1
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Wang R, Wang Z, Li Z, Lee TY. Residue-Residue Contact Can Be a Potential Feature for the Prediction of Lysine Crotonylation Sites. Front Genet 2022; 12:788467. [PMID: 35058968 PMCID: PMC8764140 DOI: 10.3389/fgene.2021.788467] [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: 10/12/2021] [Accepted: 11/23/2021] [Indexed: 11/13/2022] Open
Abstract
Lysine crotonylation (Kcr) is involved in plenty of activities in the human body. Various technologies have been developed for Kcr prediction. Sequence-based features are typically adopted in existing methods, in which only linearly neighboring amino acid composition was considered. However, modified Kcr sites are neighbored by not only the linear-neighboring amino acid but also those spatially surrounding residues around the target site. In this paper, we have used residue-residue contact as a new feature for Kcr prediction, in which features encoded with not only linearly surrounding residues but also those spatially nearby the target site. Then, the spatial-surrounding residue was used as a new scheme for feature encoding for the first time, named residue-residue composition (RRC) and residue-residue pair composition (RRPC), which were used in supervised learning classification for Kcr prediction. As the result suggests, RRC and RRPC have achieved the best performance of RRC at an accuracy of 0.77 and an area under curve (AUC) value of 0.78, RRPC at an accuracy of 0.74, and an AUC value of 0.80. In order to show that the spatial feature is of a competitively high significance as other sequence-based features, feature selection was carried on those sequence-based features together with feature RRPC. In addition, different ranges of the surrounding amino acid compositions' radii were used for comparison of the performance. After result assessment, RRC and RRPC features have shown competitively outstanding performance as others or in some cases even around 0.20 higher in accuracy or 0.3 higher in AUC values compared with sequence-based features.
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Affiliation(s)
- Rulan Wang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China
| | - Zhongyan Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China
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2
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Li Z, Li S, Luo M, Jhong JH, Li W, Yao L, Pang Y, Wang Z, Wang R, Ma R, Yu J, Huang Y, Zhu X, Cheng Q, Feng H, Zhang J, Wang C, Hsu JBK, Chang WC, Wei FX, Huang HD, Lee TY. dbPTM in 2022: an updated database for exploring regulatory networks and functional associations of protein post-translational modifications. Nucleic Acids Res 2021; 50:D471-D479. [PMID: 34788852 PMCID: PMC8728263 DOI: 10.1093/nar/gkab1017] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/08/2021] [Accepted: 10/13/2021] [Indexed: 01/02/2023] Open
Abstract
Protein post-translational modifications (PTMs) play an important role in different cellular processes. In view of the importance of PTMs in cellular functions and the massive data accumulated by the rapid development of mass spectrometry (MS)-based proteomics, this paper presents an update of dbPTM with over 2 777 000 PTM substrate sites obtained from existing databases and manual curation of literature, of which more than 2 235 000 entries are experimentally verified. This update has manually curated over 42 new modification types that were not included in the previous version. Due to the increasing number of studies on the mechanism of PTMs in the past few years, a great deal of upstream regulatory proteins of PTM substrate sites have been revealed. The updated dbPTM thus collates regulatory information from databases and literature, and merges them into a protein-protein interaction network. To enhance the understanding of the association between PTMs and molecular functions/cellular processes, the functional annotations of PTMs are curated and integrated into the database. In addition, the existing PTM-related resources, including annotation databases and prediction tools are also renewed. Overall, in this update, we would like to provide users with the most abundant data and comprehensive annotations on PTMs of proteins. The updated dbPTM is now freely accessible at https://awi.cuhk.edu.cn/dbPTM/.
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Affiliation(s)
- Zhongyan Li
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Shangfu Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Mengqi Luo
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jhih-Hua Jhong
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Wenshuo Li
- 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
| | - Lantian Yao
- 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
| | - Yuxuan Pang
- 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
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Rulan Wang
- 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
| | - Renfei Ma
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jinhan Yu
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuqi Huang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Xiaoning Zhu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Qifan Cheng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Hexiang Feng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jiahong Zhang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chunxuan Wang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Justin Bo-Kai Hsu
- Department of Medical Research, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Wen-Chi Chang
- Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
| | - Feng-Xiang Wei
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen 518172, China.,Department of Cell Biology, Jiamusi University, Jiamusi 154007, China.,Shenzhen Children's Hospital of China Medical University, Shenzhen 518172, China
| | - Hsien-Da Huang
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
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3
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Negi V, Yang J, Speyer G, Pulgarin A, Handen A, Zhao J, Tai YY, Tang Y, Culley MK, Yu Q, Forsythe P, Gorelova A, Watson AM, Al Aaraj Y, Satoh T, Sharifi-Sanjani M, Rajaratnam A, Sembrat J, Provencher S, Yin X, Vargas SO, Rojas M, Bonnet S, Torrino S, Wagner BK, Schreiber SL, Dai M, Bertero T, Al Ghouleh I, Kim S, Chan SY. Computational repurposing of therapeutic small molecules from cancer to pulmonary hypertension. SCIENCE ADVANCES 2021; 7:eabh3794. [PMID: 34669463 PMCID: PMC8528428 DOI: 10.1126/sciadv.abh3794] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 08/27/2021] [Indexed: 05/05/2023]
Abstract
Cancer therapies are being considered for treating rare noncancerous diseases like pulmonary hypertension (PH), but effective computational screening is lacking. Via transcriptomic differential dependency analyses leveraging parallels between cancer and PH, we mapped a landscape of cancer drug functions dependent upon rewiring of PH gene clusters. Bromodomain and extra-terminal motif (BET) protein inhibitors were predicted to rely upon several gene clusters inclusive of galectin-8 (LGALS8). Correspondingly, LGALS8 was found to mediate the BET inhibitor–dependent control of endothelial apoptosis, an essential role for PH in vivo. Separately, a piperlongumine analog’s actions were predicted to depend upon the iron-sulfur biogenesis gene ISCU. Correspondingly, the analog was found to inhibit ISCU glutathionylation, rescuing oxidative metabolism, decreasing endothelial apoptosis, and improving PH. Thus, we identified crucial drug-gene axes central to endothelial dysfunction and therapeutic priorities for PH. These results establish a wide-ranging, network dependency platform to redefine cancer drugs for use in noncancerous conditions.
