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Arici MK, Tuncbag N. Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction. Brief Bioinform 2024; 25:bbae399. [PMID: 39163205 PMCID: PMC11334722 DOI: 10.1093/bib/bbae399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 06/26/2024] [Accepted: 08/07/2024] [Indexed: 08/22/2024] Open
Abstract
Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.
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Affiliation(s)
- Muslum Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara 06800, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul 34450, Turkey
- School of Medicine, Koc University, Istanbul 34450, Turkey
- Koc University Research Center for Translational Medicine (KUTTAM), Koc University, Istanbul 34450, Turkey
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2
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Salcedo MV, Gravel N, Keshavarzi A, Huang LC, Kochut KJ, Kannan N. Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding. PeerJ 2023; 11:e15815. [PMID: 37868056 PMCID: PMC10590106 DOI: 10.7717/peerj.15815] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 07/10/2023] [Indexed: 10/24/2023] Open
Abstract
The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing.
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Affiliation(s)
- Mariah V. Salcedo
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States of America
| | - Nathan Gravel
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America
| | - Abbas Keshavarzi
- School of Computing, University of Georgia, Athens, GA, United States of America
| | - Liang-Chin Huang
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America
| | - Krzysztof J. Kochut
- School of Computing, University of Georgia, Athens, GA, United States of America
| | - Natarajan Kannan
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States of America
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America
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3
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Kao DS, Du Y, DeMarco AG, Min S, Hall MC, Rochet JC, Tao WA. Identification of Novel Kinases of Tau Using Fluorescence Complementation Mass Spectrometry (FCMS). Mol Cell Proteomics 2022; 21:100441. [PMID: 36379402 PMCID: PMC9755369 DOI: 10.1016/j.mcpro.2022.100441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 11/15/2022] Open
Abstract
Hyperphosphorylation of the microtubule-associated protein Tau is a major hallmark of Alzheimer's disease and other tauopathies. Understanding the protein kinases that phosphorylate Tau is critical for the development of new drugs that target Tau phosphorylation. At present, the repertoire of the Tau kinases remains incomplete, and methods to uncover novel upstream protein kinases are still limited. Here, we apply our newly developed proteomic strategy, fluorescence complementation mass spectrometry, to identify novel kinase candidates of Tau. By constructing Tau- and kinase-fluorescent fragment library, we detected 59 Tau-associated kinases, including 23 known kinases of Tau and 36 novel candidate kinases. In the validation phase using in vitro phosphorylation, among 15 candidate kinases we attempted to purify and test, four candidate kinases, OXSR1 (oxidative-stress responsive gene 1), DAPK2 (death-associated protein kinase 2), CSK (C-terminal SRC kinase), and ZAP70 (zeta chain of T-cell receptor-associated protein kinase 70), displayed the ability to phosphorylate Tau in time-course experiments. Furthermore, coexpression of these four kinases along with Tau increased the phosphorylation of Tau in human neuroglioma H4 cells. We demonstrate that fluorescence complementation mass spectrometry is a powerful proteomic strategy to systematically identify potential kinases that can phosphorylate Tau in cells. Our discovery of new candidate kinases of Tau can present new opportunities for developing Alzheimer's disease therapeutic strategies.
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Affiliation(s)
- Der-Shyang Kao
- Department of Biochemistry, Purdue University, West Lafayette, Indiana, USA
| | - Yanyan Du
- Department of Biochemistry, Purdue University, West Lafayette, Indiana, USA
| | - Andrew G DeMarco
- Department of Biochemistry, Purdue University, West Lafayette, Indiana, USA
| | - Sehong Min
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, USA
| | - Mark C Hall
- Department of Biochemistry, Purdue University, West Lafayette, Indiana, USA; Purdue Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
| | - Jean-Christophe Rochet
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
| | - W Andy Tao
- Department of Biochemistry, Purdue University, West Lafayette, Indiana, USA; Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, USA; Purdue Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA; Department of Chemistry, Purdue University, West Lafayette, Indiana, USA.
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4
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Gupta P, Venkadesan S, Mohanty D. Pf-Phospho: a machine learning-based phosphorylation sites prediction tool for Plasmodium proteins. Brief Bioinform 2022; 23:6618232. [PMID: 35753700 DOI: 10.1093/bib/bbac249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/14/2022] [Accepted: 05/28/2022] [Indexed: 12/13/2022] Open
Abstract
Even though several in silico tools are available for prediction of the phosphorylation sites for mammalian, yeast or plant proteins, currently no software is available for predicting phosphosites for Plasmodium proteins. However, the availability of significant amount of phospho-proteomics data during the last decade and advances in machine learning (ML) algorithms have opened up the opportunities for deciphering phosphorylation patterns of plasmodial system and developing ML-based phosphosite prediction tools for Plasmodium. We have developed Pf-Phospho, an ML-based method for prediction of phosphosites by training Random Forest classifiers using a large data set of 12 096 phosphosites of Plasmodium falciparum and Plasmodium bergei. Of the 12 096 known phosphosites, 75% of sites have been used for training/validation of the classifier, while remaining 25% have been used as completely unseen test data for blind testing. It is encouraging to note that Pf-Phospho can predict the kinase-independent phosphosites with 84% sensitivity, 75% specificity and 78% precision. In addition, it can also predict kinase-specific phosphosites for five plasmodial kinases-PfPKG, Plasmodium falciparum, PfPKA, PfPK7 and PbCDPK4 with high accuracy. Pf-Phospho (http://www.nii.ac.in/pfphospho.html) outperforms other widely used phosphosite prediction tools, which have been trained using mammalian phosphoproteome data. It also has been integrated with other widely used resources such as PlasmoDB, MPMP, Pfam and recently available ML-based predicted structures by AlphaFold2. Currently, Pf-phospho is the only bioinformatics resource available for ML-based prediction of phospho-signaling networks of Plasmodium and is a user-friendly platform for integrative analysis of phospho-signaling along with metabolic and protein-protein interaction networks.
