1
|
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.
Collapse
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
| |
Collapse
|
2
|
Qin J, Huang X, Gou S, Zhang S, Gou Y, Zhang Q, Chen H, Sun L, Chen M, Liu D, Han C, Tang M, Feng Z, Niu S, Zhao L, Tu Y, Liu Z, Xuan W, Dai L, Jia D, Xue Y. Ketogenic diet reshapes cancer metabolism through lysine β-hydroxybutyrylation. Nat Metab 2024; 6:1505-1528. [PMID: 39134903 DOI: 10.1038/s42255-024-01093-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 07/02/2024] [Indexed: 08/29/2024]
Abstract
Lysine β-hydroxybutyrylation (Kbhb) is a post-translational modification induced by the ketogenic diet (KD), a diet showing therapeutic effects on multiple human diseases. Little is known how cellular processes are regulated by Kbhb. Here we show that protein Kbhb is strongly affected by the KD through a multi-omics analysis of mouse livers. Using a small training dataset with known functions, we developed a bioinformatics method for the prediction of functionally important lysine modification sites (pFunK), which revealed functionally relevant Kbhb sites on various proteins, including aldolase B (ALDOB) Lys108. KD consumption or β-hydroxybutyrate supplementation in hepatocellular carcinoma cells increases ALDOB Lys108bhb and inhibits the enzymatic activity of ALDOB. A Kbhb-mimicking mutation (p.Lys108Gln) attenuates ALDOB activity and its binding to substrate fructose-1,6-bisphosphate, inhibits mammalian target of rapamycin signalling and glycolysis, and markedly suppresses cancer cell proliferation. Our study reveals a critical role of Kbhb in regulating cancer cell metabolism and provides a generally applicable algorithm for predicting functionally important lysine modification sites.
Collapse
Affiliation(s)
- Junhong Qin
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Xinhe Huang
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Shengsong Gou
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Sitao Zhang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Yujie Gou
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Zhang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Hongyu Chen
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Lin Sun
- Frontiers Science Center for Synthetic Biology, Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Tianjin University, Tianjin, China
| | - Miaomiao Chen
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Dan Liu
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Cheng Han
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Min Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Zihao Feng
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Shenghui Niu
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Lin Zhao
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Yingfeng Tu
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Zexian Liu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China
| | - Weimin Xuan
- Frontiers Science Center for Synthetic Biology, Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Tianjin University, Tianjin, China
| | - Lunzhi Dai
- National Clinical Research Center for Geriatrics and Department of General Practice, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center of Biotherapy, Chengdu, China
| | - Da Jia
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
| | - Yu Xue
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
- Nanjing University Institute of Artificial Intelligence Biomedicine, Nanjing, China.
| |
Collapse
|
3
|
Gou Y, Liu D, Chen M, Wei Y, Huang X, Han C, Feng Z, Zhang C, Lu T, Peng D, Xue Y. GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs. Nucleic Acids Res 2024; 52:W238-W247. [PMID: 38709873 PMCID: PMC11223847 DOI: 10.1093/nar/gkae346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.
Collapse
Affiliation(s)
- Yujie Gou
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Dan Liu
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Miaomiao Chen
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yuxiang Wei
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Xinhe Huang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Cheng Han
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Zihao Feng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Chi Zhang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Teng Lu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing100190, China
| | - Di Peng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yu Xue
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Nanjing University Institute of Artificial Intelligence Biomedicine, Nanjing210031, China
| |
Collapse
|
4
|
Wang T, Chen H, Li N, Zhang B, Min H. Aqueous humor proteomics analyzed by bioinformatics and machine learning in PDR cases versus controls. Clin Proteomics 2024; 21:36. [PMID: 38764026 PMCID: PMC11103871 DOI: 10.1186/s12014-024-09481-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/07/2024] [Indexed: 05/21/2024] Open
Abstract
BACKGROUND To comprehend the complexities of pathophysiological mechanisms and molecular events that contribute to proliferative diabetic retinopathy (PDR) and evaluate the diagnostic value of aqueous humor (AH) in monitoring the onset of PDR. METHODS A cohort containing 16 PDR and 10 cataract patients and another validation cohort containing 8 PDR and 4 cataract patients were studied. AH was collected and subjected to proteomics analyses. Bioinformatics analysis and a machine learning-based pipeline called inference of biomolecular combinations with minimal bias were used to explore the functional relevance, hub proteins, and biomarkers. RESULTS Deep profiling of AH proteomes revealed several insights. First, the combination of SIAE, SEMA7A, GNS, and IGKV3D-15 and the combination of ATP6AP1, SPARCL1, and SERPINA7 could serve as surrogate protein biomarkers for monitoring PDR progression. Second, ALB, FN1, ACTB, SERPINA1, C3, and VTN acted as hub proteins in the profiling of AH proteomes. SERPINA1 was the protein with the highest correlation coefficient not only for BCVA but also for the duration of diabetes. Third, "Complement and coagulation cascades" was an important pathway for PDR development. CONCLUSIONS AH proteomics provides stable and accurate biomarkers for early warning and diagnosis of PDR. This study provides a deep understanding of the molecular mechanisms of PDR and a rich resource for optimizing PDR management.
