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Qin Z, Ren H, Zhao P, Wang K, Liu H, Miao C, Du Y, Li J, Wu L, Chen Z. Current computational tools for protein lysine acylation site prediction. Brief Bioinform 2024; 25:bbae469. [PMID: 39316944 PMCID: PMC11421846 DOI: 10.1093/bib/bbae469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/20/2024] [Accepted: 09/07/2024] [Indexed: 09/26/2024] Open
Abstract
As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.
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Affiliation(s)
- Zhaohui Qin
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Haoran Ren
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Pei Zhao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Kaiyuan Wang
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Huixia Liu
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Chunbo Miao
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Yanxiu Du
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Junzhou Li
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Liuji Wu
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
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Liang JZ, Li DH, Xiao YC, Shi FJ, Zhong T, Liao QY, Wang Y, He QY. LAFEM: A Scoring Model to Evaluate Functional Landscape of Lysine Acetylome. Mol Cell Proteomics 2024; 23:100700. [PMID: 38104799 PMCID: PMC10828473 DOI: 10.1016/j.mcpro.2023.100700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 11/18/2023] [Accepted: 12/14/2023] [Indexed: 12/19/2023] Open
Abstract
Protein lysine acetylation is a critical post-translational modification involved in a wide range of biological processes. To date, about 20,000 acetylation sites of Homo sapiens were identified through mass spectrometry-based proteomic technology, but more than 95% of them have unclear functional annotations because of the lack of existing prioritization strategy to assess the functional importance of the acetylation sites on large scale. Hence, we established a lysine acetylation functional evaluating model (LAFEM) by considering eight critical features surrounding lysine acetylation site to high-throughput estimate the functional importance of given acetylation sites. This was achieved by selecting one of the random forest models with the best performance in 10-fold cross-validation on undersampled training dataset. The global analysis demonstrated that the molecular environment of acetylation sites with high acetylation functional scores (AFSs) mainly had the features of larger solvent-accessible surface area, stronger hydrogen bonding-donating abilities, near motif and domain, higher homology, and disordered degree. Importantly, LAFEM performed well in validation dataset and acetylome, showing good accuracy to screen out fitness directly relevant acetylation sites and assisting to explain the core reason for the difference between biological models from the perspective of acetylome. We further used cellular experiments to confirm that, in nuclear casein kinase and cyclin-dependent kinase substrate 1, acetyl-K35 with higher AFS was more important than acetyl-K9 with lower AFS in the proliferation of A549 cells. LAFEM provides a prioritization strategy to large scale discover the fitness directly relevant acetylation sites, which constitutes an unprecedented resource for better understanding of functional acetylome.
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Affiliation(s)
- Jun-Ze Liang
- MOE Key Laboratory of Tumor Molecular Biology and State Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Life Science and Technology, Jinan University, Guangzhou, China
| | - De-Hua Li
- MOE Key Laboratory of Tumor Molecular Biology and State Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Life Science and Technology, Jinan University, Guangzhou, China
| | - Yong-Chun Xiao
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Fu-Jin Shi
- MOE Key Laboratory of Tumor Molecular Biology and State Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Life Science and Technology, Jinan University, Guangzhou, China
| | - Tairan Zhong
- MOE Key Laboratory of Tumor Molecular Biology and State Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Life Science and Technology, Jinan University, Guangzhou, China
| | - Qian-Ying Liao
- IMEC-DistriNet Research Group, Department of Computer Science, KU Leuven, Leuven, Belgium
| | - Yang Wang
- MOE Key Laboratory of Tumor Molecular Biology and State Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Life Science and Technology, Jinan University, Guangzhou, China.
| | - Qing-Yu He
- MOE Key Laboratory of Tumor Molecular Biology and State Key Laboratory of Bioactive Molecules and Druggability Assessment, College of Life Science and Technology, Jinan University, Guangzhou, China.
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Yang YH, Wen R, Yang N, Zhang TN, Liu CF. Roles of protein post-translational modifications in glucose and lipid metabolism: mechanisms and perspectives. Mol Med 2023; 29:93. [PMID: 37415097 DOI: 10.1186/s10020-023-00684-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/10/2023] [Indexed: 07/08/2023] Open
Abstract
The metabolism of glucose and lipids is essential for energy production in the body, and dysregulation of the metabolic pathways of these molecules is implicated in various acute and chronic diseases, such as type 2 diabetes, Alzheimer's disease, atherosclerosis (AS), obesity, tumor, and sepsis. Post-translational modifications (PTMs) of proteins, which involve the addition or removal of covalent functional groups, play a crucial role in regulating protein structure, localization function, and activity. Common PTMs include phosphorylation, acetylation, ubiquitination, methylation, and glycosylation. Emerging evidence indicates that PTMs are significant in modulating glucose and lipid metabolism by modifying key enzymes or proteins. In this review, we summarize the current understanding of the role and regulatory mechanisms of PTMs in glucose and lipid metabolism, with a focus on their involvement in disease progression associated with aberrant metabolism. Furthermore, we discuss the future prospects of PTMs, highlighting their potential for gaining deeper insights into glucose and lipid metabolism and related diseases.
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Affiliation(s)
- Yu-Hang Yang
- Department of Pediatrics, Shengjing Hospital of China Medical University, No.36, SanHao Street, Liaoning Province, Shenyang City, 110004, China
| | - Ri Wen
- Department of Pediatrics, Shengjing Hospital of China Medical University, No.36, SanHao Street, Liaoning Province, Shenyang City, 110004, China
| | - Ni Yang
- Department of Pediatrics, Shengjing Hospital of China Medical University, No.36, SanHao Street, Liaoning Province, Shenyang City, 110004, China
| | - Tie-Ning Zhang
- Department of Pediatrics, Shengjing Hospital of China Medical University, No.36, SanHao Street, Liaoning Province, Shenyang City, 110004, China.
| | - Chun-Feng Liu
- Department of Pediatrics, Shengjing Hospital of China Medical University, No.36, SanHao Street, Liaoning Province, Shenyang City, 110004, China.
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Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2499:285-322. [PMID: 35696087 DOI: 10.1007/978-1-0716-2317-6_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.
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Yu K, Zhang Q, Liu Z, Du Y, Gao X, Zhao Q, Cheng H, Li X, Liu ZX. Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation. Brief Bioinform 2021; 21:1798-1805. [PMID: 32978618 DOI: 10.1093/bib/bbz107] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/18/2019] [Accepted: 07/30/2019] [Indexed: 11/14/2022] Open
Abstract
Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein-protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http://deeppla.cancerbio.info.
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Affiliation(s)
- Kai Yu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Qingfeng Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Zekun Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yimeng Du
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Xinjiao Gao
- Division of Molecular and Cell Biophysics, Hefei National Science Center for Physical Sciences at the Microscale, Anhui Key Laboratory of Cellular Dynamics and Chemical Biology, School of Life Sciences, University of Science and Technology of the China, Hefei 230027, China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Han Cheng
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Xiaoxing Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ze-Xian Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
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Basith S, Lee G, Manavalan B. STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction. Brief Bioinform 2021; 23:6370848. [PMID: 34532736 PMCID: PMC8769686 DOI: 10.1093/bib/bbab376] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/22/2021] [Accepted: 08/24/2021] [Indexed: 12/13/2022] Open
Abstract
Protein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION.
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Affiliation(s)
- Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | - Gwang Lee
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, Republic of Korea
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Yang Y, Wang H, Li W, Wang X, Wei S, Liu Y, Xu Y. Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks. BMC Bioinformatics 2021; 22:171. [PMID: 33789579 PMCID: PMC8010967 DOI: 10.1186/s12859-021-04101-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 03/23/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins. METHOD We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories. RESULTS In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN . CONCLUSIONS The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
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Affiliation(s)
- Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Hui Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China
| | - Wen Li
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xiaobo Wang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Shizhao Wei
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yulong Liu
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China.