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Affiliation(s)
- Vinny Negi
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Jimin Yang
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Gil Speyer
- Research Computing, Arizona State University, Tempe, AZ, USA
| | - Andres Pulgarin
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Adam Handen
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Jingsi Zhao
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Yi Yin Tai
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Ying Tang
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Miranda K. Culley
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Qiujun Yu
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Patricia Forsythe
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Anastasia Gorelova
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Annie M. Watson
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Yassmin Al Aaraj
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Taijyu Satoh
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Department of Cardiovascular Medicine, Tohoku University of Graduate School of Medicine, 1-1 Seiryomachi, Aoba-ku, 980-8574 Sendai, Japan
| | - Maryam Sharifi-Sanjani
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Arun Rajaratnam
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - John Sembrat
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Steeve Provencher
- Pulmonary Hypertension and Vascular Biology Research Group, Faculty of Medicine, Laval University, Quebec, QC, Canada
| | - Xianglin Yin
- Department of Chemistry, Center for Cancer Research, Institute for Drug Discovery, Purdue University, West Lafayette, IN, USA
| | - Sara O. Vargas
- Department of Pathology, Boston Children’s Hospital, MA, USA
| | - Mauricio Rojas
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Ohio State University College of Medicine, Columbus, OH, USA
| | - Sébastien Bonnet
- Pulmonary Hypertension and Vascular Biology Research Group, Faculty of Medicine, Laval University, Quebec, QC, Canada
| | | | - Bridget K. Wagner
- Department of Chemistry and Chemical Biology, Harvard University; Chemical Biology and Therapeutics Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stuart L. Schreiber
- Department of Chemistry and Chemical Biology, Harvard University; Chemical Biology and Therapeutics Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mingji Dai
- Department of Chemistry, Center for Cancer Research, Institute for Drug Discovery, Purdue University, West Lafayette, IN, USA
| | - Thomas Bertero
- Université Côte d’Azur, CNRS, IPMC, Sophia-Antipolis, France
| | - Imad Al Ghouleh
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Stephen Y. Chan
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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4
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Anashkina AA, Poluektov YM, Dmitriev VA, Kuznetsov EN, Mitkevich VA, Makarov AA, Petrushanko IY. A novel approach for predicting protein S-glutathionylation. BMC Bioinformatics 2020; 21:282. [PMID: 32921310 PMCID: PMC7489215 DOI: 10.1186/s12859-020-03571-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 05/28/2020] [Indexed: 01/22/2023] Open
Abstract
Background S-glutathionylation is the formation of disulfide bonds between the tripeptide glutathione and cysteine residues of the protein, protecting them from irreversible oxidation and in some cases causing change in their functions. Regulatory glutathionylation of proteins is a controllable and reversible process associated with cell response to the changing redox status. Prediction of cysteine residues that undergo glutathionylation allows us to find new target proteins, which function can be altered in pathologies associated with impaired redox status. We set out to analyze this issue and create new tool for predicting S-glutathionylated cysteine residues. Results One hundred forty proteins with experimentally proven S-glutathionylated cysteine residues were found in the literature and the RedoxDB database. These proteins contain 1018 non-S-glutathionylated cysteines and 235 S-glutathionylated ones. Based on 235 S-glutathionylated cysteines, non-redundant positive dataset of 221 heptapeptide sequences of S-glutathionylated cysteines was made. Based on 221 heptapeptide sequences, a position-specific matrix was created by analyzing the protein sequence near the cysteine residue (three amino acid residues before and three after the cysteine). We propose the method for calculating the glutathionylation propensity score, which utilizes the position-specific matrix and a criterion for predicting glutathionylated peptides. Conclusion Non-S-glutathionylated sites were enriched by cysteines in − 3 and + 3 positions. The proposed prediction method demonstrates 76.6% of correct predictions of S-glutathionylated cysteines. This method can be used for detecting new glutathionylation sites, especially in proteins with an unknown structure.
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Affiliation(s)
- Anastasia A Anashkina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilov St. 32, 119991, Moscow, Russia.
| | - Yuri M Poluektov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilov St. 32, 119991, Moscow, Russia
| | - Vladimir A Dmitriev
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilov St. 32, 119991, Moscow, Russia
| | - Eugene N Kuznetsov
- V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya street, Moscow, 117997, Russia
| | - Vladimir A Mitkevich
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilov St. 32, 119991, Moscow, Russia
| | - Alexander A Makarov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilov St. 32, 119991, Moscow, Russia
| | - Irina Yu Petrushanko
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilov St. 32, 119991, Moscow, Russia.