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Affiliation(s)
- Priya Gupta
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi - 110067, India
| | | | - Debasisa Mohanty
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi - 110067, India
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5
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OUP accepted manuscript. Hum Mol Genet 2022; 31:2236-2261. [DOI: 10.1093/hmg/ddac029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 01/22/2022] [Accepted: 01/24/2022] [Indexed: 11/12/2022] Open
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Liu Y, Yang H, Liu X, Gu H, Li Y, Sun C. Protein acetylation: a novel modus of obesity regulation. J Mol Med (Berl) 2021; 99:1221-1235. [PMID: 34061242 DOI: 10.1007/s00109-021-02082-2] [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: 09/28/2020] [Revised: 03/09/2021] [Accepted: 04/21/2021] [Indexed: 11/27/2022]
Abstract
Obesity is a chronic epidemic disease worldwide which has become one of the important public health issues. It is a process that excessive accumulation of adipose tissue caused by long-term energy intake exceeding energy expenditure. So far, the prevention and treatment strategies of obesity on individuals and population have not been successful in the long term. Acetylation is one of the most common ways of protein post-translational modification (PTM). It exists on thousands of non-histone proteins in almost every cell chamber. It has many influences on protein levels and metabolome levels, which is involved in a variety of metabolic reactions, including sugar metabolism, tricarboxylic acid cycle, and fatty acid metabolism, which are closely related to biological activities. Studies have shown that protein acetylation levels are dynamically regulated by lysine acetyltransferases (KATs) and lysine deacetylases (KDACs). Protein acetylation modifies protein-protein and protein-DNA interactions and regulates the activity of enzymes or cytokines which is related to obesity in order to participate in the occurrence and treatment of obesity-related metabolic diseases. Therefore, we speculated that acetylation was likely to become effective means of controlling obesity in the future. In consequence, this review focuses on the mechanisms of protein acetylation controlled obesity, to provide theoretical basis for controlling obesity and curing obesity-related diseases, which is a significance for regulating obesity in the future. This review will focus on the role of protein acetylation in controlling obesity.
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Affiliation(s)
- Yuexia Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Hong Yang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Xuanchen Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Huihui Gu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Yizhou Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Chao Sun
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, China.
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7
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Causal interactions from proteomic profiles: Molecular data meet pathway knowledge. PATTERNS 2021; 2:100257. [PMID: 34179843 PMCID: PMC8212145 DOI: 10.1016/j.patter.2021.100257] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/10/2020] [Accepted: 04/09/2021] [Indexed: 12/17/2022]
Abstract
We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org. CausalPath builds mechanistic models from proteomic profiles It integrates biological pathway models with molecular measurements It supports logical reasoning with post-translational modifications A web server, free software, and a source code are available
Molecular profiling of biological organisms provides us with a great amount of information on cellular differences, but converting it to mechanistic insights is still a very challenging task. A prominent approach is to integrate new measurements with the mechanistic knowledge described in the scientific literature and build a model that is supported by both. Although this can be done in many ways, an adept approach will use the literature knowledge in detail and follow high standards of logical reasoning while integrating the known and the new. This article describes an approach that utilizes the details in human biological pathways to identify pairs of changes with a likely cause-effect relation within. The approach automatically converts comparative proteomic and other molecular profiles into hypotheses of differentially active mechanistic relations that explain how the profiles came to be.
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8
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Caufield JH, Sigdel D, Fu J, Choi H, Guevara-Gonzalez V, Wang D, Ping P. Cardiovascular Informatics: building a bridge to data harmony. Cardiovasc Res 2021; 118:732-745. [PMID: 33751044 DOI: 10.1093/cvr/cvab067] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 03/03/2021] [Indexed: 12/11/2022] Open
Abstract
The search for new strategies for better understanding cardiovascular disease is a constant one, spanning multitudinous types of observations and studies. A comprehensive characterization of each disease state and its biomolecular underpinnings relies upon insights gleaned from extensive information collection of various types of data. Researchers and clinicians in cardiovascular biomedicine repeatedly face questions regarding which types of data may best answer their questions, how to integrate information from multiple datasets of various types, and how to adapt emerging advances in machine learning and/or artificial intelligence to their needs in data processing. Frequently lauded as a field with great practical and translational potential, the interface between biomedical informatics and cardiovascular medicine is challenged with staggeringly massive datasets. Successful application of computational approaches to decode these complex and gigantic amounts of information becomes an essential step toward realizing the desired benefits. In this review, we examine recent efforts to adapt informatics strategies to cardiovascular biomedical research: automated information extraction and unification of multifaceted -omics data. We discuss how and why this interdisciplinary space of Cardiovascular Informatics is particularly relevant to and supportive of current experimental and clinical research. We describe in detail how open data sources and methods can drive discovery while demanding few initial resources, an advantage afforded by widespread availability of cloud computing-driven platforms. Subsequently, we provide examples of how interoperable computational systems facilitate exploration of data from multiple sources, including both consistently-formatted structured data and unstructured data. Taken together, these approaches for achieving data harmony enable molecular phenotyping of cardiovascular (CV) diseases and unification of cardiovascular knowledge.
