Hasan MM, Alam MA, Shoombuatong W, Deng HW, Manavalan B, Kurata H. NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning. Brief Bioinform 2021; 22:6272801. [PMID: 33975333 DOI: 10.1093/bib/bbab167] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/23/2021] [Accepted: 04/09/2021] [Indexed: 12/13/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.,Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | | | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| |
Collapse
|
17
|
Nilamyani AN, Auliah FN, Moni MA, Shoombuatong W, Hasan MM, Kurata H. PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features. Int J Mol Sci 2021; 22:2704. [PMID: 33800121 PMCID: PMC7962192 DOI: 10.3390/ijms22052704] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 12/15/2022] Open
Abstract
Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine 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.
Collapse
Affiliation(s)
- Andi Nur Nilamyani
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (A.N.N.); (F.N.A.)
| | - Firda Nurul Auliah
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (A.N.N.); (F.N.A.)
| | - Mohammad Ali Moni
- WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Sydney, NSW 2052, Australia;
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (A.N.N.); (F.N.A.)
- Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (A.N.N.); (F.N.A.)
| |
Collapse
|
18
|
Auliah FN, Nilamyani AN, Shoombuatong W, Alam MA, Hasan MM, Kurata H. PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations. Int J Mol Sci 2021; 22:ijms22042120. [PMID: 33672741 PMCID: PMC7924619 DOI: 10.3390/ijms22042120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/12/2021] [Accepted: 02/18/2021] [Indexed: 12/30/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Firda Nurul Auliah
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (F.N.A.); (A.N.N.); (M.M.H.)
| | - Andi Nur Nilamyani
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (F.N.A.); (A.N.N.); (M.M.H.)
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA;
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (F.N.A.); (A.N.N.); (M.M.H.)
- Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (F.N.A.); (A.N.N.); (M.M.H.)
- Correspondence:
| |
Collapse
|
19
|
Hasan MM, Shoombuatong W, Kurata H, Manavalan B. Critical evaluation of web-based DNA N6-methyladenine site prediction tools. Brief Funct Genomics 2021; 20:258-272. [PMID: 33491072 DOI: 10.1093/bfgp/elaa028] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 12/11/2020] [Accepted: 12/15/2020] [Indexed: 12/13/2022] Open
Abstract
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.
Collapse
Affiliation(s)
| | - Watshara Shoombuatong
- Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics in the Kyushu Institute of Technology, Japan
| | | |
Collapse
|
20
|
Hasan MM, Alam MA, Shoombuatong W, Kurata H. IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations. J Comput Aided Mol Des 2021; 35:315-323. [PMID: 33392948 DOI: 10.1007/s10822-020-00368-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 12/06/2020] [Indexed: 12/11/2022]
Abstract
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/ .
Collapse
Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan. .,Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo, 102-0083, Japan.
| | - Md Ashad Alam
- Tulane Center of Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
| |
Collapse
|
21
|
Hasan MM, Khatun MS, Kurata H. iLBE for Computational Identification of Linear B-cell Epitopes by Integrating Sequence and Evolutionary Features. Genomics Proteomics Bioinformatics 2020; 18:593-600. [PMID: 33099033 PMCID: PMC8377379 DOI: 10.1016/j.gpb.2019.04.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 01/13/2019] [Accepted: 04/19/2019] [Indexed: 12/17/2022]
Abstract
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 Linear 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/.
Collapse
Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan.
| |
Collapse
|
22
|
Khatun MS, Hasan MM, Shoombuatong W, Kurata H. ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations. J Comput Aided Mol Des 2020; 34:1229-1236. [DOI: 10.1007/s10822-020-00343-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 09/16/2020] [Indexed: 12/11/2022]
|
23
|
Hasan MM, Basith S, Khatun MS, Lee G, Manavalan B, Kurata H. Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework. Brief Bioinform 2020; 22:5903398. [PMID: 32910169 DOI: 10.1093/bib/bbaa202] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/06/2020] [Accepted: 08/06/2020] [Indexed: 12/13/2022] Open
Abstract
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/.