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5
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Finelli MJ. Redox Post-translational Modifications of Protein Thiols in Brain Aging and Neurodegenerative Conditions-Focus on S-Nitrosation. Front Aging Neurosci 2020; 12:254. [PMID: 33088270 PMCID: PMC7497228 DOI: 10.3389/fnagi.2020.00254] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/24/2020] [Indexed: 12/14/2022] Open
Abstract
Reactive oxygen species and reactive nitrogen species (RONS) are by-products of aerobic metabolism. RONS trigger a signaling cascade that can be transduced through oxidation-reduction (redox)-based post-translational modifications (redox PTMs) of protein thiols. This redox signaling is essential for normal cellular physiology and coordinately regulates the function of redox-sensitive proteins. It plays a particularly important role in the brain, which is a major producer of RONS. Aberrant redox PTMs of protein thiols can impair protein function and are associated with several diseases. This mini review article aims to evaluate the role of redox PTMs of protein thiols, in particular S-nitrosation, in brain aging, and in neurodegenerative diseases. It also discusses the potential of using redox-based therapeutic approaches for neurodegenerative conditions.
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Affiliation(s)
- Mattéa J Finelli
- School of Medicine, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom
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6
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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: 138] [Impact Index Per Article: 34.5] [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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7
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Wang M, Cui X, Yu B, Chen C, Ma Q, Zhou H. SulSite-GTB: identification of protein S-sulfenylation sites by fusing multiple feature information and gradient tree boosting. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04792-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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8
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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: 23] [Impact Index Per Article: 4.6] [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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9
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VanHecke GC, Abeywardana MY, Ahn YH. Proteomic Identification of Protein Glutathionylation in Cardiomyocytes. J Proteome Res 2019; 18:1806-1818. [PMID: 30831029 DOI: 10.1021/acs.jproteome.8b00986] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Reactive oxygen species (ROS) are important signaling molecules, but their overproduction is associated with many cardiovascular diseases, including cardiomyopathy. ROS induce various oxidative modifications, among which glutathionylation is one of the significant protein oxidations that occur under oxidative stress. Despite previous efforts, direct and site-specific identification of glutathionylated proteins in cardiomyocytes has been limited. In this report, we used a clickable glutathione approach in a HL-1 mouse cardiomyocyte cell line under exposure to hydrogen peroxide, finding 1763 glutathionylated peptides with specific Cys modification sites, which include many muscle-specific proteins. Bioinformatic and cluster analyses found 125 glutathionylated proteins, whose mutations or dysfunctions are associated with cardiomyopathy, many of which include sarcomeric structural and contractile proteins, chaperone, and other signaling or regulatory proteins. We further provide functional implication of glutathionylation for several identified proteins, including CSRP3/MLP and complex I, II, and III, by analyzing glutathionylated sites in their structures. Our report establishes a chemoselective method for direct identification of glutathionylated proteins and provides potential target proteins whose glutathionylation may contribute to muscle diseases.
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Affiliation(s)
- Garrett C VanHecke
- Department of Chemistry , Wayne State University , Detroit , Michigan 48202 , United States
| | | | - Young-Hoon Ahn
- Department of Chemistry , Wayne State University , Detroit , Michigan 48202 , United States
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10
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Huang KY, Kao HJ, Hsu JBK, Weng SL, Lee TY. Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites. BMC Bioinformatics 2019; 19:384. [PMID: 30717647 PMCID: PMC7394328 DOI: 10.1186/s12859-018-2394-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 09/25/2018] [Indexed: 01/06/2023] Open
Abstract
Background Glutarylation, the addition of a glutaryl group (five carbons) to a lysine residue of a protein molecule, is an important post-translational modification and plays a regulatory role in a variety of physiological and biological processes. As the number of experimentally identified glutarylated peptides increases, it becomes imperative to investigate substrate motifs to enhance the study of protein glutarylation. We carried out a bioinformatics investigation of glutarylation sites based on amino acid composition using a public database containing information on 430 non-homologous glutarylation sites. Results The TwoSampleLogo analysis indicates that positively charged and polar amino acids surrounding glutarylated sites may be associated with the specificity in substrate site of protein glutarylation. Additionally, the chi-squared test was utilized to explore the intrinsic interdependence between two positions around glutarylation sites. Further, maximal dependence decomposition (MDD), which consists of partitioning a large-scale dataset into subgroups with statistically significant amino acid conservation, was used to capture motif signatures of glutarylation sites. We considered single features, such as amino acid composition (AAC), amino acid pair composition (AAPC), and composition of k-spaced amino acid pairs (CKSAAP), as well as the effectiveness of incorporating MDD-identified substrate motifs into an integrated prediction model. Evaluation by five-fold cross-validation showed that AAC was most effective in discriminating between glutarylation and non-glutarylation sites, according to support vector machine (SVM). Conclusions The SVM model integrating MDD-identified substrate motifs performed well, with a sensitivity of 0.677, a specificity of 0.619, an accuracy of 0.638, and a Matthews Correlation Coefficient (MCC) value of 0.28. Using an independent testing dataset (46 glutarylated and 92 non-glutarylated sites) obtained from the literature, we demonstrated that the integrated SVM model could improve the predictive performance effectively, yielding a balanced sensitivity and specificity of 0.652 and 0.739, respectively. This integrated SVM model has been implemented as a web-based system (MDDGlutar), which is now freely available at http://csb.cse.yzu.edu.tw/MDDGlutar/. Electronic supplementary material The online version of this article (10.1186/s12859-018-2394-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kai-Yao Huang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Hui-Ju Kao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 518172, China.,Department of Computer Science and Engineering, Yuan Ze University, Taoyuan city, 320, Taiwan
| | - Justin Bo-Kai Hsu
- Department of Medical Research, Taipei Medical University Hospital, Taipei city, 110, Taiwan
| | - Shun-Long Weng
- Department of Medicine, Mackay Medical College, New Taipei City, 252, Taiwan.,Mackay Medicine, Nursing and Management College, Taipei, 112, Taiwan.,Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsin-Chu, 300, Taiwan
| | - Tzong-Yi Lee
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China. .,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 518172, China.