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Affiliation(s)
- J Harry Caufield
- NHLBI Integrated Cardiovascular Data Science Training Program at University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.,Departments of Physiology at UCLA School of Medicine, Los Angeles, CA, 90095, USA
| | - Dibakar Sigdel
- NHLBI Integrated Cardiovascular Data Science Training Program at University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.,Departments of Physiology at UCLA School of Medicine, Los Angeles, CA, 90095, USA
| | - John Fu
- NHLBI Integrated Cardiovascular Data Science Training Program at University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA
| | - Howard Choi
- NHLBI Integrated Cardiovascular Data Science Training Program at University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA
| | - Vladimir Guevara-Gonzalez
- NHLBI Integrated Cardiovascular Data Science Training Program at University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA
| | - Ding Wang
- Departments of Physiology at UCLA School of Medicine, Los Angeles, CA, 90095, USA
| | - Peipei Ping
- NHLBI Integrated Cardiovascular Data Science Training Program at University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.,Departments of Physiology at UCLA School of Medicine, Los Angeles, CA, 90095, USA.,Department of Medicine (Cardiology) at UCLA School of Medicine, Los Angeles, CA, 90095, USA.,Bioinformatics and Medical Informatics, Los Angeles, CA, 90095, USA.,Scalable Analytics Institute (ScAi) at UCLA School of Engineering, Los Angeles, CA, 90095, USA
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9
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PhosPhAt 4.0: An Updated Arabidopsis Database for Searching Phosphorylation Sites and Kinase-Target Interactions. Methods Mol Biol 2021; 2358:189-202. [PMID: 34270056 DOI: 10.1007/978-1-0716-1625-3_14] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The PhosPhAt 4.0 database contains information on Arabidopsis phosphorylation sites identified by mass spectrometry in large-scale experiments from different research groups. So far PhosPhAt 4.0 has been one of the most significant large-scale data resources for plant phosphorylation studies. Functionalities of the web application, besides display of phosphorylation sites, include phosphorylation site prediction and kinase-target relationships retrieval. Here, we present an overview and user instructions for the PhosPhAt 4.0 database, with strong emphasis on recent renewals regarding protein annotation by SUBA4.0 and Mapman4, and additional phosphorylation site information imported from other databases, such as UniProt. Here, we provide a user guide for the retrieval of phosphorylation motifs from the kinase-target database and how to visualize these results. The improvements incorporated into the PhosPhAt 4.0 database have produced much more functionality and user flexibility for phosphoproteomic analysis.
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10
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Shi XX, Wu FX, Mei LC, Wang YL, Hao GF, Yang GF. Bioinformatics toolbox for exploring protein phosphorylation network. Brief Bioinform 2020; 22:5871447. [PMID: 32666116 DOI: 10.1093/bib/bbaa134] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/15/2020] [Accepted: 06/02/2020] [Indexed: 01/23/2023] Open
Abstract
A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification.
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Affiliation(s)
- Xing-Xing Shi
- College of Chemistry, Central China Normal University (CCNU)
| | | | | | - Yu-Liang Wang
- College of Chemistry, Central China Normal University (CCNU)
| | - Ge-Fei Hao
- Bioinformatics in State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering of GZU and College of Chemistry of CCNU
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11
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Bensimon A, Koch JP, Francica P, Roth SM, Riedo R, Glück AA, Orlando E, Blaukat A, Aebersold DM, Zimmer Y, Aebersold R, Medová M. Deciphering MET-dependent modulation of global cellular responses to DNA damage by quantitative phosphoproteomics. Mol Oncol 2020; 14:1185-1206. [PMID: 32336009 PMCID: PMC7266272 DOI: 10.1002/1878-0261.12696] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 03/18/2020] [Accepted: 04/22/2020] [Indexed: 12/17/2022] Open
Abstract
Increasing evidence suggests that interference with growth factor receptor tyrosine kinase (RTK) signaling can affect DNA damage response (DDR) networks, with a consequent impact on cellular responses to DNA-damaging agents widely used in cancer treatment. In that respect, the MET RTK is deregulated in abundance and/or activity in a variety of human tumors. Using two proteomic techniques, we explored how disrupting MET signaling modulates global cellular phosphorylation response to ionizing radiation (IR). Following an immunoaffinity-based phosphoproteomic discovery survey, we selected candidate phosphorylation sites for extensive characterization by targeted proteomics focusing on phosphorylation sites in both signaling networks. Several substrates of the DDR were confirmed to be modulated by sequential MET inhibition and IR, or MET inhibition alone. Upon combined treatment, for two substrates, NUMA1 S395 and CHEK1 S345, the gain and loss of phosphorylation, respectively, were recapitulated using invivo tumor models by immunohistochemistry, with possible utility in future translational research. Overall, we have corroborated phosphorylation sites at the intersection between MET and the DDR signaling networks, and suggest that these represent a class of proteins at the interface between oncogene-driven proliferation and genomic stability.