Collapse
Affiliation(s)
| | - Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | - Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Japan
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | | | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics in the Kyushu Institute of Technology, Japan
| |
Collapse
|
24
|
Khatun MS, Shoombuatong W, Hasan MM, Kurata H. Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction. Curr Genomics 2020; 21:454-463. [PMID: 33093807 PMCID: PMC7536797 DOI: 10.2174/1389202921999200625103936] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 03/19/2020] [Accepted: 05/27/2020] [Indexed: 12/22/2022] Open
Abstract
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.
Collapse
Affiliation(s)
| | | | - Md. Mehedi Hasan
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan; Tel: +81-948-297-828; E-mail: and Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
| | - Hiroyuki Kurata
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan; Tel: +81-948-297-828; E-mail: and Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
| |
Collapse
|
25
|
Takeuchi Y, Asano T, Tsuzaki K, Wada K, Kurata H. Asymmetric Amination of meso-Epoxide with Vegetable Powder as a Low-Toxicity Catalyst. Molecules 2020; 25:E3197. [PMID: 32668749 PMCID: PMC7397229 DOI: 10.3390/molecules25143197] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/09/2020] [Accepted: 07/10/2020] [Indexed: 11/16/2022] Open
Abstract
This paper describes the scope and limitation of substrates subjected to asymmetric amination with epoxides catalyzed by a soluble soybean polysaccharide (Soyafibe S-DN), which we recently discovered from the reaction of 1,2-epoxycyclohexane with cyclopropylamine. Various meso-epoxides reacted with various amines afforded the corresponding products with good enantiomeric selectivity. Since it was found that pectin was found to have a catalytic ability after screening commercially available polysaccharides, we studied 33 different vegetable powders having pectic substances, and we found that many vegetable powders showed catalytic ability. These results should guide in using vegetable components as low-toxic catalysts for the production of pharmaceuticals.
Collapse
Affiliation(s)
- Yuki Takeuchi
- Kyowa Pharma Chemical Co., Ltd., Chokeiji, Takaoka, Toyama 933-8511, Japan; (T.A.); (K.T.); (K.W.)
- Graduate School of Engineering, Fukui University of Technology, Fukui 910-8505, Japan
| | - Tatsuhiro Asano
- Kyowa Pharma Chemical Co., Ltd., Chokeiji, Takaoka, Toyama 933-8511, Japan; (T.A.); (K.T.); (K.W.)
| | - Kazuya Tsuzaki
- Kyowa Pharma Chemical Co., Ltd., Chokeiji, Takaoka, Toyama 933-8511, Japan; (T.A.); (K.T.); (K.W.)
| | - Koichi Wada
- Kyowa Pharma Chemical Co., Ltd., Chokeiji, Takaoka, Toyama 933-8511, Japan; (T.A.); (K.T.); (K.W.)
| | - Hiroyuki Kurata
- Organization for Fundamental Education, Fukui University of Technology, Fukui 910-8505, Japan;
| |
Collapse
|
26
|
Abstract
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.
Collapse
Affiliation(s)
- Md Mamunur Rashid
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Hiroyuki Kurata
- Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| |
Collapse
|
27
|
Ariji Y, Hayashi T, Ideta R, Koga R, Murai S, Towatari F, Terashi Y, Sakai H, Kurata H, Maeda T. 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. Spinal Cord 2020; 58:1158-1165. [DOI: 10.1038/s41393-020-0488-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 05/10/2020] [Accepted: 05/11/2020] [Indexed: 11/09/2022]
|
28
|
Hasan MM, Manavalan B, Shoombuatong W, Khatun MS, Kurata H. i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation. Plant Mol Biol 2020; 103:225-234. [PMID: 32140819 DOI: 10.1007/s11103-020-00988-y] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 02/29/2020] [Indexed: 05/28/2023]
Abstract
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/.