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Xia C, Tao Y, Li M, Che T, Qu J. Protein acetylation and deacetylation: An important regulatory modification in gene transcription (Review). Exp Ther Med 2020; 20:2923-2940. [PMID: 32855658 PMCID: PMC7444376 DOI: 10.3892/etm.2020.9073] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Accepted: 04/24/2020] [Indexed: 12/16/2022] Open
Abstract
Cells primarily rely on proteins to perform the majority of their physiological functions, and the function of proteins is regulated by post-translational modifications (PTMs). The acetylation of proteins is a dynamic and highly specific PTM, which has an important influence on the functions of proteins, such as gene transcription and signal transduction. The acetylation of proteins is primarily dependent on lysine acetyltransferases and lysine deacetylases. In recent years, due to the widespread use of mass spectrometry and the emergence of new technologies, such as protein chips, studies on protein acetylation have been further developed. Compared with histone acetylation, acetylation of non-histone proteins has gradually become the focus of research due to its important regulatory mechanisms and wide range of applications. The discovery of specific protein acetylation sites using bioinformatic tools can greatly aid the understanding of the underlying mechanisms of protein acetylation involved in related physiological and pathological processes.
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Affiliation(s)
- Can Xia
- Department of Cell Biology, Medical College of Soochow University, Suzhou, Jiangsu 215123, P.R. China
| | - Yu Tao
- Department of Cell Biology, Medical College of Soochow University, Suzhou, Jiangsu 215123, P.R. China
| | - Mingshan Li
- Department of Cell Biology, Medical College of Soochow University, Suzhou, Jiangsu 215123, P.R. China
| | - Tuanjie Che
- Laboratory of Precision Medicine and Translational Medicine, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Science and Technology Town Hospital, Suzhou, Jiangsu 215153, P.R. China
| | - Jing Qu
- Department of Cell Biology, Medical College of Soochow University, Suzhou, Jiangsu 215123, P.R. China
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Wang L, Zhang R. Towards Computational Models of Identifying Protein Ubiquitination Sites. Curr Drug Targets 2020; 20:565-578. [PMID: 30246637 DOI: 10.2174/1389450119666180924150202] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 08/29/2018] [Accepted: 09/04/2018] [Indexed: 12/25/2022]
Abstract
Ubiquitination is an important post-translational modification (PTM) process for the regulation of protein functions, which is associated with cancer, cardiovascular and other diseases. Recent initiatives have focused on the detection of potential ubiquitination sites with the aid of physicochemical test approaches in conjunction with the application of computational methods. The identification of ubiquitination sites using laboratory tests is especially susceptible to the temporality and reversibility of the ubiquitination processes, and is also costly and time-consuming. It has been demonstrated that computational methods are effective in extracting potential rules or inferences from biological sequence collections. Up to the present, the computational strategy has been one of the critical research approaches that have been applied for the identification of ubiquitination sites, and currently, there are numerous state-of-the-art computational methods that have been developed from machine learning and statistical analysis to undertake such work. In the present study, the construction of benchmark datasets is summarized, together with feature representation methods, feature selection approaches and the classifiers involved in several previous publications. In an attempt to explore pertinent development trends for the identification of ubiquitination sites, an independent test dataset was constructed and the predicting results obtained from five prediction tools are reported here, together with some related discussions.
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Affiliation(s)
- Lidong Wang
- College of Science, Dalian Maritime University, Dalian, China
| | - Ruijun Zhang
- College of Science, Dalian Maritime University, Dalian, China
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Huang KY, Lee TY, Kao HJ, Ma CT, Lee CC, Lin TH, Chang WC, Huang HD. dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications. Nucleic Acids Res 2020; 47:D298-D308. [PMID: 30418626 PMCID: PMC6323979 DOI: 10.1093/nar/gky1074] [Citation(s) in RCA: 146] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 10/19/2018] [Indexed: 12/25/2022] Open
Abstract
The dbPTM (http://dbPTM.mbc.nctu.edu.tw/) has been maintained for over 10 years with the aim to provide functional and structural analyses for post-translational modifications (PTMs). In this update, dbPTM not only integrates more experimentally validated PTMs from available databases and through manual curation of literature but also provides PTM-disease associations based on non-synonymous single nucleotide polymorphisms (nsSNPs). The high-throughput deep sequencing technology has led to a surge in the data generated through analysis of association between SNPs and diseases, both in terms of growth amount and scope. This update thus integrated disease-associated nsSNPs from dbSNP based on genome-wide association studies. The PTM substrate sites located at a specified distance in terms of the amino acids encoded from nsSNPs were deemed to have an association with the involved diseases. In recent years, increasing evidence for crosstalk between PTMs has been reported. Although mass spectrometry-based proteomics has substantially improved our knowledge about substrate site specificity of single PTMs, the fact that the crosstalk of combinatorial PTMs may act in concert with the regulation of protein function and activity is neglected. Because of the relatively limited information about concurrent frequency and functional relevance of PTM crosstalk, in this update, the PTM sites neighboring other PTM sites in a specified window length were subjected to motif discovery and functional enrichment analysis. This update highlights the current challenges in PTM crosstalk investigation and breaks the bottleneck of how proteomics may contribute to understanding PTM codes, revealing the next level of data complexity and proteomic limitation in prospective PTM research.
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Affiliation(s)
- Kai-Yao Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Science, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Science, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Hui-Ju Kao
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
| | - Chen-Tse Ma
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
| | - Chao-Chun Lee
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
| | - Tsai-Hsuan Lin
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
| | - Wen-Chi Chang
- Institute of Tropical Plant Sciences, College of Biosciences and Biotechnology, National Cheng Kung University, Tainan 70101, Taiwan
| | - Hsien-Da Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Science, The Chinese University of Hong Kong, Shenzhen 518172, China
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Huang KY, Hsu JBK, Lee TY. Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method. Sci Rep 2019; 9:16175. [PMID: 31700141 PMCID: PMC6838336 DOI: 10.1038/s41598-019-52552-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 10/18/2019] [Indexed: 12/14/2022] Open
Abstract
Succinylation is a type of protein post-translational modification (PTM), which can play important roles in a variety of cellular processes. Due to an increasing number of site-specific succinylated peptides obtained from high-throughput mass spectrometry (MS), various tools have been developed for computationally identifying succinylated sites on proteins. However, most of these tools predict succinylation sites based on traditional machine learning methods. Hence, this work aimed to carry out the succinylation site prediction based on a deep learning model. The abundance of MS-verified succinylated peptides enabled the investigation of substrate site specificity of succinylation sites through sequence-based attributes, such as position-specific amino acid composition, the composition of k-spaced amino acid pairs (CKSAAP), and position-specific scoring matrix (PSSM). Additionally, the maximal dependence decomposition (MDD) was adopted to detect the substrate signatures of lysine succinylation sites by dividing all succinylated sequences into several groups with conserved substrate motifs. According to the results of ten-fold cross-validation, the deep learning model trained using PSSM and informative CKSAAP attributes can reach the best predictive performance and also perform better than traditional machine-learning methods. Moreover, an independent testing dataset that truly did not exist in the training dataset was used to compare the proposed method with six existing prediction tools. The testing dataset comprised of 218 positive and 2621 negative instances, and the proposed model could yield a promising performance with 84.40% sensitivity, 86.99% specificity, 86.79% accuracy, and an MCC value of 0.489. Finally, the proposed method has been implemented as a web-based prediction tool (CNN-SuccSite), which is now freely accessible at http://csb.cse.yzu.edu.tw/CNN-SuccSite/.
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Affiliation(s)
- Kai-Yao Huang
- Department of Medical Research, Hsinchu Mackay Memorial Hospital, Hsinchu city, 300, Taiwan
| | - Justin Bo-Kai Hsu
- Department of Medical Research, Taipei Medical University Hospital, Taipei city, 110, Taiwan
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 518172, China. .,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, 518172, China.