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Affiliation(s)
- Ariel Bensimon
- Department of BiologyInstitute of Molecular Systems BiologyETH ZürichSwitzerland
- Present address:
CeMM Research Center for Molecular Medicine of the Austrian Academy of SciencesViennaAustria
| | - Jonas P. Koch
- Department of Radiation Oncology, InselspitalBern University HospitalUniversity of BernSwitzerland
- Department for BioMedical Research, InselspitalBern University HospitalUniversity of BernSwitzerland
| | - Paola Francica
- Department of Radiation Oncology, InselspitalBern University HospitalUniversity of BernSwitzerland
- Department for BioMedical Research, InselspitalBern University HospitalUniversity of BernSwitzerland
| | - Selina M. Roth
- Department of Radiation Oncology, InselspitalBern University HospitalUniversity of BernSwitzerland
- Department for BioMedical Research, InselspitalBern University HospitalUniversity of BernSwitzerland
| | - Rahel Riedo
- Department of Radiation Oncology, InselspitalBern University HospitalUniversity of BernSwitzerland
- Department for BioMedical Research, InselspitalBern University HospitalUniversity of BernSwitzerland
| | - Astrid A. Glück
- Department of Radiation Oncology, InselspitalBern University HospitalUniversity of BernSwitzerland
- Department for BioMedical Research, InselspitalBern University HospitalUniversity of BernSwitzerland
| | - Eleonora Orlando
- Department of Radiation Oncology, InselspitalBern University HospitalUniversity of BernSwitzerland
- Department for BioMedical Research, InselspitalBern University HospitalUniversity of BernSwitzerland
| | | | - Daniel M. Aebersold
- Department of Radiation Oncology, InselspitalBern University HospitalUniversity of BernSwitzerland
- Department for BioMedical Research, InselspitalBern University HospitalUniversity of BernSwitzerland
| | - Yitzhak Zimmer
- Department of Radiation Oncology, InselspitalBern University HospitalUniversity of BernSwitzerland
- Department for BioMedical Research, InselspitalBern University HospitalUniversity of BernSwitzerland
| | - Ruedi Aebersold
- Department of BiologyInstitute of Molecular Systems BiologyETH ZürichSwitzerland
- Faculty of ScienceUniversity of ZürichSwitzerland
| | - Michaela Medová
- Department of Radiation Oncology, InselspitalBern University HospitalUniversity of BernSwitzerland
- Department for BioMedical Research, InselspitalBern University HospitalUniversity of BernSwitzerland
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12
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Cunningham DL, Sarhan AR, Creese AJ, Larkins KPB, Zhao H, Ferguson HR, Brookes K, Marusiak AA, Cooper HJ, Heath JK. Differential responses to kinase inhibition in FGFR2-addicted triple negative breast cancer cells: a quantitative phosphoproteomics study. Sci Rep 2020; 10:7950. [PMID: 32409632 PMCID: PMC7224374 DOI: 10.1038/s41598-020-64534-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 03/25/2020] [Indexed: 12/12/2022] Open
Abstract
Fibroblast Growth Factor (FGF) dependent signalling is frequently activated in cancer by a variety of different mechanisms. However, the downstream signal transduction pathways involved are poorly characterised. Here a quantitative differential phosphoproteomics approach, SILAC, is applied to identify FGF-regulated phosphorylation events in two triple- negative breast tumour cell lines, MFM223 and SUM52, that exhibit amplified expression of FGF receptor 2 (FGFR2) and are dependent on continued FGFR2 signalling for cell viability. Comparative Gene Ontology proteome analysis revealed that SUM52 cells were enriched in proteins associated with cell metabolism and MFM223 cells enriched in proteins associated with cell adhesion and migration. FGFR2 inhibition by SU5402 impacts a significant fraction of the observed phosphoproteome of these cells. This study expands the known landscape of FGF signalling and identifies many new targets for functional investigation. FGF signalling pathways are found to be flexible in architecture as both shared, and divergent, responses to inhibition of FGFR2 kinase activity in the canonical RAF/MAPK/ERK/RSK and PI3K/AKT/PDK/mTOR/S6K pathways are identified. Inhibition of phosphorylation-dependent negative-feedback pathways is observed, defining mechanisms of intrinsic resistance to FGFR2 inhibition. These findings have implications for the therapeutic application of FGFR inhibitors as they identify both common and divergent responses in cells harbouring the same genetic lesion and pathways of drug resistance.
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Affiliation(s)
- Debbie L Cunningham
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Adil R Sarhan
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- Department of Medical Laboratory Techniques, Nasiriyah Technical Institute, Southern Technical University, Nasiriyah, 6400, Iraq
| | - Andrew J Creese
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- Immunocore, 101 Park Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RY, UK
| | | | - Hongyan Zhao
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Harriet R Ferguson
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- Division of Molecular and Cellular Function, School of Biological Science, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
| | - Katie Brookes
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Anna A Marusiak
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- Laboratory of Experimental Medicine, Centre of New Technologies, University of Warsaw, 02-097, Warszawa, Poland
| | - Helen J Cooper
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - John K Heath
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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13
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Lin S, Wang C, Zhou J, Shi Y, Ruan C, Tu Y, Yao L, Peng D, Xue Y. EPSD: a well-annotated data resource of protein phosphorylation sites in eukaryotes. Brief Bioinform 2020; 22:298-307. [PMID: 32008039 DOI: 10.1093/bib/bbz169] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 11/25/2019] [Accepted: 12/10/2019] [Indexed: 12/16/2022] Open
Abstract
As an important post-translational modification (PTM), protein phosphorylation is involved in the regulation of almost all of biological processes in eukaryotes. Due to the rapid progress in mass spectrometry-based phosphoproteomics, a large number of phosphorylation sites (p-sites) have been characterized but remain to be curated. Here, we briefly summarized the current progresses in the development of data resources for the collection, curation, integration and annotation of p-sites in eukaryotic proteins. Also, we designed the eukaryotic phosphorylation site database (EPSD), which contained 1 616 804 experimentally identified p-sites in 209 326 phosphoproteins from 68 eukaryotic species. In EPSD, we not only collected 1 451 629 newly identified p-sites from high-throughput (HTP) phosphoproteomic studies, but also integrated known p-sites from 13 additional databases. Moreover, we carefully annotated the phosphoproteins and p-sites of eight model organisms by integrating the knowledge from 100 additional resources that covered 15 aspects, including phosphorylation regulator, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein-protein interaction, drug-target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, protein expression/proteomics and subcellular localization. We anticipate that the EPSD can serve as a useful resource for further analysis of eukaryotic phosphorylation. With a data volume of 14.1 GB, EPSD is free for all users at http://epsd.biocuckoo.cn/.