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11
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He W, Wei L, Zou Q. Research progress in protein posttranslational modification site prediction. Brief Funct Genomics 2018; 18:220-229. [DOI: 10.1093/bfgp/ely039] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 11/15/2018] [Accepted: 11/22/2018] [Indexed: 01/24/2023] Open
Abstract
AbstractPosttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.
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Affiliation(s)
- Wenying He
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Leyi Wei
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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12
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Coenzyme A, protein CoAlation and redox regulation in mammalian cells. Biochem Soc Trans 2018; 46:721-728. [PMID: 29802218 PMCID: PMC6008590 DOI: 10.1042/bst20170506] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Revised: 03/19/2018] [Accepted: 03/21/2018] [Indexed: 12/16/2022]
Abstract
In a diverse family of cellular cofactors, coenzyme A (CoA) has a unique design to function in various biochemical processes. The presence of a highly reactive thiol group and a nucleotide moiety offers a diversity of chemical reactions and regulatory interactions. CoA employs them to activate carbonyl-containing molecules and to produce various thioester derivatives (e.g. acetyl CoA, malonyl CoA and 3-hydroxy-3-methylglutaryl CoA), which have well-established roles in cellular metabolism, production of neurotransmitters and the regulation of gene expression. A novel unconventional function of CoA in redox regulation, involving covalent attachment of this coenzyme to cellular proteins in response to oxidative and metabolic stress, has been recently discovered and termed protein CoAlation (S-thiolation by CoA or CoAthiolation). A diverse range of proteins was found to be CoAlated in mammalian cells and tissues under various experimental conditions. Protein CoAlation alters the molecular mass, charge and activity of modified proteins, and prevents them from irreversible sulfhydryl overoxidation. This review highlights the role of a key metabolic integrator CoA in redox regulation in mammalian cells and provides a perspective of the current status and future directions of the emerging field of protein CoAlation.
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13
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Storm AR, Kohler MR, Berndsen CE, Monroe JD. Glutathionylation Inhibits the Catalytic Activity of Arabidopsis β-Amylase3 but Not That of Paralog β-Amylase1. Biochemistry 2018; 57:711-721. [PMID: 29309132 DOI: 10.1021/acs.biochem.7b01274] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
β-Amylase3 (BAM3) is an enzyme that is essential for starch degradation in plant leaves and is also transcriptionally induced under cold stress. However, we recently reported that BAM3's enzymatic activity decreased in cold-stressed Arabidopsis leaves, although the activity of BAM1, a homologous leaf β-amylase, was largely unaffected. This decrease in BAM3 activity may relate to the accumulation of starch reported in cold-stressed plants. The aim of this study was to explore the disparity between BAM3 transcript and activity levels under cold stress, and we present evidence suggesting BAM3 is being inhibited by post-translational modification. A mechanism of enzyme inhibition was suggested by observing that BAM3 protein levels remained unchanged under cold stress. Cold stress induces nitric oxide (NO) signaling, one result being alteration of protein activity by nitrosylation or glutathionylation through agents such as S-nitrosoglutathione (GSNO). To test whether NO induction correlates with inhibition of BAM3 in vivo, plants were treated with sodium nitroprusside, which releases NO, and a decline in BAM3 but not BAM1 activity was again observed. Treatment of recombinant BAM3 and BAM1 with GSNO caused significant, dose-dependent inhibition of BAM3 activity while BAM1 was largely unaffected. Site-directed mutagenesis, anti-glutathione Western blots, and mass spectrometry were then used to determine that in vitro BAM3 inhibition was caused by glutathionylation at cysteine 433. In addition, we generated a BAM1 mutant resembling BAM3 that was sensitive to GSNO inhibition. These findings demonstrate a differential response of two BAM paralogs to the Cys-modifying reagent GSNO and provide a possible molecular basis for reduced BAM3 activity in cold-stressed plants.