Collapse
Affiliation(s)
- Tan Wang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Huan Chen
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Ningning Li
- Operating Room, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Bao Zhang
- Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Hanyi Min
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
- Department of Ophthalmology, Aier Eye Hospital, Tianjin University, Nankai District, Fukang Road No.102, Tianjin, China.
| |
Collapse
|
5
|
Adejor J, Tumukunde E, Li G, Lin H, Xie R, Wang S. Impact of Lysine Succinylation on the Biology of Fungi. Curr Issues Mol Biol 2024; 46:1020-1046. [PMID: 38392183 PMCID: PMC10888112 DOI: 10.3390/cimb46020065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 02/24/2024] Open
Abstract
Post-translational modifications (PTMs) play a crucial role in protein functionality and the control of various cellular processes and secondary metabolites (SMs) in fungi. Lysine succinylation (Ksuc) is an emerging protein PTM characterized by the addition of a succinyl group to a lysine residue, which induces substantial alteration in the chemical and structural properties of the affected protein. This chemical alteration is reversible, dynamic in nature, and evolutionarily conserved. Recent investigations of numerous proteins that undergo significant succinylation have underscored the potential significance of Ksuc in various biological processes, encompassing normal physiological functions and the development of certain pathological processes and metabolites. This review aims to elucidate the molecular mechanisms underlying Ksuc and its diverse functions in fungi. Both conventional investigation techniques and predictive tools for identifying Ksuc sites were also considered. A more profound comprehension of Ksuc and its impact on the biology of fungi have the potential to unveil new insights into post-translational modification and may pave the way for innovative approaches that can be applied across various clinical contexts in the management of mycotoxins.
Collapse
Affiliation(s)
- John Adejor
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Elisabeth Tumukunde
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Guoqi Li
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Hong Lin
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Rui Xie
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Shihua Wang
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| |
Collapse
|
6
|
Kumari S, Gupta R, Ambasta RK, Kumar P. Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme. Biochim Biophys Acta Rev Cancer 2023; 1878:188999. [PMID: 37858622 DOI: 10.1016/j.bbcan.2023.188999] [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: 06/30/2023] [Revised: 10/06/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023]
Abstract
Recent multi-omics studies, including proteomics, transcriptomics, genomics, and metabolomics have revealed the critical role of post-translational modifications (PTMs) in the progression and pathogenesis of Glioblastoma multiforme (GBM). Further, PTMs alter the oncogenic signaling events and offer a novel avenue in GBM therapeutics research through PTM enzymes as potential biomarkers for drug targeting. In addition, PTMs are critical regulators of chromatin architecture, gene expression, and tumor microenvironment (TME), that play a crucial function in tumorigenesis. Moreover, the implementation of artificial intelligence and machine learning algorithms enhances GBM therapeutics research through the identification of novel PTM enzymes and residues. Herein, we briefly explain the mechanism of protein modifications in GBM etiology, and in altering the biologics of GBM cells through chromatin remodeling, modulation of the TME, and signaling pathways. In addition, we highlighted the importance of PTM enzymes as therapeutic biomarkers and the role of artificial intelligence and machine learning in protein PTM prediction.
Collapse
Affiliation(s)
- Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India; School of Medicine, University of South Carolina, Columbia, SC, United States of America
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India; Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India.
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India.
| |
Collapse
|
7
|
Shang S, Liu J, Hua F. Protein acylation: mechanisms, biological functions and therapeutic targets. Signal Transduct Target Ther 2022; 7:396. [PMID: 36577755 PMCID: PMC9797573 DOI: 10.1038/s41392-022-01245-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/27/2022] [Accepted: 11/06/2022] [Indexed: 12/30/2022] Open
Abstract
Metabolic reprogramming is involved in the pathogenesis of not only cancers but also neurodegenerative diseases, cardiovascular diseases, and infectious diseases. With the progress of metabonomics and proteomics, metabolites have been found to affect protein acylations through providing acyl groups or changing the activities of acyltransferases or deacylases. Reciprocally, protein acylation is involved in key cellular processes relevant to physiology and diseases, such as protein stability, protein subcellular localization, enzyme activity, transcriptional activity, protein-protein interactions and protein-DNA interactions. Herein, we summarize the functional diversity and mechanisms of eight kinds of nonhistone protein acylations in the physiological processes and progression of several diseases. We also highlight the recent progress in the development of inhibitors for acyltransferase, deacylase, and acylation reader proteins for their potential applications in drug discovery.
Collapse
Affiliation(s)
- Shuang Shang
- grid.506261.60000 0001 0706 7839CAMS Key Laboratory of Molecular Mechanism and Target Discovery of Metabolic Disorder and Tumorigenesis, State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 100050 Beijing, P.R. China
| | - Jing Liu
- grid.506261.60000 0001 0706 7839CAMS Key Laboratory of Molecular Mechanism and Target Discovery of Metabolic Disorder and Tumorigenesis, State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 100050 Beijing, P.R. China
| | - Fang Hua
- grid.506261.60000 0001 0706 7839CAMS Key Laboratory of Molecular Mechanism and Target Discovery of Metabolic Disorder and Tumorigenesis, State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 100050 Beijing, P.R. China
| |
Collapse
|
8
|
Weigle AT, Feng J, Shukla D. Thirty years of molecular dynamics simulations on posttranslational modifications of proteins. Phys Chem Chem Phys 2022; 24:26371-26397. [PMID: 36285789 PMCID: PMC9704509 DOI: 10.1039/d2cp02883b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.