Collapse
Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
- Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo, 102-0083, Japan
| | | | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
- Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
| |
Collapse
|
29
|
Matsuoka Y, Kurata H. Computer-Aided Rational Design of Efficient NADPH Production System by Escherichia coli pgi Mutant Using a Mixture of Glucose and Xylose. Front Bioeng Biotechnol 2020; 8:277. [PMID: 32318559 PMCID: PMC7154054 DOI: 10.3389/fbioe.2020.00277] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 03/16/2020] [Indexed: 12/02/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Japan.,Biomedical Informatics R&D Center, Kyushu Institute of Technology, Iizuka, Japan
| |
Collapse
|
30
|
Uchihashi M, Makino M, Kaimoto S, Imai Y, Hadase M, Kurata H, Nakamura T. Platypnea-Orthodeoxia Syndrome Associated with Spontaneously Ruptured Chordae Tendineae of Tricuspid Valve. CASE 2020; 4:90-92. [PMID: 32337398 PMCID: PMC7175809 DOI: 10.1016/j.case.2019.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
POS is a rare clinical condition. Symptoms are upright posture dyspnea and arterial desaturation. Supine and sitting position oxygen saturation levels suggest right-to-left shunt. First report of uncommon spontaneous tricuspid valve chordal rupture–induced POS.
Collapse
|
31
|
Matsumoto K, Miki K, Tanaka R, Matsuda T, Nehira T, Hirao Y, Kurata H, Pescitelli G, Kubo T. Chiral Tetraarylmethane Derivative with Metal‐Coordinating Ability. ASIAN J ORG CHEM 2020. [DOI: 10.1002/ajoc.202000132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Kouzou Matsumoto
- Institute of Natural SciencesSenshu University 2-1-1 Higashimita Kawasaki, Kanagawa 214-8580 Japan
| | - Kaori Miki
- Department of Chemistry Graduate School of ScienceOsaka University 1-1 Machikaneyamacho Toyonaka, Osaka 560-0043 Japan
| | - Rina Tanaka
- Department of Chemistry Graduate School of ScienceOsaka University 1-1 Machikaneyamacho Toyonaka, Osaka 560-0043 Japan
| | - Takahiro Matsuda
- Faculty of Integrated Arts and SciencesHiroshima University 1-7-1 Kagamiyama Higashi-hiroshima 739-8521 Japan
| | - Tatsuo Nehira
- Graduate School of Integrated Sciences for LifeHiroshima University 1-7-1 Kagamiyama Higashi-hiroshima 739-8521 Japan
| | - Yasukazu Hirao
- Department of Chemistry Graduate School of ScienceOsaka University 1-1 Machikaneyamacho Toyonaka, Osaka 560-0043 Japan
| | - Hiroyuki Kurata
- Organization for Fundamental EducationFukui University of Technology 3-6-1 Gakuen Fukui 910-8505 Japan
| | - Gennaro Pescitelli
- Department of Chemistry, via Moruzzi 13University of Pisa 56124 Pisa Italy
| | - Takashi Kubo
- Department of Chemistry Graduate School of ScienceOsaka University 1-1 Machikaneyamacho Toyonaka, Osaka 560-0043 Japan
| |
Collapse
|
32
|
Rashid MM, Shatabda S, Hasan MM, Kurata H. Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites. Curr Genomics 2020; 21:194-203. [PMID: 33071613 PMCID: PMC7521030 DOI: 10.2174/1389202921666200427210833] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/12/2020] [Accepted: 04/13/2020] [Indexed: 01/10/2023] Open
Abstract
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 phosphorylation 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, window size, and software utility. Initially, we review the currently available phosphorylation site databases of the microbiome, the state-of-the-art ML approaches, working principles, and their performances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.