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Affiliation(s)
- Amanda R Storm
- Department of Biology, James Madison University , Harrisonburg, Virginia 22807, United States
| | - Matthew R Kohler
- Department of Biology, James Madison University , Harrisonburg, Virginia 22807, United States
| | - Christopher E Berndsen
- Department of Chemistry and Biochemistry, James Madison University , Harrisonburg, Virginia 22807, United States
| | - Jonathan D Monroe
- Department of Biology, James Madison University , Harrisonburg, Virginia 22807, United States
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14
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Kao HJ, Weng SL, Huang KY, Kaunang FJ, Hsu JBK, Huang CH, Lee TY. MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs. BMC SYSTEMS BIOLOGY 2017; 11:137. [PMID: 29322938 PMCID: PMC5763492 DOI: 10.1186/s12918-017-0511-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background Carbonylation, which takes place through oxidation of reactive oxygen species (ROS) on specific residues, is an irreversibly oxidative modification of proteins. It has been reported that the carbonylation is related to a number of metabolic or aging diseases including diabetes, chronic lung disease, Parkinson’s disease, and Alzheimer’s disease. Due to the lack of computational methods dedicated to exploring motif signatures of protein carbonylation sites, we were motivated to exploit an iterative statistical method to characterize and identify carbonylated sites with motif signatures. Results By manually curating experimental data from research articles, we obtained 332, 144, 135, and 140 verified substrate sites for K (lysine), R (arginine), T (threonine), and P (proline) residues, respectively, from 241 carbonylated proteins. In order to examine the informative attributes for classifying between carbonylated and non-carbonylated sites, multifarious features including composition of twenty amino acids (AAC), composition of amino acid pairs (AAPC), position-specific scoring matrix (PSSM), and positional weighted matrix (PWM) were investigated in this study. Additionally, in an attempt to explore the motif signatures of carbonylation sites, an iterative statistical method was adopted to detect statistically significant dependencies of amino acid compositions between specific positions around substrate sites. Profile hidden Markov model (HMM) was then utilized to train a predictive model from each motif signature. Moreover, based on the method of support vector machine (SVM), we adopted it to construct an integrative model by combining the values of bit scores obtained from profile HMMs. The combinatorial model could provide an enhanced performance with evenly predictive sensitivity and specificity in the evaluation of cross-validation and independent testing. Conclusion This study provides a new scheme for exploring potential motif signatures at substrate sites of protein carbonylation. The usefulness of the revealed motifs in the identification of carbonylated sites is demonstrated by their effective performance in cross-validation and independent testing. Finally, these substrate motifs were adopted to build an available online resource (MDD-Carb, http://csb.cse.yzu.edu.tw/MDDCarb/) and are also anticipated to facilitate the study of large-scale carbonylated proteomes. Electronic supplementary material The online version of this article (10.1186/s12918-017-0511-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hui-Ju Kao
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, city, 320, Taiwan
| | - Shun-Long Weng
- Department of Medicine, Mackay Medical College, New Taipei City, 252, Taiwan.,Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsinchu, city, 300, Taiwan.,Mackay Junior College of Medicine, Nursing and Management, Taipei, city, 112, Taiwan
| | - Kai-Yao Huang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, city, 320, Taiwan.,Department of Medical Research, Hsinchu Mackay Memorial Hospital, Hsinchu, city, 300, Taiwan
| | - Fergie Joanda Kaunang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, city, 320, Taiwan
| | - Justin Bo-Kai Hsu
- Department of Medical Research, Taipei Medical University Hospital, Taipei, city, 110, Taiwan
| | - Chien-Hsun Huang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, city, 320, Taiwan. .,Tao-Yuan Hospital, Ministry of Health & Welfare, Taoyuan, 320, Taiwan.
| | - Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, city, 320, Taiwan. .,Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, 320, Taiwan.
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15
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Glutathione and Glutathione Transferase Omega 1 as Key Posttranslational Regulators in Macrophages. Microbiol Spectr 2017; 5. [PMID: 28102119 DOI: 10.1128/microbiolspec.mchd-0044-2016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Macrophage activation during phagocytosis or by pattern recognition receptors, such as Toll-like receptor 4, leads to the accumulation of reactive oxygen species (ROS). ROS act as a microbicidal defense mechanism, promoting clearance of infection, allowing for resolution of inflammation. Overproduction of ROS, however, overwhelms our cellular antioxidant defense system, promoting oxidation of protein machinery, leading to macrophage dysregulation and pathophysiology of chronic inflammatory conditions, such as atherosclerosis. Here we will describe the role of the antioxidant tripeptide glutathione (GSH). Until recently, the binding of GSH, termed glutathionylation, was only considered to maintain the integrity of cellular components, limiting the damaging effects of an aberrant oxidative environment. GSH can, however, have positive and negative regulatory effects on protein function in macrophages. GSH regulates protein secretion, driving tumor necrosis factor α release, hypoxia-inducible factor-1α stability, STAT3 phosphorylation, and caspase-1 activation in macrophages. GSH also plays a role in host defense against Listeria monocytogenes, modifying the key virulence protein PrfA in infected macrophages. We will also discuss glutathione transferase omega 1, a deglutathionylating enzyme recently shown to play a role in many aspects of macrophage activity, including metabolism, NF-κB activation, and cell survival pathways. Glutathionylation is emerging as a key regulatory event in macrophage biology that might be susceptible to therapeutic targeting.