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Ning Q, Yu M, Ji J, Ma Z, Zhao X. Analysis and prediction of human acetylation using a cascade classifier based on support vector machine. BMC Bioinformatics 2019; 20:346. [PMID: 31208321 PMCID: PMC6580503 DOI: 10.1186/s12859-019-2938-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 06/06/2019] [Indexed: 12/24/2022] Open
Abstract
Background Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in proteins accurately. Computational methods are popular because they are more convenient and faster than experimental methods. In this study, we proposed a new computational method to predict acetylation sites in human by combining sequence features and structural features including physicochemical property (PCP), position specific score matrix (PSSM), auto covariation (AC), residue composition (RC), secondary structure (SS) and accessible surface area (ASA), which can well characterize the information of acetylated lysine sites. Besides, a two-step feature selection was applied, which combined mRMR and IFS. It finally trained a cascade classifier based on SVM, which successfully solved the imbalance between positive samples and negative samples and covered all negative sample information. Results The performance of this method is measured with a specificity of 72.19% and a sensibility of 76.71% on independent dataset which shows that a cascade SVM classifier outperforms single SVM classifier. Conclusions In addition to the analysis of experimental results, we also made a systematic and comprehensive analysis of the acetylation data.
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Affiliation(s)
- Qiao Ning
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Miao Yu
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Jinchao Ji
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.
| | - Xiaowei Zhao
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.
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13
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Xu Y, Yang Y, Wang H, Shao Y. Lysine Malonylation Identification in E. coli with Multiple Features. CURR PROTEOMICS 2019. [DOI: 10.2174/1570164615666181005104614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Motivation: Lysine malonylation in eukaryote proteins had been found in 2011 through
high-throughput proteomic analysis. However, it was poorly understood in prokaryotes. Recent researches
have shown that maonylation in E. coli was significantly enriched in protein translation, energy
metabolism pathways and fatty acid biosynthesis.
Results:
In this work we proposed a predictor to identify the lysine malonylation sites in E. coli
through physicochemical properties, binary code and sequence frequency by support vector machine
algorithm. The experimentally determined lysine malonylation sites were retrieved from the first and
largest malonylome dataset in prokaryotes up to date. The physicochemical properties plus position
specific amino acid sequence propensity features got the best results with AUC (the area under the
Receive Operating Character curve) 0.7994, MCC (Mathew correlation coefficient) 0.4335 in 10-fold
cross-validation. Meanwhile the AUC values were 0.7800, 0.7851 and 0.8050 in 6-fold, 8-fold and
LOO (leave-one-out) cross-validation, respectively. All the ROC curves were close to each other
which illustrated the robustness and performance of the proposed predictor. We also analyzed the sequence
propensities through TwoSampleLogo and found some peptides differences with t-test p<0.01.
The predictor had shown better results than those of other methods K-Nearest Neighbors, C4.5
decision tree, Naïve Bayes and Random Forest. Functional analysis showed that malonylated proteins
were involved in many transcription activities and diverse biological processes. Meanwhile we also
developed an online package which could be freely downloaded https://github.com/Sunmile/
Malonylation E.coli.
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Affiliation(s)
- Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China
| | - Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China
| | - Hui Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
| | - Yuanhai Shao
- School of Economics and Management, Hainan University, Haikou 570228, China
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14
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Wu M, Yang Y, Wang H, Xu Y. A deep learning method to more accurately recall known lysine acetylation sites. BMC Bioinformatics 2019; 20:49. [PMID: 30674277 PMCID: PMC6343287 DOI: 10.1186/s12859-019-2632-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 01/16/2019] [Indexed: 12/11/2022] Open
Abstract
Background Lysine acetylation in protein is one of the most important post-translational modifications (PTMs). It plays an important role in essential biological processes and is related to various diseases. To obtain a comprehensive understanding of regulatory mechanism of lysine acetylation, the key is to identify lysine acetylation sites. Previously, several shallow machine learning algorithms had been applied to predict lysine modification sites in proteins. However, shallow machine learning has some disadvantages. For instance, it is not as effective as deep learning for processing big data. Results In this work, a novel predictor named DeepAcet was developed to predict acetylation sites. Six encoding schemes were adopted, including a one-hot, BLOSUM62 matrix, a composition of K-space amino acid pairs, information gain, physicochemical properties, and a position specific scoring matrix to represent the modified residues. A multilayer perceptron (MLP) was utilized to construct a model to predict lysine acetylation sites in proteins with many different features. We also integrated all features and implemented the feature selection method to select a feature set that contained 2199 features. As a result, the best prediction achieved 84.95% accuracy, 83.45% specificity, 86.44% sensitivity, 0.8540 AUC, and 0.6993 MCC in a 10-fold cross-validation. For an independent test set, the prediction achieved 84.87% accuracy, 83.46% specificity, 86.28% sensitivity, 0.8407 AUC, and 0.6977 MCC. Conclusion The predictive performance of our DeepAcet is better than that of other existing methods. DeepAcet can be freely downloaded from https://github.com/Sunmile/DeepAcet. Electronic supplementary material The online version of this article (10.1186/s12859-019-2632-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Meiqi Wu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Hui Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China. .,Beijing Key Laboratory for Magneto-photoelectrical Composite and Interface Science, University of Science and Technology Beijing, Beijing, 100083, China.
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15
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Chen G, Cao M, Yu J, Guo X, Shi S. Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC. J Theor Biol 2019; 461:92-101. [DOI: 10.1016/j.jtbi.2018.10.047] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 10/09/2018] [Accepted: 10/22/2018] [Indexed: 12/12/2022]
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16
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Lau BYC, Othman A, Ramli US. Application of Proteomics Technologies in Oil Palm Research. Protein J 2018; 37:473-499. [DOI: 10.1007/s10930-018-9802-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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17
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Yang Y, Wang H, Ding J, Xu Y. iAcet-Sumo: Identification of lysine acetylation and sumoylation sites in proteins by multi-class transformation methods. Comput Biol Med 2018; 100:144-151. [DOI: 10.1016/j.compbiomed.2018.07.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/30/2018] [Accepted: 07/08/2018] [Indexed: 11/16/2022]
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18
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Chen G, Cao M, Luo K, Wang L, Wen P, Shi S. ProAcePred: prokaryote lysine acetylation sites prediction based on elastic net feature optimization. Bioinformatics 2018; 34:3999-4006. [DOI: 10.1093/bioinformatics/bty444] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 05/30/2018] [Indexed: 02/02/2023] Open
Affiliation(s)
- Guodong Chen
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences
| | - Man Cao
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences
| | - Kun Luo
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences
| | - Lina Wang
- Department of Chemistry, College of Chemistry, Nanchang University, Nanchang, China
| | - Pingping Wen
- Department of Chemistry, College of Chemistry, Nanchang University, Nanchang, China
| | - Shaoping Shi
- Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences
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19
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Ahmed MS, Shahjaman M, Kabir E, Kamruzzaman M. Prediction of Protein Acetylation Sites using Kernel Naive Bayes Classifier Based on Protein Sequences Profiling. Bioinformation 2018; 14:213-218. [PMID: 30108418 PMCID: PMC6077816 DOI: 10.6026/97320630014213] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 04/29/2018] [Accepted: 04/29/2018] [Indexed: 12/11/2022] Open
Abstract
Lysine acetylation is one of the decisive categories of protein post-translational modification (PTM), it is convoluted in many significant cellular developments and severe diseases in the biological system. The experimental identification of protein-acetylated sites is painstaking, time-consuming and expensive. Hence, there is significant interest in the development of computational approaches for consistent prediction of acetylation sites using protein sequences. Features selection from protein sequences plays a significant role for acetylation sites prediction. We describe an improved feature selection approach for acetylation sites prediction based on kernel naive Bayes classifier (KNBC). We have shown that KNBC generated from selected features by a new feature selection method outperforms than the existing methods for identification of acetylation sites. The sensitivity, specificity, ACC (Accuracy), MCC (Matthews Correlation Coefficient) and AUC (Area under Curve of ROC) in our proposed method are as follows 80.71%, 93.39%, 76.73%, 41.37% and 83.0% with the optimum window size is 47. Thus the kernel naive Bayes classifier finds application in acetylation site prediction.