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Affiliation(s)
| | | | - Jiaqi Zhou
- Huazhong University of Science and Technology
| | - Ying Shi
- Huazhong University of Science and Technology
| | - Chen Ruan
- Huazhong University of Science and Technology
| | - Yiran Tu
- Huazhong University of Science and Technology
| | - Lan Yao
- Huazhong University of Science and Technology
| | - Di Peng
- Huazhong University of Science and Technology
| | - Yu Xue
- Huazhong University of Science and Technology
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14
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Gavali S, Cowart J, Chen C, Ross KE, Arighi C, Wu CH. RESTful API for iPTMnet: a resource for protein post-translational modification network discovery. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5829784. [PMID: 32395768 PMCID: PMC7216315 DOI: 10.1093/database/baz157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/09/2019] [Accepted: 12/23/2019] [Indexed: 11/12/2022]
Abstract
iPTMnet is a bioinformatics resource that integrates protein post-translational modification (PTM) data from text mining and curated databases and ontologies to aid in knowledge discovery and scientific study. The current iPTMnet website can be used for querying and browsing rich PTM information but does not support automated iPTMnet data integration with other tools. Hence, we have developed a RESTful API utilizing the latest developments in cloud technologies to facilitate the integration of iPTMnet into existing tools and pipelines. We have packaged iPTMnet API software in Docker containers and published it on DockerHub for easy redistribution. We have also developed Python and R packages that allow users to integrate iPTMnet for scientific discovery, as demonstrated in a use case that connects PTM sites to kinase signaling pathways.
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Affiliation(s)
- Sachin Gavali
- Center for Bioinformatics and Computational Biology, 205 Delaware Biotechnology Institute, 15 Innovation Way, Newark, DE 19711, USA
| | - Julie Cowart
- Center for Bioinformatics and Computational Biology, 205 Delaware Biotechnology Institute, 15 Innovation Way, Newark, DE 19711, USA
| | - Chuming Chen
- Center for Bioinformatics and Computational Biology, 205 Delaware Biotechnology Institute, 15 Innovation Way, Newark, DE 19711, USA.,Department of Computer and Information Sciences, 101 Smith Hall, 18 Amstel Ave Newark, DE 19716, USA
| | - Karen E Ross
- Department of Biochemistry and Molecular & Cellular Biology, 337 Basic Science Building, 3900 Reservoir Road, N.W, Washington D.C. 20057, USA
| | - Cecilia Arighi
- Center for Bioinformatics and Computational Biology, 205 Delaware Biotechnology Institute, 15 Innovation Way, Newark, DE 19711, USA.,Department of Computer and Information Sciences, 101 Smith Hall, 18 Amstel Ave Newark, DE 19716, USA
| | - Cathy H Wu
- Center for Bioinformatics and Computational Biology, 205 Delaware Biotechnology Institute, 15 Innovation Way, Newark, DE 19711, USA.,Department of Biochemistry and Molecular & Cellular Biology, 337 Basic Science Building, 3900 Reservoir Road, N.W, Washington D.C. 20057, USA.,Department of Computer and Information Sciences, 101 Smith Hall, 18 Amstel Ave Newark, DE 19716, USA
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15
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Fert-Bober J, Murray CI, Parker SJ, Van Eyk JE. Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome: Where There Is a Will, There Is a Way. Circ Res 2019; 122:1221-1237. [PMID: 29700069 DOI: 10.1161/circresaha.118.310966] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
There is an exponential increase in biological complexity as initial gene transcripts are spliced, translated into amino acid sequence, and post-translationally modified. Each protein can exist as multiple chemical or sequence-specific proteoforms, and each has the potential to be a critical mediator of a physiological or pathophysiological signaling cascade. Here, we provide an overview of how different proteoforms come about in biological systems and how they are most commonly measured using mass spectrometry-based proteomics and bioinformatics. Our goal is to present this information at a level accessible to every scientist interested in mass spectrometry and its application to proteome profiling. We will specifically discuss recent data linking various protein post-translational modifications to cardiovascular disease and conclude with a discussion for enablement and democratization of proteomics across the cardiovascular and scientific community. The aim is to inform and inspire the readership to explore a larger breadth of proteoform, particularity post-translational modifications, related to their particular areas of expertise in cardiovascular physiology.
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Affiliation(s)
- Justyna Fert-Bober
- From the Advanced Clinical BioSystems Research Institute, Smidt Heart Institute, Department of Medicine, Cedars Sinai Medical Center, Los Angeles, CA
| | - Christopher I Murray
- From the Advanced Clinical BioSystems Research Institute, Smidt Heart Institute, Department of Medicine, Cedars Sinai Medical Center, Los Angeles, CA
| | - Sarah J Parker
- From the Advanced Clinical BioSystems Research Institute, Smidt Heart Institute, Department of Medicine, Cedars Sinai Medical Center, Los Angeles, CA.
| | - Jennifer E Van Eyk
- From the Advanced Clinical BioSystems Research Institute, Smidt Heart Institute, Department of Medicine, Cedars Sinai Medical Center, Los Angeles, CA
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16
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Huang H, Arighi CN, Ross KE, Ren J, Li G, Chen SC, Wang Q, Cowart J, Vijay-Shanker K, Wu CH. iPTMnet: an integrated resource for protein post-translational modification network discovery. Nucleic Acids Res 2019; 46:D542-D550. [PMID: 29145615 PMCID: PMC5753337 DOI: 10.1093/nar/gkx1104] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 10/24/2017] [Indexed: 12/19/2022] Open
Abstract
Protein post-translational modifications (PTMs) play a pivotal role in numerous biological processes by modulating regulation of protein function. We have developed iPTMnet (http://proteininformationresource.org/iPTMnet) for PTM knowledge discovery, employing an integrative bioinformatics approach—combining text mining, data mining, and ontological representation to capture rich PTM information, including PTM enzyme-substrate-site relationships, PTM-specific protein-protein interactions (PPIs) and PTM conservation across species. iPTMnet encompasses data from (i) our PTM-focused text mining tools, RLIMS-P and eFIP, which extract phosphorylation information from full-scale mining of PubMed abstracts and full-length articles; (ii) a set of curated databases with experimentally observed PTMs; and iii) Protein Ontology that organizes proteins and PTM proteoforms, enabling their representation, annotation and comparison within and across species. Presently covering eight major PTM types (phosphorylation, ubiquitination, acetylation, methylation, glycosylation, S-nitrosylation, sumoylation and myristoylation), iPTMnet knowledgebase contains more than 654 500 unique PTM sites in over 62 100 proteins, along with more than 1200 PTM enzymes and over 24 300 PTM enzyme-substrate-site relations. The website supports online search, browsing, retrieval and visual analysis for scientific queries. Several examples, including functional interpretation of phosphoproteomic data, demonstrate iPTMnet as a gateway for visual exploration and systematic analysis of PTM networks and conservation, thereby enabling PTM discovery and hypothesis generation.