Collapse
Affiliation(s)
- Austin T Weigle
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Jiangyan Feng
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
| |
Collapse
|
9
|
Jia J, Wu G, Li M, Qiu W. pSuc-EDBAM: Predicting lysine succinylation sites in proteins based on ensemble dense blocks and an attention module. BMC Bioinformatics 2022; 23:450. [PMID: 36316638 PMCID: PMC9620660 DOI: 10.1186/s12859-022-05001-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Lysine succinylation is a newly discovered protein post-translational modifications. Predicting succinylation sites helps investigate the metabolic disease treatments. However, the biological experimental approaches are costly and inefficient, it is necessary to develop efficient computational approaches. RESULTS In this paper, we proposed a novel predictor based on ensemble dense blocks and an attention module, called as pSuc-EDBAM, which adopted one hot encoding to derive the feature maps of protein sequences, and generated the low-level feature maps through 1-D CNN. Afterward, the ensemble dense blocks were used to capture feature information at different levels in the process of feature learning. We also introduced an attention module to evaluate the importance degrees of different features. The experimental results show that Acc reaches 74.25%, and MCC reaches 0.2927 on the testing dataset, which suggest that the pSuc-EDBAM outperforms the existing predictors. CONCLUSIONS The experimental results of ten-fold cross-validation on the training dataset and independent test on the testing dataset showed that pSuc-EDBAM outperforms the existing succinylation site predictors and can predict potential succinylation sites effectively. The pSuc-EDBAM is feasible and obtains the credible predictive results, which may also provide valuable references for other related research. To make the convenience of the experimental scientists, a user-friendly web server has been established ( http://bioinfo.wugenqiang.top/pSuc-EDBAM/ ), by which the desired results can be easily obtained.
Collapse
Affiliation(s)
- Jianhua Jia
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 China
| | - Genqiang Wu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 China
| | - Meifang Li
- Computer Department, Nanchang Institute of Technology, Nanchang, 330044 China
| | - Wangren Qiu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 China
| |
Collapse
|
10
|
PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability. Int J Mol Sci 2022; 23:ijms232012385. [PMID: 36293242 PMCID: PMC9604182 DOI: 10.3390/ijms232012385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 12/03/2022] Open
Abstract
Peptide detectability is defined as the probability of identifying a peptide from a mixture of standard samples, which is a key step in protein identification and analysis. Exploring effective methods for predicting peptide detectability is helpful for disease treatment and clinical research. However, most existing computational methods for predicting peptide detectability rely on a single information. With the increasing complexity of feature representation, it is necessary to explore the influence of multivariate information on peptide detectability. Thus, we propose an ensemble deep learning method, PD-BertEDL. Bidirectional encoder representations from transformers (BERT) is introduced to capture the context information of peptides. Context information, sequence information, and physicochemical information of peptides were combined to construct the multivariate feature space of peptides. We use different deep learning methods to capture the high-quality features of different categories of peptides information and use the average fusion strategy to integrate three model prediction results to solve the heterogeneity problem and to enhance the robustness and adaptability of the model. The experimental results show that PD-BertEDL is superior to the existing prediction methods, which can effectively predict peptide detectability and provide strong support for protein identification and quantitative analysis, as well as disease treatment.
Collapse
|
11
|
Xu H, Zhao Z. NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:1002-1012. [PMID: 36526218 PMCID: PMC10025766 DOI: 10.1016/j.gpb.2022.11.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 10/27/2022] [Accepted: 11/11/2022] [Indexed: 12/15/2022]
Abstract
Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achieved high accuracy and robust performance with the average area under the curve (AUC) value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features. NetBCE substantially outperformed the conventional machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared to other tools using an independent dataset. Through investigating the output of important network modules in NetBCE, epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy. The NetBCE is freely available at https://github.com/bsml320/NetBCE.
Collapse
Affiliation(s)
- Haodong Xu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.
| |
Collapse
|
12
|
Liu X, Xu LL, Lu YP, Yang T, Gu XY, Wang L, Liu Y. Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites. Front Genet 2022; 13:1007618. [PMID: 36246655 PMCID: PMC9557156 DOI: 10.3389/fgene.2022.1007618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. Therefore, it is necessary to construct an efficient computational method to prediction the presence of Ksucc sites in protein sequences. In this study, we proposed a novel and effective predictor for the identification of Ksucc sites based on deep learning algorithms that was termed as Deep_KsuccSite. The predictor adopted Composition, Transition, and Distribution (CTD) Composition (CTDC), Enhanced Grouped Amino Acid Composition (EGAAC), Amphiphilic Pseudo-Amino Acid Composition (APAAC), and Embedding Encoding methods to encode peptides, then constructed three base classifiers using one-dimensional (1D) convolutional neural network (CNN) and 2D-CNN, and finally utilized voting method to get the final results. K-fold cross-validation and independent testing showed that Deep_KsuccSite could serve as an effective tool to identify Ksucc sites in protein sequences. In addition, the ablation experiment results based on voting, feature combination, and model architecture showed that Deep_KsuccSite could make full use of the information of different features to construct an effective classifier. Taken together, we developed Deep_KsuccSite in this study, which was based on deep learning algorithm and could achieved better prediction accuracy than current methods for lysine succinylation sites. The code and dataset involved in this methodological study are permanently available at the URL https://github.com/flyinsky6/Deep_KsuccSite.