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Affiliation(s)
- Md. Shakil Ahmed
- Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
| | - Md. Shahjaman
- Department of Statistics, Begum Rokeya University, Rangpur-5400, Bangladesh
| | - Enamul Kabir
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia
| | - Md. Kamruzzaman
- Data Science for Knowledge Creation Research Center, Seoul National University, Korea
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20
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Audagnotto M, Dal Peraro M. Protein post-translational modifications: In silico prediction tools and molecular modeling. Comput Struct Biotechnol J 2017; 15:307-319. [PMID: 28458782 PMCID: PMC5397102 DOI: 10.1016/j.csbj.2017.03.004] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2016] [Revised: 03/17/2017] [Accepted: 03/21/2017] [Indexed: 02/09/2023] Open
Abstract
Post-translational modifications (PTMs) occur in almost all proteins and play an important role in numerous biological processes by significantly affecting proteins' structure and dynamics. Several computational approaches have been developed to study PTMs (e.g., phosphorylation, sumoylation or palmitoylation) showing the importance of these techniques in predicting modified sites that can be further investigated with experimental approaches. In this review, we summarize some of the available online platforms and their contribution in the study of PTMs. Moreover, we discuss the emerging capabilities of molecular modeling and simulation that are able to complement these bioinformatics methods, providing deeper molecular insights into the biological function of post-translational modified proteins.
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Affiliation(s)
- Martina Audagnotto
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Matteo Dal Peraro
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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21
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Nguyen VN, Huang KY, Huang CH, Lai KR, Lee TY. A New Scheme to Characterize and Identify Protein Ubiquitination Sites. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:393-403. [PMID: 26887002 DOI: 10.1109/tcbb.2016.2520939] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Protein ubiquitination, involving the conjugation of ubiquitin on lysine residue, serves as an important modulator of many cellular functions in eukaryotes. Recent advancements in proteomic technology have stimulated increasing interest in identifying ubiquitination sites. However, most computational tools for predicting ubiquitination sites are focused on small-scale data. With an increasing number of experimentally verified ubiquitination sites, we were motivated to design a predictive model for identifying lysine ubiquitination sites for large-scale proteome dataset. This work assessed not only single features, such as amino acid composition (AAC), amino acid pair composition (AAPC) and evolutionary information, but also the effectiveness of incorporating two or more features into a hybrid approach to model construction. The support vector machine (SVM) was applied to generate the prediction models for ubiquitination site identification. Evaluation by five-fold cross-validation showed that the SVM models learned from the combination of hybrid features delivered a better prediction performance. Additionally, a motif discovery tool, MDDLogo, was adopted to characterize the potential substrate motifs of ubiquitination sites. The SVM models integrating the MDDLogo-identified substrate motifs could yield an average accuracy of 68.70 percent. Furthermore, the independent testing result showed that the MDDLogo-clustered SVM models could provide a promising accuracy (78.50 percent) and perform better than other prediction tools. Two cases have demonstrated the effective prediction of ubiquitination sites with corresponding substrate motifs.
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22
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Tatjewski M, Kierczak M, Plewczynski D. Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices. Methods Mol Biol 2017; 1484:275-300. [PMID: 27787833 DOI: 10.1007/978-1-4939-6406-2_19] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Here, we present two perspectives on the task of predicting post translational modifications (PTMs) from local sequence fragments using machine learning algorithms. The first is the description of the fundamental steps required to construct a PTM predictor from the very beginning. These steps include data gathering, feature extraction, or machine-learning classifier selection. The second part of our work contains the detailed discussion of more advanced problems which are encountered in PTM prediction task. Probably the most challenging issues which we have covered here are: (1) how to address the training data class imbalance problem (we also present statistics describing the problem); (2) how to properly set up cross-validation folds with an approach which takes into account the homology of protein data records, to address this problem we present our folds-over-clusters algorithm; and (3) how to efficiently reach for new sources of learning features. Presented techniques and notes resulted from intense studies in the field, performed by our and other groups, and can be useful both for researchers beginning in the field of PTM prediction and for those who want to extend the repertoire of their research techniques.
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Affiliation(s)
- Marcin Tatjewski
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland
- Centre of New Technologies, University of Warsaw, S. Banacha 2c, 02-097, Warsaw, Poland
| | - Marcin Kierczak
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Dariusz Plewczynski
- Centre of New Technologies, University of Warsaw, S. Banacha 2c, Warsaw, 02-097, Poland.
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23
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Patel K, Singh M, Gowda H. Bioinformatics Methods to Deduce Biological Interpretation from Proteomics Data. Methods Mol Biol 2017; 1549:147-161. [PMID: 27975290 DOI: 10.1007/978-1-4939-6740-7_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
High-throughput proteomics studies generate large amounts of data. Biological interpretation of these large scale datasets is often challenging. Over the years, several computational tools have been developed to facilitate meaningful interpretation of large-scale proteomics data. In this chapter, we describe various analyses that can be performed and bioinformatics tools and resources that enable users to do the analyses. Many Web-based and stand-alone tools are relatively user-friendly and can be used by most biologists without significant assistance.
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Affiliation(s)
- Krishna Patel
- Institute of Bioinformatics, Discoverer Building, International Technology Park, Whitefield, Bangalore, 560066, India
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, India
| | - Manika Singh
- Institute of Bioinformatics, Discoverer Building, International Technology Park, Whitefield, Bangalore, 560066, India
| | - Harsha Gowda
- Institute of Bioinformatics, Discoverer Building, International Technology Park, Whitefield, Bangalore, 560066, India.
- YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore, India.
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24
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GPS-PAIL: prediction of lysine acetyltransferase-specific modification sites from protein sequences. Sci Rep 2016; 6:39787. [PMID: 28004786 PMCID: PMC5177928 DOI: 10.1038/srep39787] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 11/28/2016] [Indexed: 01/02/2023] Open
Abstract
Protein acetylation catalyzed by specific histone acetyltransferases (HATs) is an essential post-translational modification (PTM) and involved in the regulation a broad spectrum of biological processes in eukaryotes. Although several ten thousands of acetylation sites have been experimentally identified, the upstream HATs for most of the sites are unclear. Thus, the identification of HAT-specific acetylation sites is fundamental for understanding the regulatory mechanisms of protein acetylation. In this work, we first collected 702 known HAT-specific acetylation sites of 205 proteins from the literature and public data resources, and a motif-based analysis demonstrated that different types of HATs exhibit similar but considerably distinct sequence preferences for substrate recognition. Using 544 human HAT-specific sites for training, we constructed a highly useful tool of GPS-PAIL for the prediction of HAT-specific sites for up to seven HATs, including CREBBP, EP300, HAT1, KAT2A, KAT2B, KAT5 and KAT8. The prediction accuracy of GPS-PAIL was critically evaluated, with a satisfying performance. Using GPS-PAIL, we also performed a large-scale prediction of potential HATs for known acetylation sites identified from high-throughput experiments in nine eukaryotes. Both online service and local packages were implemented, and GPS-PAIL is freely available at: http://pail.biocuckoo.org.
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25
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Trost B, Maleki F, Kusalik A, Napper S. DAPPLE 2: a Tool for the Homology-Based Prediction of Post-Translational Modification Sites. J Proteome Res 2016; 15:2760-7. [PMID: 27367363 DOI: 10.1021/acs.jproteome.6b00304] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The post-translational modification of proteins is critical for regulating their function. Although many post-translational modification sites have been experimentally determined, particularly in certain model organisms, experimental knowledge of these sites is severely lacking for many species. Thus, it is important to be able to predict sites of post-translational modification in such species. Previously, we described DAPPLE, a tool that facilitates the homology-based prediction of one particular post-translational modification, phosphorylation, in an organism of interest using known phosphorylation sites from other organisms. Here, we describe DAPPLE 2, which expands and improves upon DAPPLE in three major ways. First, it predicts sites for many post-translational modifications (20 different types) using data from several sources (15 online databases). Second, it has the ability to make predictions approximately 2-7 times faster than DAPPLE depending on the database size and the organism of interest. Third, it simplifies and accelerates the process of selecting predicted sites of interest by categorizing them based on gene ontology terms, keywords, and signaling pathways. We show that DAPPLE 2 can successfully predict known human post-translational modification sites using, as input, known sites from species that are either closely (e.g., mouse) or distantly (e.g., yeast) related to humans. DAPPLE 2 can be accessed at http://saphire.usask.ca/saphire/dapple2 .