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Affiliation(s)
- Hongzhan Huang
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA.,Department of Computer & Information Sciences, University of Delaware, Newark, DE 19711, USA
| | - Cecilia N Arighi
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA.,Department of Computer & Information Sciences, University of Delaware, Newark, DE 19711, USA
| | - Karen E Ross
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Jia Ren
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Gang Li
- Department of Computer & Information Sciences, University of Delaware, Newark, DE 19711, USA
| | - Sheng-Chih Chen
- Department of Computer & Information Sciences, University of Delaware, Newark, DE 19711, USA
| | - Qinghua Wang
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA.,Department of Computer & Information Sciences, University of Delaware, Newark, DE 19711, USA
| | - Julie Cowart
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - K Vijay-Shanker
- Department of Computer & Information Sciences, University of Delaware, Newark, DE 19711, USA
| | - Cathy H Wu
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA.,Department of Computer & Information Sciences, University of Delaware, Newark, DE 19711, USA.,Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC 20057, USA
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17
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Sandall CF, MacDonald JA. Effects of phosphorylation on the NLRP3 inflammasome. Arch Biochem Biophys 2019; 670:43-57. [PMID: 30844378 DOI: 10.1016/j.abb.2019.02.020] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 02/22/2019] [Accepted: 02/27/2019] [Indexed: 01/04/2023]
Abstract
The pyrin domain containing Nod-like receptors (NLRPs) are a family of pattern recognition receptors known to regulate an array of immune signaling pathways. Emergent studies demonstrate the potential for regulatory control of inflammasome assembly by phosphorylation, notably NLRP3. Over a dozen phosphorylation sites have been identified for NLRP3 with many more suggested by phosphoproteomic studies of the NLRP family. Well characterized NLRP3 phosphorylation events include Ser198 by c-Jun terminal kinase (JNK), Ser295 by protein kinase D (PKD) and/or protein kinase A (PKA), and Tyr861 by an unknown kinase but is dephosphorylated by protein tyrosine phosphatase non-receptor 22 (PTPN22). Since the PKA- and PKD-dependent phosphorylation of NLRP3 at Ser295 is best characterized, we provide detailed review of this aspect of NLRP3 regulation. Phosphorylation of Ser295 can attenuate ATPase activity as compared to its dephosphorylated counterpart, and this event is likely unique to NLRP3. In silico modeling of NLRP3 is useful in predicting how Ser295 phosphorylation might impact upon the structural topology of the ATP-binding domain to influence catalytic activity. It is important to gain as complete understanding as possible of the complex phosphorylation-mediated mechanisms of regulation for NLRP3 in part because of its involvement in many pathological processes.
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Affiliation(s)
- Christina F Sandall
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4Z6, Canada
| | - Justin A MacDonald
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4Z6, Canada.
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18
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Arrington JV, Hsu CC, Elder SG, Andy Tao W. Recent advances in phosphoproteomics and application to neurological diseases. Analyst 2018; 142:4373-4387. [PMID: 29094114 DOI: 10.1039/c7an00985b] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Phosphorylation has an incredible impact on the biological behavior of proteins, altering everything from intrinsic activity to cellular localization and complex formation. It is no surprise then that this post-translational modification has been the subject of intense study and that, with the advent of faster, more accurate instrumentation, the number of large-scale mass spectrometry-based phosphoproteomic studies has swelled over the past decade. Recent developments in sample preparation, phosphorylation enrichment, quantification, and data analysis strategies permit both targeted and ultra-deep phosphoproteome profiling, but challenges remain in pinpointing biologically relevant phosphorylation events. We describe here technological advances that have facilitated phosphoproteomic analysis of cells, tissues, and biofluids and note applications to neuropathologies in which the phosphorylation machinery may be dysregulated, much as it is in cancer.
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19
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Liu B, Sureda-Gómez M, Zhen Y, Amador V, Reverter D. A quaternary tetramer assembly inhibits the deubiquitinating activity of USP25. Nat Commun 2018; 9:4973. [PMID: 30478318 PMCID: PMC6255862 DOI: 10.1038/s41467-018-07510-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 11/01/2018] [Indexed: 01/13/2023] Open
Abstract
USP25 deubiquitinating enzyme is a key member of the ubiquitin system, which acts as a positive regulator of the Wnt/β-catenin signaling by promoting the deubiquitination and stabilization of tankyrases. USP25 is characterized by the presence of a long insertion in the middle of the conserved catalytic domain. The crystal structure of USP25 displays an unexpected homotetrameric quaternary assembly that is directly involved in the inhibition of its enzymatic activity. The tetramer is assembled by the association of two dimers and includes contacts between the coiled-coil insertion domain and the ubiquitin-binding pocket at the catalytic domain, revealing a distinctive autoinhibitory mechanism. Biochemical and kinetic assays with dimer, tetramer and truncation constructs of USP25 support this mechanism, displaying higher catalytic activity in the dimer assembly. Moreover, the high stabilization of tankyrases in cultured cells by ectopic expression of a constitutive dimer of USP25 supports a biological relevance of this tetramerization/inhibition mechanism. USP25 is a deubiquitinating enzyme and a positive regulator of Wnt/β-catenin signaling. Here the authors present the crystal structure of USP25 in a tetrameric inactive state and their biochemical and kinetic assays support an USP25 autoinhibitory mechanism that is mediated through a dimer to tetramerization transition.