Collapse
Affiliation(s)
- Xin Liu
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
- *Correspondence: Xin Liu, ; Liang Wang, ; Yong Liu,
| | - Lin-Lin Xu
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Ya-Ping Lu
- College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
| | - Ting Yang
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Xin-Yu Gu
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Liang Wang
- Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Xin Liu, ; Liang Wang, ; Yong Liu,
| | - Yong Liu
- Jiangsu Center for the Collaboration and Innovation of Cancer Biotherapy, Cancer Institute, Xuzhou Medical University, Xuzhou, Jiangsu, China
- *Correspondence: Xin Liu, ; Liang Wang, ; Yong Liu,
| |
Collapse
|
13
|
Xia Y, Jiang M, Luo Y, Feng G, Jia G, Zhang H, Wang P, Ge R. SuccSPred2.0: A Two-Step Model to Predict Succinylation Sites Based on Multifeature Fusion and Selection Algorithm. J Comput Biol 2022; 29:1085-1094. [PMID: 35714347 DOI: 10.1089/cmb.2022.0109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Protein succinylation is a novel type of post-translational modification in recent decade years. It played an important role in biological structure and functions verified by experiments. However, it is time consuming and laborious for the wet experimental identification of succinylation sites. Traditional technology cannot adapt to the rapid growth of the biological sequence data sets. In this study, a new computational method named SuccSPred2.0 was proposed to identify succinylation sites in the protein sequences based on multifeature fusion and maximal information coefficient (MIC) method. SuccSPred2.0 was implemented based on a two-step strategy. At first, high-dimension features were reduced by linear discriminant analysis to prevent overfitting. Subsequently, MIC method was employed to select the important features binding classifiers to predict succinylation sites. From the compared experiments on 10-fold cross-validation and independent test data sets, SuccSPred2.0 obtained promising improvements. Comparative experiments showed that SuccSPred2.0 was superior to previous tools in identifying succinylation sites in the given proteins.
Collapse
Affiliation(s)
- Yixiao Xia
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Minchao Jiang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Yizhang Luo
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Guanwen Feng
- Xi'an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Gangyong Jia
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Hua Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Pu Wang
- Computer School, Hubei University of Arts and Science, Xiangyang, China
| | - Ruiquan Ge
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| |
Collapse
|
14
|
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.
Collapse
|
15
|
Jia J, Wu G, Qiu W. pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm. Front Cell Dev Biol 2022; 10:894874. [PMID: 35686053 PMCID: PMC9170990 DOI: 10.3389/fcell.2022.894874] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Being a new type of widespread protein post-translational modifications discovered in recent years, succinylation plays a key role in protein conformational regulation and cellular function regulation. Numerous studies have shown that succinylation modifications are closely associated with the development of many diseases. In order to gain insight into the mechanism of succinylation, it is vital to identify lysine succinylation sites. However, experimental identification of succinylation sites is time-consuming and laborious, and traditional identification tools are unable to meet the rapid growth of datasets. Therefore, to solve this problem, we developed a new predictor named pSuc-FFSEA, which can predict succinylation sites in protein sequences by feature fusion and stacking ensemble algorithm. Specifically, the sequence information and physicochemical properties were first extracted using EBGW, One-Hot, continuous bag-of-words, chaos game representation, and AAF_DWT. Following that, feature selection was performed, which applied LASSO to select the optimal subset of features for the classifier, and then, stacking ensemble classifier was designed using two-layer stacking ensemble, selecting three classifiers, SVM, broad learning system and LightGBM classifier, as the base classifiers of the first layer, using logistic regression classifier as the meta classifier of the second layer. In order to further improve the model prediction accuracy and reduce the computational effort, bayesian optimization algorithm and grid search algorithm were utilized to optimize the hyperparameters of the classifier. Finally, the results of rigorous 10-fold cross-validation indicated our predictor showed excellent robustness and performed better than the previous prediction tools, which achieved an average prediction accuracy of 0.7773 ± 0.0120. Besides, for the convenience of the most experimental scientists, a user-friendly and comprehensive web-server for pSuc-FFSEA has been established at https://bio.cangmang.xyz/pSuc-FFSEA, by which one can easily obtain the expected data and results without going through the complicated mathematics.