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Affiliation(s)
- Brett Trost
- Vaccine and Infectious Disease Organization, ‡Department of Computer Science, and §Department of Biochemistry, University of Saskatchewan , Saskatoon, SK S7N 5A2, Canada
| | - Farhad Maleki
- Vaccine and Infectious Disease Organization, ‡Department of Computer Science, and §Department of Biochemistry, University of Saskatchewan , Saskatoon, SK S7N 5A2, Canada
| | - Anthony Kusalik
- Vaccine and Infectious Disease Organization, ‡Department of Computer Science, and §Department of Biochemistry, University of Saskatchewan , Saskatoon, SK S7N 5A2, Canada
| | - Scott Napper
- Vaccine and Infectious Disease Organization, ‡Department of Computer Science, and §Department of Biochemistry, University of Saskatchewan , Saskatoon, SK S7N 5A2, Canada
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26
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Wuyun Q, Zheng W, Zhang Y, Ruan J, Hu G. Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set. PLoS One 2016; 11:e0155370. [PMID: 27183223 PMCID: PMC4868276 DOI: 10.1371/journal.pone.0155370] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 04/27/2016] [Indexed: 12/21/2022] Open
Abstract
Lysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Therefore, the alternative computational methods are necessary. Here, we developed a novel tool, KA-predictor, to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. We incorporated different types of features and employed an efficient feature selection on each type to form the final optimal feature set for model learning. And our predictor was highly competitive for the majority of species when compared with other methods. Feature contribution analysis indicated that HSE features, which were firstly introduced for lysine acetylation prediction, significantly improved the predictive performance. Particularly, we constructed a high-accurate structure dataset of H.sapiens from PDB to analyze the structural properties around lysine acetylation sites. Our datasets and a user-friendly local tool of KA-predictor can be freely available at http://sourceforge.net/p/ka-predictor.
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Affiliation(s)
- Qiqige Wuyun
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071
| | - Wei Zheng
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071
| | - Yanping Zhang
- Department of Mathematics, School of Science, Hebei University of Engineering, Handan, China, 056038
| | - Jishou Ruan
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China, 300071
| | - Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China, 300071
- * E-mail:
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27
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Nguyen VN, Huang KY, Weng JTY, Lai KR, Lee TY. UbiNet: an online resource for exploring the functional associations and regulatory networks of protein ubiquitylation. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw054. [PMID: 27114492 PMCID: PMC4843525 DOI: 10.1093/database/baw054] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Accepted: 03/20/2016] [Indexed: 12/19/2022]
Abstract
Protein ubiquitylation catalyzed by E3 ubiquitin ligases are crucial in the regulation of many cellular processes. Owing to the high throughput of mass spectrometry-based proteomics, a number of methods have been developed for the experimental determination of ubiquitylation sites, leading to a large collection of ubiquitylation data. However, there exist no resources for the exploration of E3-ligase-associated regulatory networks of for ubiquitylated proteins in humans. Therefore, the UbiNet database was developed to provide a full investigation of protein ubiquitylation networks by incorporating experimentally verified E3 ligases, ubiquitylated substrates and protein-protein interactions (PPIs). To date, UbiNet has accumulated 43 948 experimentally verified ubiquitylation sites from 14 692 ubiquitylated proteins of humans. Additionally, we have manually curated 499 E3 ligases as well as two E1 activating and 46 E2 conjugating enzymes. To delineate the regulatory networks among E3 ligases and ubiquitylated proteins, a total of 430 530 PPIs were integrated into UbiNet for the exploration of ubiquitylation networks with an interactive network viewer. A case study demonstrated that UbiNet was able to decipher a scheme for the ubiquitylation of tumor proteins p63 and p73 that is consistent with their functions. Although the essential role of Mdm2 in p53 regulation is well studied, UbiNet revealed that Mdm2 and additional E3 ligases might be implicated in the regulation of other tumor proteins by protein ubiquitylation. Moreover, UbiNet could identify potential substrates for a specific E3 ligase based on PPIs and substrate motifs. With limited knowledge about the mechanisms through which ubiquitylated proteins are regulated by E3 ligases, UbiNet offers users an effective means for conducting preliminary analyses of protein ubiquitylation. The UbiNet database is now freely accessible via http://csb.cse.yzu.edu.tw/UbiNet/ The content is regularly updated with the literature and newly released data.Database URL: http://csb.cse.yzu.edu.tw/UbiNet/.
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Affiliation(s)
- Van-Nui Nguyen
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan University of Information and Communication Technology, Thai Nguyen University, Vietnam and
| | - Kai-Yao Huang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan
| | - Julia Tzu-Ya Weng
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 320, Taiwan
| | - K Robert Lai
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 320, Taiwan
| | - Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 320, Taiwan
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28
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Zhao X, Ning Q, Ai M, Chai H, Yang G. Identification of S-glutathionylation sites in species-specific proteins by incorporating five sequence-derived features into the general pseudo-amino acid composition. J Theor Biol 2016; 398:96-102. [PMID: 27025952 DOI: 10.1016/j.jtbi.2016.03.030] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Revised: 02/29/2016] [Accepted: 03/17/2016] [Indexed: 11/25/2022]
Abstract
As a selective and reversible protein post-translational modification, S-glutathionylation generates mixed disulfides between glutathione (GSH) and cysteine residues, and plays an important role in regulating protein activity, stability, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-Glutathionylated sites is crucial. Experimental identification of S-glutathionylated sites is labor-intensive and time consuming, so establishing an effective computational method is much desirable due to their convenient and fast speed. Therefore, in this study, a new bioinformatics tool named SSGlu (Species-Specific identification of Protein S-glutathionylation Sites) was developed to identify species-specific protein S-glutathionylated sites, utilizing support vector machines that combine multiple sequence-derived features with a two-step feature selection. By 5-fold cross validation, the performance of SSGlu was measured with an AUC of 0.8105 and 0.8041 for Homo sapiens and Mus musculus, respectively. Additionally, SSGlu was compared with the existing methods, and the higher MCC and AUC of SSGlu demonstrated that SSGlu was very promising to predict S-glutathionylated sites. Furthermore, a site-specific analysis showed that S-glutathionylation intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, SSGlu is freely accessible at http://59.73.198.144:8080/SSGlu/.
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Affiliation(s)
- Xiaowei Zhao
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China.
| | - Qiao Ning
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
| | - Meiyue Ai
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
| | - Haiting Chai
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
| | - Guifu Yang
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
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Huang KY, Su MG, Kao HJ, Hsieh YC, Jhong JH, Cheng KH, Huang HD, Lee TY. dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins. Nucleic Acids Res 2015; 44:D435-46. [PMID: 26578568 PMCID: PMC4702878 DOI: 10.1093/nar/gkv1240] [Citation(s) in RCA: 134] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Accepted: 11/02/2015] [Indexed: 01/23/2023] Open
Abstract
Owing to the importance of the post-translational modifications (PTMs) of proteins in regulating biological processes, the dbPTM (http://dbPTM.mbc.nctu.edu.tw/) was developed as a comprehensive database of experimentally verified PTMs from several databases with annotations of potential PTMs for all UniProtKB protein entries. For this 10th anniversary of dbPTM, the updated resource provides not only a comprehensive dataset of experimentally verified PTMs, supported by the literature, but also an integrative interface for accessing all available databases and tools that are associated with PTM analysis. As well as collecting experimental PTM data from 14 public databases, this update manually curates over 12 000 modified peptides, including the emerging S-nitrosylation, S-glutathionylation and succinylation, from approximately 500 research articles, which were retrieved by text mining. As the number of available PTM prediction methods increases, this work compiles a non-homologous benchmark dataset to evaluate the predictive power of online PTM prediction tools. An increasing interest in the structural investigation of PTM substrate sites motivated the mapping of all experimental PTM peptides to protein entries of Protein Data Bank (PDB) based on database identifier and sequence identity, which enables users to examine spatially neighboring amino acids, solvent-accessible surface area and side-chain orientations for PTM substrate sites on tertiary structures. Since drug binding in PDB is annotated, this update identified over 1100 PTM sites that are associated with drug binding. The update also integrates metabolic pathways and protein-protein interactions to support the PTM network analysis for a group of proteins. Finally, the web interface is redesigned and enhanced to facilitate access to this resource.