Collapse
Affiliation(s)
| | | | - Md. Mehedi Hasan
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828;, E-mail: and Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
| | - Hiroyuki Kurata
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828;, E-mail: and Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
| |
Collapse
|
33
|
Matsumoto K, Miki K, Tanaka R, Matsuda T, Nehira T, Hirao Y, Kurata H, Pescitelli G, Kubo T. Chiral Tetraarylmethane Derivative with Metal‐Coordinating Ability. ASIAN J ORG CHEM 2020. [DOI: 10.1002/ajoc.201900760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Kouzou Matsumoto
- Institute of Natural SciencesSenshu University 2-1-1 Higashimita Kawasaki, Kanagawa 214-8580 Japan
| | - Kaori Miki
- Department of Chemistry Graduate School of ScienceOsaka University 1-1 Machikaneyamacho Toyonaka, Osaka 560-0043 Japan
| | - Rina Tanaka
- Department of Chemistry Graduate School of ScienceOsaka University 1-1 Machikaneyamacho Toyonaka, Osaka 560-0043 Japan
| | - Takahiro Matsuda
- Faculty of Integrated Arts and SciencesHiroshima University 1-7-1 Kagamiyama Higashi-hiroshima 739-8521 Japan
| | - Tatsuo Nehira
- Graduate School of Integrated Sciences for LifeHiroshima University 1-7-1 Kagamiyama Higashi-hiroshima 739-8521 Japan
| | - Yasukazu Hirao
- Department of Chemistry Graduate School of ScienceOsaka University 1-1 Machikaneyamacho Toyonaka, Osaka 560-0043 Japan
| | - Hiroyuki Kurata
- Organization for Fundamental EducationFukui University of Technology 3-6-1 Gakuen Fukui 910-8505 Japan
| | - Gennaro Pescitelli
- Department of Chemistry, via Moruzzi 13University of Pisa 56124 Pisa Italy
| | - Takashi Kubo
- Department of Chemistry Graduate School of ScienceOsaka University 1-1 Machikaneyamacho Toyonaka, Osaka 560-0043 Japan
| |
Collapse
|
34
|
Hasan ABMSU, Kurata H, Pechmann S. Improvement of the memory function of a mutual repression network in a stochastic environment by negative autoregulation. BMC Bioinformatics 2019; 20:734. [PMID: 31881978 PMCID: PMC6935196 DOI: 10.1186/s12859-019-3315-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 12/12/2019] [Indexed: 11/10/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- A B M Shamim Ul Hasan
- Department of Biochemistry, Université de Montréal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada.,The Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Hiroyuki Kurata
- The Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
| | - Sebastian Pechmann
- Department of Biochemistry, Université de Montréal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada.
| |
Collapse
|
35
|
Hasan MM, Manavalan B, Khatun MS, Kurata H. i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome. Int J Biol Macromol 2019; 157:752-758. [PMID: 31805335 DOI: 10.1016/j.ijbiomac.2019.12.009] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 11/29/2019] [Accepted: 12/02/2019] [Indexed: 12/18/2022]
Abstract
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/.
Collapse
Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Balachandran Manavalan
- Department of Physiology, Ajou University School of Medicine, Suwon 443380, Republic of Korea
| | - Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
| |
Collapse
|
36
|
Abstract
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.
Collapse
Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka, Japan. .,Biomedical Informatics R&D Center, Kyushu Institute of Technology, Fukuoka, Japan.
| |
Collapse
|
37
|
Mori D, Miyagawa S, Kawamura T, Hata H, Ueno T, Toda K, Kuratani T, Kurata H, Nishida H, Sawa Y. P315In-vivo and vitro mitochondrial transfer from adipose-derived mesenchymal stem cell to ischemic cardiomyocyte. Eur Heart J 2019. [DOI: 10.1093/eurheartj/ehz747.0150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Although transplantation of human Adipose-derived Mesenchymal stem cell (hADSC) shows efficacy in the treatment of ischemic cardiomyopathy, its therapeutic mechanisms have not been fully elucidated. It has been already reported that mitochondria transfer to recipient cells have impact on resistance to injury and tissue regeneration, however this phenomenon has not been elucidated in the damaged heart. Therefore, we hypothesized that ADSC transfer own mitochondria to cardiomyocytes in-vivo and in-vitro under ischemic condition, resulting in the functional recovery of cardiomyocyte.