Collapse
Affiliation(s)
- Jianhua Jia
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, China
| | - Genqiang Wu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, China
| | - Wangren Qiu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, China
| |
Collapse
|
16
|
Wang H, Zhao H, Zhang J, Han J, Liu Z. A parallel model of DenseCNN and ordered-neuron LSTM for generic and species-specific succinylation site prediction. Biotechnol Bioeng 2022; 119:1755-1767. [PMID: 35320585 DOI: 10.1002/bit.28091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 03/12/2022] [Accepted: 03/19/2022] [Indexed: 11/07/2022]
Abstract
Lysine succinylation (Ksucc) regulates various metabolic processes, participates in vital life processes, ans is involved in the occurrence and development of numerous diseases. Accurate recognition of succinylation sites can reveal underlying functional mechanisms and pathogenesis. However, most remain undetected. Moreover, a deep learning architecture focusing on generic and species-specific predictions is still lacking. Thus, we proposed a deep learning-based framework named Deep-Ksucc, combining a dense convolutional network (DenseCNN) and ordered-neuron long short-term memory (OnLSTM) in parallel, which took the cascading characteristics of sequence information and physicochemical properties as the input. The results of the generic and species-specific predictions indicated that Deep-Ksucc can identify sequence patterns of different organisms and recognize plenty of succinylation sites. The case study showed that Deep-Ksucc can serve as a reliable tool for biology verification and computer-aided recognition of succinylation sites. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Huiqing Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Hong Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jing Zhang
- Engineering Training Center, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jiale Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Zhihao Liu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| |
Collapse
|
17
|
Wang C, Tan X, Tang D, Gou Y, Han C, Ning W, Lin S, Zhang W, Chen M, Peng D, Xue Y. GPS-Uber: a hybrid-learning framework for prediction of general and E3-specific lysine ubiquitination sites. Brief Bioinform 2022; 23:6509047. [PMID: 35037020 DOI: 10.1093/bib/bbab574] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 12/11/2021] [Accepted: 12/14/2021] [Indexed: 12/13/2022] Open
Abstract
As an important post-translational modification, lysine ubiquitination participates in numerous biological processes and is involved in human diseases, whereas the site specificity of ubiquitination is mainly decided by ubiquitin-protein ligases (E3s). Although numerous ubiquitination predictors have been developed, computational prediction of E3-specific ubiquitination sites is still a great challenge. Here, we carefully reviewed the existing tools for the prediction of general ubiquitination sites. Also, we developed a tool named GPS-Uber for the prediction of general and E3-specific ubiquitination sites. From the literature, we manually collected 1311 experimentally identified site-specific E3-substrate relations, which were classified into different clusters based on corresponding E3s at different levels. To predict general ubiquitination sites, we integrated 10 types of sequence and structure features, as well as three types of algorithms including penalized logistic regression, deep neural network and convolutional neural network. Compared with other existing tools, the general model in GPS-Uber exhibited a highly competitive accuracy, with an area under curve values of 0.7649. Then, transfer learning was adopted for each E3 cluster to construct E3-specific models, and in total 112 individual E3-specific predictors were implemented. Using GPS-Uber, we conducted a systematic prediction of human cancer-associated ubiquitination events, which could be helpful for further experimental consideration. GPS-Uber will be regularly updated, and its online service is free for academic research at http://gpsuber.biocuckoo.cn/.
Collapse
Affiliation(s)
- Chenwei Wang
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiaodan Tan
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Dachao Tang
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yujie Gou
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Cheng Han
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Wanshan Ning
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Shaofeng Lin
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Weizhi Zhang
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Miaomiao Chen
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Di Peng
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yu Xue
- Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| |
Collapse
|
18
|
Lv H, Zhang Y, Wang JS, Yuan SS, Sun ZJ, Dao FY, Guan ZX, Lin H, Deng KJ. iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice. Brief Bioinform 2021; 23:6447435. [PMID: 34864888 DOI: 10.1093/bib/bbab486] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/05/2021] [Accepted: 10/23/2021] [Indexed: 12/13/2022] Open
Abstract
Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice. For each PTM-specific model, we adopted eight feature encoding schemes, including sequence-based features, physicochemical property-based features and spatial mapping information-based features. The optimal feature set was identified from each encoding, and their respective models were established. Extensive experimental results show that iRice-MS always display excellent performance on 5-fold cross-validation and independent dataset test. In addition, our novel approach provides the superiority to other existing tools in terms of AUC value. Based on the proposed model, a web server named iRice-MS was established and is freely accessible at http://lin-group.cn/server/iRice-MS.
Collapse
Affiliation(s)
- Hao Lv
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, China
| | - Jia-Shu Wang
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Shi-Shi Yuan
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Zi-Jie Sun
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Fu-Ying Dao
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Zheng-Xing Guan
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Hao Lin
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Ke-Jun Deng
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| |
Collapse
|
19
|
Sha Y, Ma C, Wei X, Liu Y, Chen Y, Li L. DeepSADPr: A hybrid-learning architecture for serine ADP-ribosylation site prediction. Methods 2021; 203:575-583. [PMID: 34560250 DOI: 10.1016/j.ymeth.2021.09.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 09/13/2021] [Accepted: 09/16/2021] [Indexed: 01/28/2023] Open
Abstract
Protein adenosine diphosphate-ribosylation (ADPr) is caused by the covalent binding of one or more ADP-ribose moieties to a target protein and regulates the biological functions of the target protein. To fully understand the regulatory mechanism of ADP-ribosylation, the essential step is the identification of the ADPr sites from the proteome. As the experimental approaches are costly and time-consuming, it is necessary to develop a computational tool to predict ADPr sites. Recently, serine has been found to be the major residue type for ADP-ribosylation but no predictor is available. In this study, we collected thousands of experimentally validated human ADPr sites on serine residue and constructed several different machine-learning classifiers. We found that the hybrid model, dubbed DeepSADPr, which integrated the one-dimensional convolutional neural network (CNN) with the One-Hot encoding approach and the word-embedding approach, compared favourably to other models in terms of both ten-fold cross-validation and independent test. Its AUC values reached 0.935 for ten-fold cross-validation. Its values of sensitivity, accuracy and Matthews's correlation coefficient reached 0.933, 0.867 and 0.740, respectively, with the fixed specificity value of 0.80. Overall, DeepSADPr is the first classifier for predicting Serine ADPr sites, which is available at http://www.bioinfogo.org/DeepSADPr.