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Affiliation(s)
- Kai-Yao Huang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Min-Gang Su
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Hui-Ju Kao
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Yun-Chung Hsieh
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Jhih-Hua Jhong
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Kuang-Hao Cheng
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Hsien-Da Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan
| | - Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 320, Taiwan
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Gianazza E, Parravicini C, Primi R, Miller I, Eberini I. In silico prediction and characterization of protein post-translational modifications. J Proteomics 2015; 134:65-75. [PMID: 26436211 DOI: 10.1016/j.jprot.2015.09.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2015] [Revised: 07/17/2015] [Accepted: 09/23/2015] [Indexed: 01/06/2023]
Abstract
This review outlines the computational approaches and procedures for predicting post translational modification (PTM)-induced changes in protein conformation and their influence on protein function(s), the latter being assessed as differential affinity in interaction with either low (ligands for receptors or transporters, substrates for enzymes) or high molecular mass molecules (proteins or nucleic acids in supramolecular assemblies). The scope for an in silico approach is discussed against a summary of the in vitro evidence on the structural and functional outcome of protein PTM.
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Affiliation(s)
- Elisabetta Gianazza
- Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Gruppo di Studio per la Proteomica e la Struttura delle Proteine, Sezione di Scienze Farmacologiche, Via Balzaretti 9, I-20133 Milan, Italy.
| | - Chiara Parravicini
- Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Laboratorio di Biochimica e Biofisica Computazionale, Sezione di Biochimica, Biofisica, Fisiologia ed Immunopatologia, Via Trentacoste, 2, I-20134 Milan, Italy
| | - Roberto Primi
- Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Laboratorio di Biochimica e Biofisica Computazionale, Sezione di Biochimica, Biofisica, Fisiologia ed Immunopatologia, Via Trentacoste, 2, I-20134 Milan, Italy
| | - Ingrid Miller
- Institut für Medizinische Biochemie, Veterinärmedizinische Universität Wien, Veterinärplatz 1, A-1210 Vienna, Austria
| | - Ivano Eberini
- Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Laboratorio di Biochimica e Biofisica Computazionale, Sezione di Biochimica, Biofisica, Fisiologia ed Immunopatologia, Via Trentacoste, 2, I-20134 Milan, Italy
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31
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Zhang N, Zhou Y, Huang T, Zhang YC, Li BQ, Chen L, Cai YD. Discriminating between lysine sumoylation and lysine acetylation using mRMR feature selection and analysis. PLoS One 2014; 9:e107464. [PMID: 25222670 PMCID: PMC4164654 DOI: 10.1371/journal.pone.0107464] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2014] [Accepted: 08/10/2014] [Indexed: 11/18/2022] Open
Abstract
Post-translational modifications (PTMs) are crucial steps in protein synthesis and are important factors contributing to protein diversity. PTMs play important roles in the regulation of gene expression, protein stability and metabolism. Lysine residues in protein sequences have been found to be targeted for both types of PTMs: sumoylations and acetylations; however, each PTM has a different cellular role. As experimental approaches are often laborious and time consuming, it is challenging to distinguish the two types of PTMs on lysine residues using computational methods. In this study, we developed a method to discriminate between sumoylated lysine residues and acetylated residues. The method incorporated several features: PSSM conservation scores, amino acid factors, secondary structures, solvent accessibilities and disorder scores. By using the mRMR (Maximum Relevance Minimum Redundancy) method and the IFS (Incremental Feature Selection) method, an optimal feature set was selected from all of the incorporated features, with which the classifier achieved 92.14% accuracy with an MCC value of 0.7322. Analysis of the optimal feature set revealed some differences between acetylation and sumoylation. The results from our study also supported the previous finding that there exist different consensus motifs for the two types of PTMs. The results could suggest possible dominant factors governing the acetylation and sumoylation of lysine residues, shedding some light on the modification dynamics and molecular mechanisms of the two types of PTMs, and provide guidelines for experimental validations.
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Affiliation(s)
- Ning Zhang
- Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, Tianjin, P.R. China
| | - You Zhou
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, Shanghai, P. R. China
| | - Tao Huang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Yu-Chao Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, Shanghai, P. R. China
| | - Bi-Qing Li
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, P.R. China
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai, P.R. China
- * E-mail:
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An intelligent system for identifying acetylated lysine on histones and nonhistone proteins. BIOMED RESEARCH INTERNATIONAL 2014; 2014:528650. [PMID: 25147802 PMCID: PMC4132336 DOI: 10.1155/2014/528650] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Revised: 06/23/2014] [Accepted: 06/24/2014] [Indexed: 01/15/2023]
Abstract
Lysine acetylation is an important and ubiquitous posttranslational modification conserved in prokaryotes and eukaryotes. This process, which is dynamically and temporally regulated by histone acetyltransferases and deacetylases, is crucial for numerous essential biological processes such as transcriptional regulation, cellular signaling, and stress response. Since the experimental identification of lysine acetylation sites within proteins is time-consuming and laboratory-intensive, several computational approaches have been developed to identify candidates for experimental validation. In this work, acetylated protein data collected from UniProtKB were categorized into histone or nonhistone proteins. Support vector machines (SVMs) were applied to build predictive models by using amino acid pair composition (AAPC) as a feature in a histone model. We combined BLOSUM62 and AAPC features in a nonhistone model. Furthermore, using maximal dependence decomposition (MDD) clustering can enhance the performance of the model on a fivefold cross-validation evaluation to yield a sensitivity of 0.863, specificity of 0.885, accuracy of 0.880, and MCC of 0.706. Additionally, the proposed method is evaluated using independent test sets resulting in a predictive accuracy of 74%. This indicates that the performance of our method is comparable with that of other acetylation prediction methods.
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Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features. Sci Rep 2014; 4:5765. [PMID: 25042424 PMCID: PMC4104576 DOI: 10.1038/srep05765] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Accepted: 07/03/2014] [Indexed: 11/08/2022] Open
Abstract
Lysine acetylation is a reversible post-translational modification, playing an important role in cytokine signaling, transcriptional regulation, and apoptosis. To fully understand acetylation mechanisms, identification of substrates and specific acetylation sites is crucial. Experimental identification is often time-consuming and expensive. Alternative bioinformatics methods are cost-effective and can be used in a high-throughput manner to generate relatively precise predictions. Here we develop a method termed as SSPKA for species-specific lysine acetylation prediction, using random forest classifiers that combine sequence-derived and functional features with two-step feature selection. Feature importance analysis indicates functional features, applied for lysine acetylation site prediction for the first time, significantly improve the predictive performance. We apply the SSPKA model to screen the entire human proteome and identify many high-confidence putative substrates that are not previously identified. The results along with the implemented Java tool, serve as useful resources to elucidate the mechanism of lysine acetylation and facilitate hypothesis-driven experimental design and validation.
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Hou T, Zheng G, Zhang P, Jia J, Li J, Xie L, Wei C, Li Y. LAceP: lysine acetylation site prediction using logistic regression classifiers. PLoS One 2014; 9:e89575. [PMID: 24586884 PMCID: PMC3930742 DOI: 10.1371/journal.pone.0089575] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Accepted: 01/22/2014] [Indexed: 11/19/2022] Open
Abstract
Background Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Those methods are not suitable to identify a large number of acetylated sites quickly. Therefore, computational methods are still very valuable to accelerate lysine acetylated site finding. Result In this study, many biological characteristics of acetylated sites have been investigated, such as the amino acid sequence around the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids. A logistic regression method was then utilized to integrate these information for generating a novel lysine acetylation prediction system named LAceP. When compared with existing methods, LAceP overwhelms most of state-of-the-art methods. Especially, LAceP has a more balanced prediction capability for positive and negative datasets. Conclusion LAceP can integrate different biological features to predict lysine acetylation with high accuracy. An online web server is freely available at http://www.scbit.org/iPTM/.