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Affiliation(s)
- Bing Liu
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain.,Dept. de Bioquímica i Biologia Molecular, Serra Hunter Fellow, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain
| | - Marta Sureda-Gómez
- Institut de Investigacions Biomèdiques Agustí Pi i Sunyer (IDIBABS), Barcelona, 08036, Spain
| | - Yang Zhen
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain.,Dept. de Bioquímica i Biologia Molecular, Serra Hunter Fellow, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain
| | - Virginia Amador
- Institut de Investigacions Biomèdiques Agustí Pi i Sunyer (IDIBABS), Barcelona, 08036, Spain
| | - David Reverter
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain. .,Dept. de Bioquímica i Biologia Molecular, Serra Hunter Fellow, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain.
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20
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Huang LC, Ross KE, Baffi TR, Drabkin H, Kochut KJ, Ruan Z, D'Eustachio P, McSkimming D, Arighi C, Chen C, Natale DA, Smith C, Gaudet P, Newton AC, Wu C, Kannan N. Integrative annotation and knowledge discovery of kinase post-translational modifications and cancer-associated mutations through federated protein ontologies and resources. Sci Rep 2018; 8:6518. [PMID: 29695735 PMCID: PMC5916945 DOI: 10.1038/s41598-018-24457-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 03/23/2018] [Indexed: 11/09/2022] Open
Abstract
Many bioinformatics resources with unique perspectives on the protein landscape are currently available. However, generating new knowledge from these resources requires interoperable workflows that support cross-resource queries. In this study, we employ federated queries linking information from the Protein Kinase Ontology, iPTMnet, Protein Ontology, neXtProt, and the Mouse Genome Informatics to identify key knowledge gaps in the functional coverage of the human kinome and prioritize understudied kinases, cancer variants and post-translational modifications (PTMs) for functional studies. We identify 32 functional domains enriched in cancer variants and PTMs and generate mechanistic hypotheses on overlapping variant and PTM sites by aggregating information at the residue, protein, pathway and species level from these resources. We experimentally test the hypothesis that S768 phosphorylation in the C-helix of EGFR is inhibitory by showing that oncogenic variants altering S768 phosphorylation increase basal EGFR activity. In contrast, oncogenic variants altering conserved phosphorylation sites in the ‘hydrophobic motif’ of PKCβII (S660F and S660C) are loss-of-function in that they reduce kinase activity and enhance membrane translocation. Our studies provide a framework for integrative, consistent, and reproducible annotation of the cancer kinomes.
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Affiliation(s)
- Liang-Chin Huang
- Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA
| | - Karen E Ross
- Protein Information Resource (PIR), Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, 20007, USA
| | - Timothy R Baffi
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - Krzysztof J Kochut
- Department of Computer Science, University of Georgia, Athens, GA, 30602, USA
| | - Zheng Ruan
- Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA
| | - Peter D'Eustachio
- Department of Biochemistry & Molecular Pharmacology, NYU School of Medicine, New York, NY, 10016, USA
| | - Daniel McSkimming
- Genome, Environment, and Microbiome (GEM) Center of Excellence, University at Buffalo, Buffalo, NY, 14203, USA
| | - Cecilia Arighi
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA
| | - Chuming Chen
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA
| | - Darren A Natale
- Protein Information Resource (PIR), Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, 20007, USA
| | | | - Pascale Gaudet
- SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - Alexandra C Newton
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Cathy Wu
- Protein Information Resource (PIR), Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, 20007, USA.,Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA
| | - Natarajan Kannan
- Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA.
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21
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Agyei D, Tsopmo A, Udenigwe CC. Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides. Anal Bioanal Chem 2018. [PMID: 29516135 DOI: 10.1007/s00216-018-0974-1] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
There are emerging advancements in the strategies used for the discovery and development of food-derived bioactive peptides because of their multiple food and health applications. Bioinformatics and peptidomics are two computational and analytical techniques that have the potential to speed up the development of bioactive peptides from bench to market. Structure-activity relationships observed in peptides form the basis for bioinformatics and in silico prediction of bioactive sequences encrypted in food proteins. Peptidomics, on the other hand, relies on "hyphenated" (liquid chromatography-mass spectrometry-based) techniques for the detection, profiling, and quantitation of peptides. Together, bioinformatics and peptidomics approaches provide a low-cost and effective means of predicting, profiling, and screening bioactive protein hydrolysates and peptides from food. This article discuses the basis, strengths, and limitations of bioinformatics and peptidomics approaches currently used for the discovery and analysis of food-derived bioactive peptides.
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Affiliation(s)
- Dominic Agyei
- Department of Food Science, University of Otago, Dunedin, 9054, New Zealand
| | - Apollinaire Tsopmo
- Food Science and Nutrition Program, Department of Chemistry, Carleton University, Ottawa, ON, K1S 5B6, Canada
| | - Chibuike C Udenigwe
- School of Nutrition Sciences, University of Ottawa, Ottawa, ON, K1N 6N5, Canada. .,Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, K1N 6N5, Canada.