Collapse
Affiliation(s)
- Yutong Sha
- College of Computer Science & Technology, Qingdao University, Qingdao 266071, China
| | - Chenglong Ma
- College of Life Sciences, Qingdao University, Qingdao 266071, China
| | - Xilin Wei
- College of Computer Science & Technology, Qingdao University, Qingdao 266071, China
| | - Yuhai Liu
- Dawning International Information Industry, Co., Ltd., Qingdao 266101, China
| | - Yu Chen
- College of Computer Science & Technology, Qingdao University, Qingdao 266071, China.
| | - Lei Li
- College of Computer Science & Technology, Qingdao University, Qingdao 266071, China; School of Basic Medicine, Qingdao University, Qingdao 266071, China; College of Life Sciences, Qingdao University, Qingdao 266071, China.
| |
Collapse
|
20
|
Jiang P, Ning W, Shi Y, Liu C, Mo S, Zhou H, Liu K, Guo Y. FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction. Comput Struct Biotechnol J 2021; 19:4497-4509. [PMID: 34471495 PMCID: PMC8385177 DOI: 10.1016/j.csbj.2021.08.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/05/2021] [Accepted: 08/08/2021] [Indexed: 01/04/2023] Open
Abstract
As a novel lactate-derived post-translational modification (PTM), lysine lactylation (Kla) is involved in diverse biological processes, and participates in human tumorigenesis. Identification of Kla substrates with their exact sites is crucial for revealing the molecular mechanisms of lactylation. In contrast with labor-intensive and time-consuming experimental approaches, computational prediction of Kla could provide convenience and increased speed, but is still lacking. In this work, although current identified Kla sites are limited, we constructed the first Kla benchmark dataset and developed a few-shot learning-based architecture approach to leverage the power of small datasets and reduce the impact of imbalance and overfitting. A maximum 11.7% (0.745 versus 0.667) increase of area under the curve (AUC) value was achieved in contrast to conventional machine learning methods. We conducted a comprehensive survey of the performance by combining 8 sequence-based features and 3 structure-based features and tailored a multi-feature hybrid system for synergistic combination. This system achieved >16.2% improvement of the AUC value (0.889 versus 0.765) compared with single feature-based models for the prediction of Kla sites in silico. Taken few-shot learning and hybrid system together, we present our newly designed predictor named FSL-Kla, which is not only a cutting-edge tool for Kla site profile but also could generate candidates for further experimental approaches. The webserver of FSL-Kla is freely accessible for academic research at http://kla.zbiolab.cn/.
Collapse
Affiliation(s)
- Peiran Jiang
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Wanshan Ning
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yunshu Shi
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
- Henan Provincial Cooperative Innovation Center for Cancer Chemoprevention, Zhengzhou, Henan 450001, China
| | - Chuan Liu
- State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Saijun Mo
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Haoran Zhou
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Kangdong Liu
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, Zhengzhou, Henan 450001, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Yaping Guo
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, Zhengzhou, Henan 450001, China
| |
Collapse
|
21
|
Wang C, Li X, Ning W, Gong S, Yang F, Fang C, Gong Y, Wu D, Huang M, Gou Y, Fu S, Ren Y, Yang R, Qiu Y, Xue Y, Xu Y, Zhou X. Multi-omic profiling of plasma reveals molecular alterations in children with COVID-19. Theranostics 2021; 11:8008-8026. [PMID: 34335977 PMCID: PMC8315065 DOI: 10.7150/thno.61832] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/30/2021] [Indexed: 12/15/2022] Open
Abstract
Rationale: Children usually develop less severe symptoms responding to Coronavirus Disease 2019 (COVID-19) than adults. However, little is known about the molecular alterations and pathogenesis of COVID-19 in children. Methods: We conducted plasma proteomic and metabolomic profilings of the blood samples of a cohort containing 18 COVID-19-children with mild symptoms and 12 healthy children, which were enrolled from hospital admissions and outpatients, respectively. Statistical analyses were performed to identify molecules specifically altered in COVID-19-children. We also developed a machine learning-based pipeline named inference of biomolecular combinations with minimal bias (iBM) to prioritize proteins and metabolites strongly altered in COVID-19-children, and experimentally validated the predictions. Results: By comparing to the multi-omic data in adults, we identified 44 proteins and 249 metabolites differentially altered in COVID-19-children against healthy children or COVID-19-adults. Further analyses demonstrated that both deteriorative immune response/inflammation processes and protective antioxidant or anti-inflammatory processes were markedly induced in COVID-19-children. Using iBM, we prioritized two combinations that contained 5 proteins and 5 metabolites, respectively, each exhibiting a total area under curve (AUC) value of 100% to accurately distinguish COVID-19-children from healthy children or COVID-19-adults. Further experiments validated that all the 5 proteins were up-regulated upon coronavirus infection. Interestingly, we found that the prioritized metabolites inhibited the expression of pro-inflammatory factors, and two of them, methylmalonic acid (MMA) and mannitol, also suppressed coronaviral replication, implying a protective role of these metabolites in COVID-19-children. Conclusion: The finding of a strong antagonism of deteriorative and protective effects provided new insights on the mechanism and pathogenesis of COVID-19 in children that mostly underwent mild symptoms. The identified metabolites strongly altered in COVID-19-children could serve as potential therapeutic agents of COVID-19.