Method and result
Transplantation of hADSC (group A) to the heart surface or sham operation (group C) was performed in rats that were subjected to LAD ligation 2 weeks prior to the treatment (n=10 each). The number of transplant cell was 1x106/body. Three days after transplantation, transferred hADSCs' mitochondria were observed in recipient cardiomyocytes histologically (Figure). Quantitative PCR analysis revealed that mitochondrial genome of recipient myocytes increased over time. The cardiac function assessed with echocardiography was significantly better in group A. Furthermore, live-imaging of hADSC transplantation revealed the suspected transfer of mitochondria to beating heart.
In-vitro, the co-culture of rat cardiomyocytes (rCM) and hADSC was observed with time-lapse photography and demonstrated mitochondrial transfer under the hypoxic condition. The measuring the oxygen consumption rate (OCR) of these cells showed that OCR of rCM was reinforced by co-culture with hADSC conspicuously.
Figure 1
Conclusion
Mitochondrial transfer from hADSC to rCM was suggested in-vivo and in-vitro ischemic condition and suspected to be related to functional recovery of ischemic cardiomyocyte.
Collapse
Affiliation(s)
- D Mori
- Osaka University Graduate School of Medicine, Osaka, Japan
| | - S Miyagawa
- Osaka University Graduate School of Medicine, Osaka, Japan
| | - T Kawamura
- Osaka University Graduate School of Medicine, Osaka, Japan
| | - H Hata
- Osaka University Graduate School of Medicine, Osaka, Japan
| | - T Ueno
- Osaka University Graduate School of Medicine, Osaka, Japan
| | - K Toda
- Osaka University Graduate School of Medicine, Osaka, Japan
| | - T Kuratani
- Osaka University Graduate School of Medicine, Osaka, Japan
| | - H Kurata
- Osaka University Graduate School of Medicine, Osaka, Japan
| | - H Nishida
- Osaka University Graduate School of Medicine, Osaka, Japan
| | - Y Sawa
- Osaka University Graduate School of Medicine, Osaka, Japan
| |
Collapse
|
38
|
Khatun S, Hasan M, Kurata H. Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties. FEBS Lett 2019; 593:3029-3039. [PMID: 31297788 DOI: 10.1002/1873-3468.13536] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 06/25/2019] [Accepted: 07/05/2019] [Indexed: 12/30/2022]
Abstract
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.
Collapse
Affiliation(s)
- Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| | - Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.,Biomedical Informatics R&D Center, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| |
Collapse
|
39
|
Hasan MM, Rashid MM, Khatun MS, Kurata H. Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information. Sci Rep 2019; 9:8258. [PMID: 31164681 PMCID: PMC6547684 DOI: 10.1038/s41598-019-44548-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 05/20/2019] [Indexed: 11/30/2022] Open
Abstract
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/.
Collapse
Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Md Mamunur Rashid
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan. .,Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
| |
Collapse
|
40
|
Maeda K, Westerhoff HV, Kurata H, Boogerd FC. Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network. NPJ Syst Biol Appl 2019; 5:14. [PMID: 30993002 PMCID: PMC6461619 DOI: 10.1038/s41540-019-0091-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 03/12/2019] [Indexed: 11/17/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Kazuhiro Maeda
- Frontier Research Academy for Young Researchers, Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka Japan
| | - Hans V. Westerhoff
- Department of Molecular Cell Biology, Faculty of Science, VU University Amsterdam, O|2 building, Amsterdam, Netherlands
- Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka Japan
- Biomedical Informatics R&D Center, Kyushu Institute of Technology, Iizuka, Fukuoka Japan
| | - Fred C. Boogerd
- Department of Molecular Cell Biology, Faculty of Science, VU University Amsterdam, O|2 building, Amsterdam, Netherlands
| |
Collapse
|
41
|
Khatun MS, Hasan MM, Kurata H. PreAIP: Computational Prediction of Anti-inflammatory Peptides by Integrating Multiple Complementary Features. Front Genet 2019; 10:129. [PMID: 30891059 PMCID: PMC6411759 DOI: 10.3389/fgene.2019.00129] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 02/06/2019] [Indexed: 12/31/2022] Open
Abstract
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/.