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16
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Weng SL, Kao HJ, Huang CH, Lee TY. MDD-Palm: Identification of protein S-palmitoylation sites with substrate motifs based on maximal dependence decomposition. PLoS One 2017; 12:e0179529. [PMID: 28662047 PMCID: PMC5491019 DOI: 10.1371/journal.pone.0179529] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Accepted: 05/31/2017] [Indexed: 12/14/2022] Open
Abstract
S-palmitoylation, the covalent attachment of 16-carbon palmitic acids to a cysteine residue via a thioester linkage, is an important reversible lipid modification that plays a regulatory role in a variety of physiological and biological processes. As the number of experimentally identified S-palmitoylated peptides increases, it is imperative to investigate substrate motifs to facilitate the study of protein S-palmitoylation. Based on 710 non-homologous S-palmitoylation sites obtained from published databases and the literature, we carried out a bioinformatics investigation of S-palmitoylation sites based on amino acid composition. Two Sample Logo indicates that positively charged and polar amino acids surrounding S-palmitoylated sites may be associated with the substrate site specificity of protein S-palmitoylation. Additionally, maximal dependence decomposition (MDD) was applied to explore the motif signatures of S-palmitoylation sites by categorizing a large-scale dataset into subgroups with statistically significant conservation of amino acids. Single features such as amino acid composition (AAC), amino acid pair composition (AAPC), position specific scoring matrix (PSSM), position weight matrix (PWM), amino acid substitution matrix (BLOSUM62), and accessible surface area (ASA) were considered, along with the effectiveness of incorporating MDD-identified substrate motifs into a two-layered prediction model. Evaluation by five-fold cross-validation showed that a hybrid of AAC and PSSM performs best at discriminating between S-palmitoylation and non-S-palmitoylation sites, according to the support vector machine (SVM). The two-layered SVM model integrating MDD-identified substrate motifs performed well, with a sensitivity of 0.79, specificity of 0.80, accuracy of 0.80, and Matthews Correlation Coefficient (MCC) value of 0.45. Using an independent testing dataset (613 S-palmitoylated and 5412 non-S-palmitoylated sites) obtained from the literature, we demonstrated that the two-layered SVM model could outperform other prediction tools, yielding a balanced sensitivity and specificity of 0.690 and 0.694, respectively. This two-layered SVM model has been implemented as a web-based system (MDD-Palm), which is now freely available at http://csb.cse.yzu.edu.tw/MDDPalm/.
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Affiliation(s)
- Shun-Long Weng
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
- Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsinchu city, Taiwan
- Mackay Junior College of Medicine, Nursing and Management, Taipei, Taiwan
| | - Hui-Ju Kao
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan
| | - Chien-Hsun Huang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan
- Tao-Yuan Hospital, Ministry of Health & Welfare, Taoyuan, Taiwan
- * E-mail: (TYL); (CHH)
| | - Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan
- Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Taiwan
- * E-mail: (TYL); (CHH)
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17
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Weng SL, Huang KY, Kaunang FJ, Huang CH, Kao HJ, Chang TH, Wang HY, Lu JJ, Lee TY. Investigation and identification of protein carbonylation sites based on position-specific amino acid composition and physicochemical features. BMC Bioinformatics 2017; 18:66. [PMID: 28361707 PMCID: PMC5374553 DOI: 10.1186/s12859-017-1472-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Protein carbonylation, an irreversible and non-enzymatic post-translational modification (PTM), is often used as a marker of oxidative stress. When reactive oxygen species (ROS) oxidized the amino acid side chains, carbonyl (CO) groups are produced especially on Lysine (K), Arginine (R), Threonine (T), and Proline (P). Nevertheless, due to the lack of information about the carbonylated substrate specificity, we were encouraged to develop a systematic method for a comprehensive investigation of protein carbonylation sites. RESULTS After the removal of redundant data from multipe carbonylation-related articles, totally 226 carbonylated proteins in human are regarded as training dataset, which consisted of 307, 126, 128, and 129 carbonylation sites for K, R, T and P residues, respectively. To identify the useful features in predicting carbonylation sites, the linear amino acid sequence was adopted not only to build up the predictive model from training dataset, but also to compare the effectiveness of prediction with other types of features including amino acid composition (AAC), amino acid pair composition (AAPC), position-specific scoring matrix (PSSM), positional weighted matrix (PWM), solvent-accessible surface area (ASA), and physicochemical properties. The investigation of position-specific amino acid composition revealed that the positively charged amino acids (K and R) are remarkably enriched surrounding the carbonylated sites, which may play a functional role in discriminating between carbonylation and non-carbonylation sites. A variety of predictive models were built using various features and three different machine learning methods. Based on the evaluation by five-fold cross-validation, the models trained with PWM feature could provide better sensitivity in the positive training dataset, while the models trained with AAindex feature achieved higher specificity in the negative training dataset. Additionally, the model trained using hybrid features, including PWM, AAC and AAindex, obtained best MCC values of 0.432, 0.472, 0.443 and 0.467 on K, R, T and P residues, respectively. CONCLUSION When comparing to an existing prediction tool, the selected models trained with hybrid features provided a promising accuracy on an independent testing dataset. In short, this work not only characterized the carbonylated substrate preference, but also demonstrated that the proposed method could provide a feasible means for accelerating preliminary discovery of protein carbonylation.