Collapse
Affiliation(s)
- Chong Wang
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510120, China
- Department of Infectious Diseases, Guangzhou Women and Childrens Medical Center, Guangzhou, 510120, China
- State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy Sciences, Wuhan, Hubei 430071, China
| | - Xufang Li
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510120, China
- Department of Infectious Diseases, Guangzhou Women and Childrens Medical Center, Guangzhou, 510120, China
| | - Wanshan Ning
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Sitang Gong
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510120, China
- Department of Infectious Diseases, Guangzhou Women and Childrens Medical Center, Guangzhou, 510120, China
| | - Fengxia Yang
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510120, China
- Department of Infectious Diseases, Guangzhou Women and Childrens Medical Center, Guangzhou, 510120, China
| | - Chunxiao Fang
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510120, China
- Department of Infectious Diseases, Guangzhou Women and Childrens Medical Center, Guangzhou, 510120, China
| | - Yu Gong
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510120, China
- Department of Infectious Diseases, Guangzhou Women and Childrens Medical Center, Guangzhou, 510120, China
| | - Di Wu
- State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy Sciences, Wuhan, Hubei 430071, China
| | - Muhan Huang
- State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy Sciences, Wuhan, Hubei 430071, China
| | - Yujie Gou
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Shanshan Fu
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yujie Ren
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510120, China
- Department of Infectious Diseases, Guangzhou Women and Childrens Medical Center, Guangzhou, 510120, China
- State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy Sciences, Wuhan, Hubei 430071, China
| | - Ruyi Yang
- State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy Sciences, Wuhan, Hubei 430071, China
| | - Yang Qiu
- State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy Sciences, Wuhan, Hubei 430071, China
| | - Yu Xue
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yi Xu
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510120, China
- Department of Infectious Diseases, Guangzhou Women and Childrens Medical Center, Guangzhou, 510120, China
| | - Xi Zhou
- Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510120, China
- State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy Sciences, Wuhan, Hubei 430071, China
| |
Collapse
|
22
|
Wang H, Zhao H, Yan Z, Zhao J, Han J. MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network. Biomolecules 2021; 11:biom11060872. [PMID: 34208298 PMCID: PMC8231176 DOI: 10.3390/biom11060872] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/30/2021] [Accepted: 06/07/2021] [Indexed: 12/26/2022] Open
Abstract
Lysine succinylation is an important post-translational modification, whose abnormalities are closely related to the occurrence and development of many diseases. Therefore, exploring effective methods to identify succinylation sites is helpful for disease treatment and research of related drugs. However, most existing computational methods for the prediction of succinylation sites are still based on machine learning. With the increasing volume of data and complexity of feature representations, it is necessary to explore effective deep learning methods to recognize succinylation sites. In this paper, we propose a multilane dense convolutional attention network, MDCAN-Lys. MDCAN-Lys extracts sequence information, physicochemical properties of amino acids, and structural properties of proteins using a three-way network, and it constructs feature space. For each sub-network, MDCAN-Lys uses the cascading model of dense convolutional block and convolutional block attention module to capture feature information at different levels and improve the abstraction ability of the network. The experimental results of 10-fold cross-validation and independent testing show that MDCAN-Lys can recognize more succinylation sites, which is consistent with the conclusion of the case study. Thus, it is worthwhile to explore deep learning-based methods for the recognition of succinylation sites.
Collapse
|
23
|
Dong Y, Li P, Li P, Chen C. First comprehensive analysis of lysine succinylation in paper mulberry (Broussonetia papyrifera). BMC Genomics 2021; 22:255. [PMID: 33838656 PMCID: PMC8035759 DOI: 10.1186/s12864-021-07567-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 03/26/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Lysine succinylation is a naturally occurring post-translational modification (PTM) that is ubiquitous in organisms. Lysine succinylation plays important roles in regulating protein structure and function as well as cellular metabolism. Global lysine succinylation at the proteomic level has been identified in a variety of species; however, limited information on lysine succinylation in plant species, especially paper mulberry, is available. Paper mulberry is not only an important plant in traditional Chinese medicine, but it is also a tree species with significant economic value. Paper mulberry is found in the temperate and tropical zones of China. The present study analyzed the effects of lysine succinylation on the growth, development, and physiology of paper mulberry. RESULTS A total of 2097 lysine succinylation sites were identified in 935 proteins associated with the citric acid cycle (TCA cycle), glyoxylic acid and dicarboxylic acid metabolism, ribosomes and oxidative phosphorylation; these pathways play a role in carbon fixation in photosynthetic organisms and may be regulated by lysine succinylation. The modified proteins were distributed in multiple subcellular compartments and were involved in a wide variety of biological processes, such as photosynthesis and the Calvin-Benson cycle. CONCLUSION Lysine-succinylated proteins may play key regulatory roles in metabolism, primarily in photosynthesis and oxidative phosphorylation, as well as in many other cellular processes. In addition to the large number of succinylated proteins associated with photosynthesis and oxidative phosphorylation, some proteins associated with the TCA cycle are succinylated. Our study can serve as a reference for further proteomics studies of the downstream effects of succinylation on the physiology and biochemistry of paper mulberry.