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Affiliation(s)
- Ting Hou
- School of Biological Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Guangyong Zheng
- CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- * E-mail: (GZ); (CCW); (YXL)
| | - Pingyu Zhang
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jia Jia
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Jing Li
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Chaochun Wei
- Shanghai Center for Bioinformation Technology, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- * E-mail: (GZ); (CCW); (YXL)
| | - Yixue Li
- School of Biological Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- * E-mail: (GZ); (CCW); (YXL)
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35
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Chen X, Qiu JD, Shi SP, Suo SB, Huang SY, Liang RP. Incorporating key position and amino acid residue features to identify general and species-specific Ubiquitin conjugation sites. Bioinformatics 2013; 29:1614-22. [PMID: 23626001 DOI: 10.1093/bioinformatics/btt196] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Systematic dissection of the ubiquitylation proteome is emerging as an appealing but challenging research topic because of the significant roles ubiquitylation play not only in protein degradation but also in many other cellular functions. High-throughput experimental studies using mass spectrometry have identified many ubiquitylation sites, primarily from eukaryotes. However, the vast majority of ubiquitylation sites remain undiscovered, even in well-studied systems. Because mass spectrometry-based experimental approaches for identifying ubiquitylation events are costly, time-consuming and biased toward abundant proteins and proteotypic peptides, in silico prediction of ubiquitylation sites is a potentially useful alternative strategy for whole proteome annotation. Because of various limitations, current ubiquitylation site prediction tools were not well designed to comprehensively assess proteomes. RESULTS We present a novel tool known as UbiProber, specifically designed for large-scale predictions of both general and species-specific ubiquitylation sites. We collected proteomics data for ubiquitylation from multiple species from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates the information from key positions and key amino acid residues. Cross-validation tests reveal that UbiProber achieves some improvement over existing tools in predicting species-specific ubiquitylation sites. Moreover, independent tests show that UbiProber improves the areas under receiver operating characteristic curves by ~15% by using the Combined model. AVAILABILITY The UbiProber server is freely available on the web at http://bioinfo.ncu.edu.cn/UbiProber.aspx. The software system of UbiProber can be downloaded at the same site. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiang Chen
- Department of Chemistry, Nanchang University, Nanchang 330031, People's Republic of China
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Shao J, Xu D, Hu L, Kwan YW, Wang Y, Kong X, Ngai SM. Systematic analysis of human lysine acetylation proteins and accurate prediction of human lysine acetylation through bi-relative adapted binomial score Bayes feature representation. MOLECULAR BIOSYSTEMS 2013; 8:2964-73. [PMID: 22936054 DOI: 10.1039/c2mb25251a] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Lysine acetylation is a reversible post-translational modification (PTM) which has been linked to many biological and pathological implications. Hence, localization of lysine acetylation is essential for deciphering the mechanism of such implications. Whereas many acetylated lysines in human proteins have been localized through experimental approaches in wet lab, it still fails to reach completion. In the present study, we proposed a novel feature extraction approach, bi-relative adapted binomial score Bayes (BRABSB), combined with support vector machines (SVMs) to construct a human-specific lysine acetylation predictor, which yields, on average, a sensitivity of 83.91%, a specificity of 87.25% and an accuracy of 85.58%, in the case of 5-fold cross validation experiments. Results obtained through the validation on independent data sets show that the proposed approach here outperforms other existing lysine acetylation predictors. Furthermore, due to the fact that global analysis of human lysine acetylproteins, which would ultimately facilitate the systematic investigation of the biological and pathological consequences associated with lysine acetylation events, remains to be resolved, we made an attempt to systematically analyze human lysine acetylproteins, demonstrating their diversity with respect to subcellular localization as well as biological process and predominance by "binding" in terms of molecular function. Our analysis also revealed that human lysine acetylproteins are significantly enriched in neurodegenerative disorders and cancer pathways. Remarkably, lysine acetylproteins in mitochondria are significantly related to neurodegenerative disorders and those in the nucleus are instead significantly involved in pathways in cancers, all of which might ultimately provide novel global insights into such pathological processes for the therapeutic purpose. The web server is deployed at http://www.bioinfo.bio.cuhk.edu.hk/bpbphka.
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Affiliation(s)
- Jianlin Shao
- Institute of Health Sciences, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
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EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou's PseAAC. J Comput Aided Mol Des 2013; 27:91-103. [PMID: 23283513 DOI: 10.1007/s10822-012-9628-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2012] [Accepted: 12/17/2012] [Indexed: 01/25/2023]
Abstract
The function of a protein is generally related to its subcellular localization. Therefore, knowing its subcellular localization is helpful in understanding its potential functions and roles in biological processes. This work develops a hybrid method for computationally predicting the subcellular localization of eukaryotic protein. The method is called EuLoc and incorporates the Hidden Markov Model (HMM) method, homology search approach and the support vector machines (SVM) method by fusing several new features into Chou's pseudo-amino acid composition. The proposed SVM module overcomes the shortcoming of the homology search approach in predicting the subcellular localization of a protein which only finds low-homologous or non-homologous sequences in a protein subcellular localization annotated database. The proposed HMM modules overcome the shortcoming of SVM in predicting subcellular localizations using few data on protein sequences. Several features of a protein sequence are considered, including the sequence-based features, the biological features derived from PROSITE, NLSdb and Pfam, the post-transcriptional modification features and others. The overall accuracy and location accuracy of EuLoc are 90.5 and 91.2 %, respectively, revealing a better predictive performance than obtained elsewhere. Although the amounts of data of the various subcellular location groups in benchmark dataset differ markedly, the accuracies of 12 subcellular localizations of EuLoc range from 82.5 to 100 %, indicating that this tool is much more balanced than other tools. EuLoc offers a high, balanced predictive power for each subcellular localization. EuLoc is now available on the web at http://euloc.mbc.nctu.edu.tw/.
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Zhang JS. Role of lysine acetylation of proteins in the pathogenesis of hepatic fibrosis. Shijie Huaren Xiaohua Zazhi 2012; 20:3621-3624. [DOI: 10.11569/wcjd.v20.i36.3621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Protein acetylation is a widely studied covalent modification that affects gene regulation in eukaryotic cells. The post-translational ε-amino lysine acetylation of proteins is highly reversible and catalyzed by many lysine acetyltransferases (KATs). The opposing process of acetylation is deacetylation which is governed by histone deacetylases (HDACs). The ε-amino lysine acetylation is a reversible post-translational modification with the potential to rival phosphorylation and plays important roles in diverse physiological and pathological processes. Many studies have been performed on potentials of ε-amino lysine acetylation in carcinogenesis, especially in hematological malignancies. Two HDAC inhibitors, romidepsin (Istodax) and vorinostat (SAHA), have been recently approved by the US FDA to treat skin T cell lymphoma. It is expected that ε-amino lysine acetylation will be the hot spot in research on solid tumors and non-tumor diseases. Following is a review regarding the role of lysine acetylation in the pathogenesis of hepatic fibrosis.
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Lu CT, Huang KY, Su MG, Lee TY, Bretaña NA, Chang WC, Chen YJ, Chen YJ, Huang HD. DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications. Nucleic Acids Res 2012. [PMID: 23193290 PMCID: PMC3531199 DOI: 10.1093/nar/gks1229] [Citation(s) in RCA: 165] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Protein modification is an extremely important post-translational regulation that adjusts the physical and chemical properties, conformation, stability and activity of a protein; thus altering protein function. Due to the high throughput of mass spectrometry (MS)-based methods in identifying site-specific post-translational modifications (PTMs), dbPTM (http://dbPTM.mbc.nctu.edu.tw/) is updated to integrate experimental PTMs obtained from public resources as well as manually curated MS/MS peptides associated with PTMs from research articles. Version 3.0 of dbPTM aims to be an informative resource for investigating the substrate specificity of PTM sites and functional association of PTMs between substrates and their interacting proteins. In order to investigate the substrate specificity for modification sites, a newly developed statistical method has been applied to identify the significant substrate motifs for each type of PTMs containing sufficient experimental data. According to the data statistics in dbPTM, >60% of PTM sites are located in the functional domains of proteins. It is known that most PTMs can create binding sites for specific protein-interaction domains that work together for cellular function. Thus, this update integrates protein–protein interaction and domain–domain interaction to determine the functional association of PTM sites located in protein-interacting domains. Additionally, the information of structural topologies on transmembrane (TM) proteins is integrated in dbPTM in order to delineate the structural correlation between the reported PTM sites and TM topologies. To facilitate the investigation of PTMs on TM proteins, the PTM substrate sites and the structural topology are graphically represented. Also, literature information related to PTMs, orthologous conservations and substrate motifs of PTMs are also provided in the resource. Finally, this version features an improved web interface to facilitate convenient access to the resource.