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Affiliation(s)
- Shun-Long Weng
- Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsin-Chu, 300, Taiwan.,Mackay Medicine, Nursing and Management College, Taipei, 112, Taiwan.,Department of Medicine, Mackay Medical College, New Taipei City, 252, Taiwan
| | - Kai-Yao Huang
- Department of Medical Research, Hsinchu Mackay Memorial Hospital, Hsin-Chu, 300, Taiwan.,Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan
| | - Fergie Joanda Kaunang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan
| | - Chien-Hsun Huang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan.,Tao-Yuan Hospital, Ministry of Health & Welfare, Taoyuan, 320, Taiwan
| | - Hui-Ju Kao
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan
| | - Tzu-Hao Chang
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, 110, Taiwan
| | - Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, 333, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, 333, Taiwan. .,Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, 333, 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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18
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Short JD, Downs K, Tavakoli S, Asmis R. Protein Thiol Redox Signaling in Monocytes and Macrophages. Antioxid Redox Signal 2016; 25:816-835. [PMID: 27288099 PMCID: PMC5107717 DOI: 10.1089/ars.2016.6697] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
SIGNIFICANCE Monocyte and macrophage dysfunction plays a critical role in a wide range of inflammatory disease processes, including obesity, impaired wound healing diabetic complications, and atherosclerosis. Emerging evidence suggests that the earliest events in monocyte or macrophage dysregulation include elevated reactive oxygen species production, thiol modifications, and disruption of redox-sensitive signaling pathways. This review focuses on the current state of research in thiol redox signaling in monocytes and macrophages, including (i) the molecular mechanisms by which reversible protein-S-glutathionylation occurs, (ii) the identification of bona fide S-glutathionylated proteins that occur under physiological conditions, and (iii) how disruptions of thiol redox signaling affect monocyte and macrophage functions and contribute to atherosclerosis. Recent Advances: Recent advances in redox biochemistry and biology as well as redox proteomic techniques have led to the identification of many new thiol redox-regulated proteins and pathways. In addition, major advances have been made in expanding the list of S-glutathionylated proteins and assessing the role that protein-S-glutathionylation and S-glutathionylation-regulating enzymes play in monocyte and macrophage functions, including monocyte transmigration, macrophage polarization, foam cell formation, and macrophage cell death. CRITICAL ISSUES Protein-S-glutathionylation/deglutathionylation in monocytes and macrophages has emerged as a new and important signaling paradigm, which provides a molecular basis for the well-established relationship between metabolic disorders, oxidative stress, and cardiovascular diseases. FUTURE DIRECTIONS The identification of specific S-glutathionylated proteins as well as the mechanisms that control this post-translational protein modification in monocytes and macrophages will facilitate the development of new preventive and therapeutic strategies to combat atherosclerosis and other metabolic diseases. Antioxid. Redox Signal. 25, 816-835.
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Affiliation(s)
- John D Short
- 1 Department of Pharmacology, University of Texas Health Science Center at San Antonio , San Antonio, Texas
| | - Kevin Downs
- 2 Department of Cellular and Structural Biology, University of Texas Health Science Center at San Antonio , San Antonio, Texas
| | - Sina Tavakoli
- 3 Department of Radiology, University of Texas Health Science Center at San Antonio , San Antonio, Texas
| | - Reto Asmis
- 4 Department of Clinical Laboratory Sciences, University of Texas Health Science Center at San Antonio , San Antonio, Texas.,5 Department of Biochemistry, University of Texas Health Science Center at San Antonio , San Antonio, Texas
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19
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Pal D, Sharma D, Kumar M, Sandur SK. Prediction of glutathionylation sites in proteins using minimal sequence information and their experimental validation. Free Radic Res 2016; 50:1011-21. [DOI: 10.1080/10715762.2016.1216551] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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20
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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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21
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Huang CH, Su MG, Kao HJ, Jhong JH, Weng SL, Lee TY. UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 1:6. [PMID: 26818456 PMCID: PMC4895383 DOI: 10.1186/s12918-015-0246-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Background The conjugation of ubiquitin to a substrate protein (protein ubiquitylation), which involves a sequential process – E1 activation, E2 conjugation and E3 ligation, is crucial to the regulation of protein function and activity in eukaryotes. This ubiquitin-conjugation process typically binds the last amino acid of ubiquitin (glycine 76) to a lysine residue of a target protein. The high-throughput of mass spectrometry-based proteomics has stimulated a large-scale identification of ubiquitin-conjugated peptides. Hence, a new web resource, UbiSite, was developed to identify ubiquitin-conjugation site on lysines based on large-scale proteome dataset. Results Given a total of 37,647 ubiquitin-conjugated proteins, including 128026 ubiquitylated peptides, obtained from various resources, this study carries out a large-scale investigation on ubiquitin-conjugation sites based on sequenced and structural characteristics. A TwoSampleLogo reveals that a significant depletion of histidine (H), arginine (R) and cysteine (C) residues around ubiquitylation sites may impact the conjugation of ubiquitins in closed three-dimensional environments. Based on the large-scale ubiquitylation dataset, a motif discovery tool, MDDLogo, has been adopted to characterize the potential substrate motifs for ubiquitin conjugation. Not only are single features such as amino acid composition (AAC), positional weighted matrix (PWM), position-specific scoring matrix (PSSM) and solvent-accessible surface area (SASA) considered, but also the effectiveness of incorporating MDDLogo-identified substrate motifs into a two-layered prediction model is taken into account. Evaluation by five-fold cross-validation showed that PSSM is the best feature in discriminating between ubiquitylation and non-ubiquitylation sites, based on support vector machine (SVM). Additionally, the two-layered SVM model integrating MDDLogo-identified substrate motifs could obtain a promising accuracy and the Matthews Correlation Coefficient (MCC) at 81.06 % and 0.586, respectively. Furthermore, the independent testing showed that the two-layered SVM model could outperform other prediction tools, reaching at 85.10 % sensitivity, 69.69 % specificity, 73.69 % accuracy and the 0.483 of MCC value. Conclusion The independent testing result indicated the effectiveness of incorporating MDDLogo-identified motifs into the prediction of ubiquitylation sites. In order to provide meaningful assistance to researchers interested in large-scale ubiquitinome data, the two-layered SVM model has been implemented onto a web-based system (UbiSite), which is freely available at http://csb.cse.yzu.edu.tw/UbiSite/. Two cases given in the UbiSite provide a demonstration of effective identification of ubiquitylation sites with reference to substrate motifs. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0246-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chien-Hsun Huang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan. .,Ministry of Health & Welfare, Tao-Yuan Hospital, 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.