Collapse
Affiliation(s)
- Yibo Dong
- College of Animal Science, Guizhou university, Guiyang, 550025, Guizhou, China
- Department of Plant Protection, Institute of Crop Protection, College of Agriculture, Guizhou University, Guiyang, 550025, Guizhou, China
| | - Ping Li
- Institute of Grassland Research, Sichuan Academy of Grassland Science, Chengdu, 610000, Sichuan, China
| | - Ping Li
- College of Animal Science, Guizhou university, Guiyang, 550025, Guizhou, China
| | - Chao Chen
- College of Animal Science, Guizhou university, Guiyang, 550025, Guizhou, China.
| |
Collapse
|
24
|
Dou L, Yang F, Xu L, Zou Q. A comprehensive review of the imbalance classification of protein post-translational modifications. Brief Bioinform 2021; 22:6217722. [PMID: 33834199 DOI: 10.1093/bib/bbab089] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/17/2021] [Accepted: 02/24/2021] [Indexed: 12/13/2022] Open
Abstract
Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-depth research on related biological mechanisms, disease treatments and drug design. Due to the high cost and time consumption of high-throughput sequencing techniques, developing machine learning-based predictors has been considered an effective approach to rapidly recognize potential modified sites. However, the imbalanced distribution of true and false PTM sites, namely, the data imbalance problem, largely effects the reliability and application of prediction tools. In this article, we conduct a systematic survey of the research progress in the imbalanced PTMs classification. First, we describe the modeling process in detail and outline useful data imbalance solutions. Then, we summarize the recently proposed bioinformatics tools based on imbalanced PTM data and simultaneously build a convenient website, ImClassi_PTMs (available at lab.malab.cn/∼dlj/ImbClassi_PTMs/), to facilitate the researchers to view. Moreover, we analyze the challenges of current computational predictors and propose some suggestions to improve the efficiency of imbalance learning. We hope that this work will provide comprehensive knowledge of imbalanced PTM recognition and contribute to advanced predictors in the future.
Collapse
Affiliation(s)
- Lijun Dou
- University of Electronic Science and Technology of China and the Shenzhen Polytechnic, China
| | - Fenglong Yang
- University of Electronic Science and Technology of China and the Shenzhen Polytechnic, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
25
|
Tasmia SA, Ahmed FF, Mosharaf P, Hasan M, Mollah NH. An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on Homo sapiens by Fusing Three Sequence Encoding Schemes with the Random Forest Classifier. Curr Genomics 2021; 22:122-136. [PMID: 34220299 PMCID: PMC8188582 DOI: 10.2174/1389202922666210219114211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 12/13/2020] [Accepted: 01/06/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Lysine succinylation is one of the reversible protein post-translational modifications (PTMs), which regulate the structure and function of proteins. It plays a significant role in various cellular physiologies including some diseases of human as well as many other organisms. The accurate identification of succinylation site is essential to understand the various biological functions and drug development. METHODS In this study, we developed an improved method to predict lysine succinylation sites mapping on Homo sapiens by the fusion of three encoding schemes such as binary, the composition of k-spaced amino acid pairs (CKSAAP) and amino acid composition (AAC) with the random forest (RF) classifier. The prediction performance of the proposed random forest (RF) based on the fusion model in a comparison of other candidates was investigated by using 20-fold cross-validation (CV) and two independent test datasets were collected from two different sources. RESULTS The CV results showed that the proposed predictor achieves the highest scores of sensitivity (SN) as 0.800, specificity (SP) as 0.902, accuracy (ACC) as 0.919, Mathew correlation coefficient (MCC) as 0.766 and partial AUC (pAUC) as 0.163 at a false-positive rate (FPR) = 0.10 and area under the ROC curve (AUC) as 0.958. It achieved the highest performance scores of SN as 0.811, SP as 0.902, ACC as 0.891, MCC as 0.629 and pAUC as 0.139 and AUC as 0.921 for the independent test protein set-1 and SN as 0.772, SP as 0.901, ACC as 0.836, MCC as 0.677 and pAUC as 0.141 at FPR = 0.10 and AUC as 0.923 for the independent test protein set-2. It also outperformed all the other existing prediction models. CONCLUSION The prediction performances as discussed in this article recommend that the proposed method might be a useful and encouraging computational resource for lysine succinylation site prediction in the case of human population.
Collapse
Affiliation(s)
| | | | | | | | - Nurul Haque Mollah
- Address correspondence to this author at the Bioinformatics Lab., Department of Statistics, Rajshahi University, Rajshahi-6205, Bangladesh; E-mail:
| |
Collapse
|
26
|
Jiang P, Ning W, Shi Y, Liu C, Mo S, Zhou H, Liu K, Guo Y. FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction. Comput Struct Biotechnol J 2021. [DOI: 10.1016/j.csbj.2021.08.013\] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
|