Collapse
Affiliation(s)
- Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka, Japan
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka, Japan.,Biomedical Informatics R&D Center, Kyushu Institute of Technology, Fukuoka, Japan
| |
Collapse
|
42
|
Hasan MM, Khatun MS, Kurata H. Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites. Cells 2019; 8:cells8020095. [PMID: 30696115 PMCID: PMC6406724 DOI: 10.3390/cells8020095] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 01/24/2019] [Accepted: 01/24/2019] [Indexed: 12/19/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680⁻4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
| | - Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680⁻4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680⁻4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
- Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
| |
Collapse
|
43
|
Hasan MM, Manavalan B, Khatun MS, Kurata H. Prediction of S-nitrosylation sites by integrating support vector machines and random forest. Mol Omics 2019; 15:451-458. [DOI: 10.1039/c9mo00098d] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Cysteine S-nitrosylation is a type of reversible post-translational modification of proteins, which controls diverse biological processes.
Collapse
Affiliation(s)
- Md. Mehedi Hasan
- Department of Bioscience and Bioinformatics
- Kyushu Institute of Technology
- Iizuka
- Japan
- Japan Society for the Promotion of Science
| | | | - Mst. Shamima Khatun
- Department of Bioscience and Bioinformatics
- Kyushu Institute of Technology
- Iizuka
- Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics
- Kyushu Institute of Technology
- Iizuka
- Japan
- Biomedical Informatics R&D Center
| |
Collapse
|
44
|
Hasan MM, Khatun MS, Kurata H. A Comprehensive Review of In silico Analysis for Protein S-sulfenylation Sites. Protein Pept Lett 2018; 25:815-821. [DOI: 10.2174/0929866525666180905110619] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 07/05/2018] [Accepted: 08/29/2018] [Indexed: 11/22/2022]
Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820- 8502, Japan
| | - Mst Shamima Khatun
- Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820- 8502, Japan
| |
Collapse
|
45
|
Hasan MM, Kurata H. GPSuc: Global Prediction of Generic and Species-specific Succinylation Sites by aggregating multiple sequence features. PLoS One 2018; 13:e0200283. [PMID: 30312302 PMCID: PMC6193575 DOI: 10.1371/journal.pone.0200283] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 06/22/2018] [Indexed: 01/09/2023] Open
Abstract
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 species-specific 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/.
Collapse
Affiliation(s)
- Md. Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Kawazu, Iizuka, Fukuoka, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Kawazu, Iizuka, Fukuoka, Japan
- Biomedi Informatics R&D Center, Kyushu Institute of Technology, Kawazu, Iizuka, Fukuoka, Japan
- * E-mail:
| |
Collapse
|
46
|
Maeda K, Kurata H. Long negative feedback loop enhances period tunability of biological oscillators. J Theor Biol 2018; 440:21-31. [PMID: 29253507 DOI: 10.1016/j.jtbi.2017.12.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Revised: 12/08/2017] [Accepted: 12/14/2017] [Indexed: 11/18/2022]
Abstract
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.