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Affiliation(s)
- Cheng-Tsung Lu
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li 320, Taiwan
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Suo SB, Qiu JD, Shi SP, Sun XY, Huang SY, Chen X, Liang RP. Position-specific analysis and prediction for protein lysine acetylation based on multiple features. PLoS One 2012; 7:e49108. [PMID: 23173045 PMCID: PMC3500252 DOI: 10.1371/journal.pone.0049108] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2012] [Accepted: 10/04/2012] [Indexed: 11/17/2022] Open
Abstract
Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an initial but crucial step is the recognition of acetylated substrates and the corresponding acetylation sites. In this study, we developed a position-specific method named PSKAcePred for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples. A feature analysis showed that all features applied in this method contributed to the acetylation process. A position-specific analysis showed that the features derived from the critical neighboring residues contributed profoundly to the acetylation site determination. The detailed analysis in this paper can help us to understand more of the acetylation mechanism and can provide guidance for the related experimental validation.
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Affiliation(s)
- Sheng-Bao Suo
- Department of Chemistry, Nanchang University, Nanchang, China
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41
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Prediction of lysine post-translational modifications using bioinformatic tools. Essays Biochem 2012; 52:165-77. [PMID: 22708570 DOI: 10.1042/bse0520165] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Our understanding of the importance of lysine post-translational modifications in mediating protein function has led to a significant improvement in the experimental tools aimed at characterizing their existence. Nevertheless, it remains likely that at present we have only experimentally detected a small fraction of all lysine modification sites across the commonly studied proteomes. As a result, online computational tools aimed at predicting lysine modification sites have the potential to provide valuable insight to researchers developing hypotheses regarding these modifications. This chapter discusses the metrics and procedures used to assess predictive tools and surveys 11 online computational tools aimed at the prediction of the four most widely studied lysine post-translational modifications (acetylation, methylation, SUMOylation and ubiquitination). Analyses using unbiased testing data sets suggest that nine of the 11 lysine post-translational modification tools perform no better than random, or have false-positive rates which make them unusable by the experimental biologist, despite self-reported sensitivity and specificity values to the contrary. The implications of these findings for those using and creating lysine post-translational modification software are discussed.
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Shi SP, Qiu JD, Sun XY, Suo SB, Huang SY, Liang RP. A method to distinguish between lysine acetylation and lysine methylation from protein sequences. J Theor Biol 2012; 310:223-30. [DOI: 10.1016/j.jtbi.2012.06.030] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2012] [Revised: 05/21/2012] [Accepted: 06/25/2012] [Indexed: 01/21/2023]
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Wang X, Mi G, Wang C, Zhang Y, Li J, Guo Y, Pu X, Li M. Prediction of flavin mono-nucleotide binding sites using modified PSSM profile and ensemble support vector machine. Comput Biol Med 2012; 42:1053-9. [PMID: 22985817 DOI: 10.1016/j.compbiomed.2012.08.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2012] [Revised: 07/12/2012] [Accepted: 08/13/2012] [Indexed: 11/25/2022]
Abstract
Flavin mono-nucleotide (FMN) closely evolves in many biological processes. In this study, a computational method was proposed to identify FMN binding sites based on amino acid sequences of proteins only. A modified Position Specific Score Matrix was used to characterize the local environmental sequence information, and a visible improvement of performance was obtained. Also, the ensemble SVM was applied to solve the imbalanced data problem. Additionally, an independent dataset was built to evaluate the practical performance of the method, and a satisfactory accuracy of 87.87% was achieved. It demonstrates that the method is effective in predicting FMN-binding sites.
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Affiliation(s)
- Xia Wang
- College of Chemistry, Sichuan University, Chengdu 610064, PR China
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Shi SP, Qiu JD, Sun XY, Suo SB, Huang SY, Liang RP. PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features. MOLECULAR BIOSYSTEMS 2012; 8:1520-7. [DOI: 10.1039/c2mb05502c] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Lee TY, Lu CT, Chen SA, Bretaña NA, Cheng TH, Su MG, Huang KY. Investigation and identification of protein γ-glutamyl carboxylation sites. BMC Bioinformatics 2011; 12 Suppl 13:S10. [PMID: 22372765 PMCID: PMC3278826 DOI: 10.1186/1471-2105-12-s13-s10] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Carboxylation is a modification of glutamate (Glu) residues which occurs post-translation that is catalyzed by γ-glutamyl carboxylase in the lumen of the endoplasmic reticulum. Vitamin K is a critical co-factor in the post-translational conversion of Glu residues to γ-carboxyglutamate (Gla) residues. It has been shown that the process of carboxylation is involved in the blood clotting cascade, bone growth, and extraosseous calcification. However, studies in this field have been limited by the difficulty of experimentally studying substrate site specificity in γ-glutamyl carboxylation. In silico investigations have the potential for characterizing carboxylated sites before experiments are carried out. RESULTS Because of the importance of γ-glutamyl carboxylation in biological mechanisms, this study investigates the substrate site specificity in carboxylation sites. It considers not only the composition of amino acids that surround carboxylation sites, but also the structural characteristics of these sites, including secondary structure and solvent-accessible surface area (ASA). The explored features are used to establish a predictive model for differentiating between carboxylation sites and non-carboxylation sites. A support vector machine (SVM) is employed to establish a predictive model with various features. A five-fold cross-validation evaluation reveals that the SVM model, trained with the combined features of positional weighted matrix (PWM), amino acid composition (AAC), and ASA, yields the highest accuracy (0.892). Furthermore, an independent testing set is constructed to evaluate whether the predictive model is over-fitted to the training set. CONCLUSIONS Independent testing data that did not undergo the cross-validation process shows that the proposed model can differentiate between carboxylation sites and non-carboxylation sites. This investigation is the first to study carboxylation sites and to develop a system for identifying them. The proposed method is a practical means of preliminary analysis and greatly diminishes the total number of potential carboxylation sites requiring further experimental confirmation.
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Affiliation(s)
- Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li 320, Taiwan.
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Lu CT, Chen SA, Bretaña NA, Cheng TH, Lee TY. Carboxylator: incorporating solvent-accessible surface area for identifying protein carboxylation sites. J Comput Aided Mol Des 2011; 25:987-95. [PMID: 22038416 DOI: 10.1007/s10822-011-9477-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2011] [Accepted: 09/29/2011] [Indexed: 02/07/2023]
Abstract
In proteins, glutamate (Glu) residues are transformed into γ-carboxyglutamate (Gla) residues in a process called carboxylation. The process of protein carboxylation catalyzed by γ-glutamyl carboxylase is deemed to be important due to its involvement in biological processes such as blood clotting cascade and bone growth. There is an increasing interest within the scientific community to identify protein carboxylation sites. However, experimental identification of carboxylation sites via mass spectrometry-based methods is observed to be expensive, time-consuming, and labor-intensive. Thus, we were motivated to design a computational method for identifying protein carboxylation sites. This work aims to investigate the protein carboxylation by considering the composition of amino acids that surround modification sites. With the implication of a modified residue prefers to be accessible on the surface of a protein, the solvent-accessible surface area (ASA) around carboxylation sites is also investigated. Radial basis function network is then employed to build a predictive model using various features for identifying carboxylation sites. Based on a five-fold cross-validation evaluation, a predictive model trained using the combined features of amino acid sequence (AA20D), amino acid composition, and ASA, yields the highest accuracy at 0.874. Furthermore, an independent test done involving data not included in the cross-validation process indicates that in silico identification is a feasible means of preliminary analysis. Additionally, the predictive method presented in this work is implemented as Carboxylator ( http://csb.cse.yzu.edu.tw/Carboxylator/ ), a web-based tool for identifying carboxylated proteins with modification sites in order to help users in investigating γ-glutamyl carboxylation.
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Affiliation(s)
- Cheng-Tsung Lu
- Department of Computer Science and Engineering, Yuan Ze University, Room 3312, 135 Yuan-Tung Road, Chungli, Taoyuan, 32003 Taiwan, ROC.
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