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22
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Doellinger J, Grossegesse M, Nitsche A, Lasch P. DMSO as a mobile phase additive enhances detection of ubiquitination sites by nano-LC-ESI-MS/MS. JOURNAL OF MASS SPECTROMETRY : JMS 2018; 53:183-187. [PMID: 29193534 DOI: 10.1002/jms.4049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 10/30/2017] [Accepted: 11/16/2017] [Indexed: 06/07/2023]
Abstract
Large-scale detection of ubiquitination sites in whole cell proteomes using nano-liquid chromatography coupled to tandem mass spectrometry is a well-established technique that has deepened the understanding of protein degradation processes in eukaryotic cells. Ubiquitination sites are usually identified by detection of Lys-ɛ-Gly-Gly (K-ɛ-GG)-remnant peptides, which are generated by tryptic digestion of proteomes. We show in this application note that dimethyl sulfoxide addition to the liquid chromatography mobile phase enhances identification rates of K-ɛ-GG peptides by more than 100% due to an increase of peptide signal intensities. The gain in the number of ubiquitination site identifications exceeds by far the gain that has been published for other posttranslational modifications, namely, phosphorylation and acetylation.
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Affiliation(s)
- Joerg Doellinger
- Centre for Biological Threats and Special Pathogens, Proteomics and Spectroscopy (ZBS6), Robert Koch Institute, Berlin, Germany
- Centre for Biological Threats and Special Pathogens, Highly Pathogenic Viruses (ZBS1), Robert Koch Institute, Berlin, Germany
| | - Marica Grossegesse
- Centre for Biological Threats and Special Pathogens, Highly Pathogenic Viruses (ZBS1), Robert Koch Institute, Berlin, Germany
| | - Andreas Nitsche
- Centre for Biological Threats and Special Pathogens, Highly Pathogenic Viruses (ZBS1), Robert Koch Institute, Berlin, Germany
| | - Peter Lasch
- Centre for Biological Threats and Special Pathogens, Proteomics and Spectroscopy (ZBS6), Robert Koch Institute, Berlin, Germany
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23
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Sun D, Wang M, Li A. MPTM: A tool for mining protein post-translational modifications from literature. J Bioinform Comput Biol 2017; 15:1740005. [PMID: 28982288 DOI: 10.1142/s0219720017400054] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Due to the importance of post-translational modifications (PTMs) in human health and diseases, PTMs are regularly reported in the biomedical literature. However, the continuing and rapid pace of expansion of this literature brings a huge challenge for researchers and database curators. Therefore, there is a pressing need to aid them in identifying relevant PTM information more efficiently by using a text mining system. So far, only a few web servers are available for mining information of a very limited number of PTMs, which are based on simple pattern matching or pre-defined rules. In our work, in order to help researchers and database curators easily find and retrieve PTM information from available text, we have developed a text mining tool called MPTM, which extracts and organizes valuable knowledge about 11 common PTMs from abstracts in PubMed by using relations extracted from dependency parse trees and a heuristic algorithm. It is the first web server that provides literature mining service for hydroxylation, myristoylation and GPI-anchor. The tool is also used to find new publications on PTMs from PubMed and uncovers potential PTM information by large-scale text analysis. MPTM analyzes text sentences to identify protein names including substrates and protein-interacting enzymes, and automatically associates them with the UniProtKB protein entry. To facilitate further investigation, it also retrieves PTM-related information, such as human diseases, Gene Ontology terms and organisms from the input text and related databases. In addition, an online database (MPTMDB) with extracted PTM information and a local MPTM Lite package are provided on the MPTM website. MPTM is freely available online at http://bioinformatics.ustc.edu.cn/mptm/ and the source codes are hosted on GitHub: https://github.com/USTC-HILAB/MPTM .
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Affiliation(s)
- Dongdong Sun
- 1 School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, P. R. China
| | - Minghui Wang
- 1 School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, P. R. China
| | - Ao Li
- 1 School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, P. R. China
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24
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Ross KE, Natale DA, Arighi C, Chen SC, Huang H, Li G, Ren J, Wang M, Vijay-Shanker K, Wu CH. Scalable Text Mining Assisted Curation of Post-Translationally Modified Proteoforms in the Protein Ontology. CEUR WORKSHOP PROCEEDINGS 2016; 1747:http://ceur-ws.org/Vol-1747/BIT103_ICBO2016.pdf. [PMID: 28706471 PMCID: PMC5504912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The Protein Ontology (PRO) defines protein classes and their interrelationships from the family to the protein form (proteoform) level within and across species. One of the unique contributions of PRO is its representation of post-translationally modified (PTM) proteoforms. However, progress in adding PTM proteoform classes to PRO has been relatively slow due to the extensive manual curation effort required. Here we report an automated pipeline for creation of PTM proteoform classes that leverages two phosphorylation-focused text mining tools (RLIMS-P, which detects mentions of kinases, substrates, and phosphorylation sites, and eFIP, which detects phosphorylation-dependent protein-protein interactions (PPIs)) and our integrated PTM database, iPTMnet. By applying this pipeline, we obtained a set of ~820 substrate-site pairs that are suitable for automated PRO term generation with literature-based evidence attribution. Inclusion of these terms in PRO will increase PRO coverage of species-specific PTM proteoforms by 50%. Many of these new proteoforms also have associated kinase and/or PPI information. Finally, we show a phosphorylation network for the human and mouse peptidyl-prolyl cis-trans isomerase (PIN1/Pin1) derived from our dataset that demonstrates the biological complexity of the information we have extracted. Our approach addresses scalability in PRO curation and will be further expanded to advance PRO representation of phosphorylated proteoforms.
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Affiliation(s)
- Karen E Ross
- Protein Information Resource, Georgetown University Medical Center, Washington, DC, USA
| | - Darren A Natale
- Protein Information Resource, Georgetown University Medical Center, Washington, DC, USA
| | - Cecilia Arighi
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Sheng-Chih Chen
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Hongzhan Huang
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Gang Li
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Jia Ren
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Michael Wang
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - K Vijay-Shanker
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Cathy H Wu
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
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