Collapse
Affiliation(s)
- Kazuhiro Maeda
- Frontier Research Academy for Young Researchers, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata, Kitakyushu, Fukuoka 804-8550, Japan; Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
| |
Collapse
|
47
|
Kamijo Y, Kawahara K, Yoshinaga T, Kurata H, Arima K, Furukawa T. A novel isolation method for cancer prognostic factors via the p53 pathway by a combination of in vitro and in silico analyses. Oncoscience 2018; 5:88-98. [PMID: 29854877 PMCID: PMC5978436 DOI: 10.18632/oncoscience.411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 04/21/2018] [Indexed: 12/02/2022] Open
Abstract
Identifying new therapeutic target genes affecting the survival of patients with cancer is crucial for the development of new cancer therapies. Here, we developed a novel technology combining in vitro short hairpin RNA (shRNA) library screening and in silico analysis of the tumor transcriptome to identify prognostic factors via the p53 tumor-suppressor pathway. For initial screening, we screened 5,000 genes through selection of shRNAs in p53 wild-type tumor cells that altered sensitivity to the p53 activator actinomycin D (ActD) to identify p53 regulatory genes; shRNAs targeting 322 genes were obtained. Among these 322 genes, seven were prognostic factor candidates whose high expression increased ActD sensitivity while prolonging the survival period in patients with the p53 wild-type genotype. Conversely, we identified 33 genes as prognostic factor candidates among ActD-resistant genes related to a shortened survival period only in p53 wild-type tumors. These 40 genes had biological functions such as apoptosis, drug response, cell cycle checkpoint, and cell proliferation. The 40 genes selected by this method contained many known genes related to the p53 pathway and prognosis in patients with cancer. In summary, we developed an efficient screening method to identify p53-dependent prognostic factors with in vitro experimental data and database analysis.
Collapse
Affiliation(s)
- Yohey Kamijo
- Department of Molecular Oncology, Graduate School Medical and Dental Sciences, Kagoshima University, Kagoshima 890- 8544, Japan
- Department of Chemistry and Bioscience, Faculty of Science, Graduate School of Science and Engineering, Kagoshima University, Kagoshima 890-0065, Japan
| | - Kohichi Kawahara
- Department of Molecular Oncology, Graduate School Medical and Dental Sciences, Kagoshima University, Kagoshima 890- 8544, Japan
| | - Takuma Yoshinaga
- Division of Clinical Application, Nanpuh Hospital, Kagoshima 892-8512, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan
| | - Kazunari Arima
- Department of Chemistry and Bioscience, Faculty of Science, Graduate School of Science and Engineering, Kagoshima University, Kagoshima 890-0065, Japan
| | - Tatsuhiko Furukawa
- Department of Molecular Oncology, Graduate School Medical and Dental Sciences, Kagoshima University, Kagoshima 890- 8544, Japan
| |
Collapse
|
48
|
Ramasse QM, van Aken PA, Kurata H. EDGE 2017 - Enhanced Data Generated by Electrons, Okinawa, May 2017. Microscopy (Oxf) 2018; 67:i1-i2. [PMID: 29584930 DOI: 10.1093/jmicro/dfy013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- Q M Ramasse
- SuperSTEM Laboratory, SciTech Daresbury Campus, Keckwick Lane, Daresbury WA4 4AD, UK.,School of Chemical and Process Engineering, School of Physics, University of Leeds, Leeds, LS2 9JT, UK
| | - P A van Aken
- Stuttgart Center for Electron Microscopy, Max Planck Institute for Solid State Research, Heisenbergstr. 1, 70569 Stuttgart, Germany
| | - H Kurata
- Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
| |
Collapse
|
49
|
Matsuoka Y, Kurata H. Modeling and simulation of the redox regulation of the metabolism in Escherichia coli at different oxygen concentrations. Biotechnol Biofuels 2017; 10:183. [PMID: 28725263 PMCID: PMC5512849 DOI: 10.1186/s13068-017-0867-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 07/05/2017] [Indexed: 06/07/2023]
Abstract
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.
Collapse
Affiliation(s)
- Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502 Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502 Japan
- Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502 Japan
| |
Collapse
|
50
|
Takeshita T, Kurata H, Hara M. Improvement of photoionization efficiency of diarylethene-cyclodextrin complexes by using multi-laser pulse excitation. J Photochem Photobiol A Chem 2017. [DOI: 10.1016/j.jphotochem.2017.04.026] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|