| | - Jhih-Hua Jhong
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan.
| | - Shun-Long Weng
- Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsin-Chu, 300, Taiwan. .,Mackay Junior College of Medicine, Nursing and Management , Taipei, 112, Taiwan. .,Department of Medicine, Mackay Medical College, New Taipei City, 252, 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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Huang KY, Weng JTY, Lee TY, Weng SL. A new scheme to discover functional associations and regulatory networks of E3 ubiquitin ligases. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 1:3. [PMID: 26818115 PMCID: PMC4895279 DOI: 10.1186/s12918-015-0244-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Background Protein ubiquitination catalyzed by E3 ubiquitin ligases play important modulatory roles in various biological processes. With the emergence of high-throughput mass spectrometry technology, the proteomics research community embraced the development of numerous experimental methods for the determination of ubiquitination sites. The result is an accumulation of ubiquitinome data, coupled with a lack of available resources for investigating the regulatory networks among E3 ligases and ubiquitinated proteins. In this study, by integrating existing ubiquitinome data, experimentally validated E3 ligases and established protein-protein interactions, we have devised a strategy to construct a comprehensive map of protein ubiquitination networks. Results In total, 41,392 experimentally verified ubiquitination sites from 12,786 ubiquitinated proteins of humans have been obtained for this study. Additional 494 E3 ligases along with 1220 functional annotations and 28588 protein domains were manually curated. To characterize the regulatory networks among E3 ligases and ubiquitinated proteins, a well-established network viewer was utilized for the exploration of ubiquitination networks from 40892 protein-protein interactions. The effectiveness of the proposed approach was demonstrated in a case study examining E3 ligases involved in the ubiquitination of tumor suppressor p53. In addition to Mdm2, a known regulator of p53, the investigation also revealed other potential E3 ligases that may participate in the ubiquitination of p53. Conclusion Aside from the ability to facilitate comprehensive investigations of protein ubiquitination networks, by integrating information regarding protein-protein interactions and substrate specificities, the proposed method could discover potential E3 ligases for ubiquitinated proteins. Our strategy presents an efficient means for the preliminary screen of ubiquitination networks and overcomes the challenge as a result of limited knowledge about E3 ligase-regulated ubiquitination. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0244-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- 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, Taoyuan, 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, Taoyuan, 320, Taiwan.
| | - Shun-Long Weng
- Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsin-Chu, 300, Taiwan. .,Mackay Junior College of Medicine, Nursing and Management, Taipei, 112, Taiwan. .,Department of Medicine, Mackay Medical College, New Taipei City, 252, Taiwan.
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Protein S-glutathionlyation links energy metabolism to redox signaling in mitochondria. Redox Biol 2015; 8:110-8. [PMID: 26773874 PMCID: PMC4731959 DOI: 10.1016/j.redox.2015.12.010] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Accepted: 12/29/2015] [Indexed: 12/17/2022] Open
Abstract
At its core mitochondrial function relies on redox reactions. Electrons stripped from nutrients are used to form NADH and NADPH, electron carriers that are similar in structure but support different functions. NADH supports ATP production but also generates reactive oxygen species (ROS), superoxide (O2·-) and hydrogen peroxide (H2O2). NADH-driven ROS production is counterbalanced by NADPH which maintains antioxidants in an active state. Mitochondria rely on a redox buffering network composed of reduced glutathione (GSH) and peroxiredoxins (Prx) to quench ROS generated by nutrient metabolism. As H2O2 is quenched, NADPH is expended to reactivate antioxidant networks and reset the redox environment. Thus, the mitochondrial redox environment is in a constant state of flux reflecting changes in nutrient and ROS metabolism. Changes in redox environment can modulate protein function through oxidation of protein cysteine thiols. Typically cysteine oxidation is considered to be mediated by H2O2 which oxidizes protein thiols (SH) forming sulfenic acid (SOH). However, problems begin to emerge when one critically evaluates the regulatory function of SOH. Indeed SOH formation is slow, non-specific, and once formed SOH reacts rapidly with a variety of molecules. By contrast, protein S-glutathionylation (PGlu) reactions involve the conjugation and removal of glutathione moieties from modifiable cysteine residues. PGlu reactions are driven by fluctuations in the availability of GSH and oxidized glutathione (GSSG) and thus should be exquisitely sensitive to changes ROS flux due to shifts in the glutathione pool in response to varying H2O2 availability. Here, we propose that energy metabolism-linked redox signals originating from mitochondria are mediated indirectly by H2O2 through the GSH redox buffering network in and outside mitochondria. This proposal is based on several observations that have shown that unlike other redox modifications PGlu reactions fulfill the requisite criteria to serve as an effective posttranslational modification that controls protein function. Mitochondria employ redox buffering networks to regulate bioenergetics. PGlu reactions link energy/nutrient metabolism to redox signaling. Other redox modifications lack regulatory features.
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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: 131] [Impact Index Per Article: 14.6] [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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