151
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Clinical candidates modulating protein-protein interactions: The fragment-based experience. Eur J Med Chem 2019; 167:76-95. [DOI: 10.1016/j.ejmech.2019.01.084] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 01/30/2019] [Accepted: 01/31/2019] [Indexed: 12/23/2022]
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152
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Abstract
Phenotype robustness to environmental fluctuations is a common biological phenomenon. Although most phenotypes involve multiple proteins that interact with each other, the basic principles of how such interactome networks respond to environmental unpredictability and change during evolution are largely unknown. Here we study interactomes of 1,840 species across the tree of life involving a total of 8,762,166 protein-protein interactions. Our study focuses on the resilience of interactomes to network failures and finds that interactomes become more resilient during evolution, meaning that interactomes become more robust to network failures over time. In bacteria, we find that a more resilient interactome is in turn associated with the greater ability of the organism to survive in a more complex, variable, and competitive environment. We find that at the protein family level proteins exhibit a coordinated rewiring of interactions over time and that a resilient interactome arises through gradual change of the network topology. Our findings have implications for understanding molecular network structure in the context of both evolution and environment.
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153
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Vázquez N, Rocha S, López-Fernández H, Torres A, Camacho R, Fdez-Riverola F, Vieira J, Vieira CP, Reboiro-Jato M. EvoPPI 1.0: a Web Platform for Within- and Between-Species Multiple Interactome Comparisons and Application to Nine PolyQ Proteins Determining Neurodegenerative Diseases. Interdiscip Sci 2019; 11:45-56. [DOI: 10.1007/s12539-019-00317-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 01/07/2019] [Accepted: 01/09/2019] [Indexed: 01/21/2023]
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154
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Kotlyar M, Pastrello C, Malik Z, Jurisica I. IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species. Nucleic Acids Res 2019; 47:D581-D589. [PMID: 30407591 PMCID: PMC6323934 DOI: 10.1093/nar/gky1037] [Citation(s) in RCA: 131] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 10/15/2018] [Accepted: 10/28/2018] [Indexed: 12/11/2022] Open
Abstract
Knowing the set of physical protein-protein interactions (PPIs) that occur in a particular context-a tissue, disease, or other condition-can provide valuable insights into key research questions. However, while the number of identified human PPIs is expanding rapidly, context information remains limited, and for most non-human species context-specific networks are completely unavailable. The Integrated Interactions Database (IID) provides one of the most comprehensive sets of context-specific human PPI networks, including networks for 133 tissues, 91 disease conditions, and many other contexts. Importantly, it also provides context-specific networks for 17 non-human species including model organisms and domesticated animals. These species are vitally important for drug discovery and agriculture. IID integrates interactions from multiple databases and datasets. It comprises over 4.8 million PPIs annotated with several types of context: tissues, subcellular localizations, diseases, and druggability information (the latter three are new annotations not available in the previous version). This update increases the number of species from 6 to 18, the number of PPIs from ∼1.5 million to ∼4.8 million, and the number of tissues from 30 to 133. IID also now supports topology and enrichment analyses of returned networks. IID is available at http://ophid.utoronto.ca/iid.
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Affiliation(s)
- Max Kotlyar
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Chiara Pastrello
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Zara Malik
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Igor Jurisica
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON M5S 1A4, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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155
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Yakubu RR, Nieves E, Weiss LM. The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein-Protein Interactions (PPIs). ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1140:169-198. [PMID: 31347048 DOI: 10.1007/978-3-030-15950-4_10] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.
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Affiliation(s)
- Rama R Yakubu
- Department of Pathology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Edward Nieves
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Developmental and Molecular Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Louis M Weiss
- Department of Pathology, Albert Einstein College of Medicine, Bronx, NY, USA. .,Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA.
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156
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Fernandes R, Nogueira G, da Costa PJ, Pinto F, Romão L. Nonsense-Mediated mRNA Decay in Development, Stress and Cancer. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1157:41-83. [DOI: 10.1007/978-3-030-19966-1_3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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157
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Allosteric Modulators of Protein-Protein Interactions (PPIs). ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1163:313-334. [PMID: 31707709 DOI: 10.1007/978-981-13-8719-7_13] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Protein-protein interactions (PPIs) represent promising drug targets of broad-spectrum therapeutic interests due to their critical implications in both health and disease circumstances. Hence, they are widely accepted as the Holy Grail of drug development. Historically, PPIs were rendered "undruggable" for their large, flat, and pocket-less structures. Current attempts to drug these "intractable" targets include orthosteric and allosteric methodologies. Previous efforts employing orthosteric approaches like protein therapeutics and orthosteric small molecules frequently suffered from poor performance caused by the difficulties in directly targeting PPI interfaces. As structural biology progresses rapidly, allosteric modulators, which direct to the allosteric regulatory sites remote to the PPI surfaces, have gradually established as a potential solution. Allosteric pockets are topologically distal from the PPI orthosteric sites, and their ligands do not need to compete with the PPI partners, which helps to improve the physiochemical and pharmacological properties of allosteric PPI modulators. Thus, exploiting allostery to tailor PPIs is regarded as a tempting strategy in future PPI drug discovery. Here, we provide a comprehensive review of our representative achievements along the way we utilize allosteric effects to tame the difficult PPI systems into druggable targets. Importantly, we provide an in-depth mechanistic analysis of this success, which will be instructive to future related lead optimizations and drug design. Finally, we discuss the current challenges in allosteric PPI drug discovery. Their solutions as well as future perspectives are also presented.
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158
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Alonso-López D, Campos-Laborie FJ, Gutiérrez MA, Lambourne L, Calderwood MA, Vidal M, De Las Rivas J. APID database: redefining protein-protein interaction experimental evidences and binary interactomes. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5304002. [PMID: 30715274 PMCID: PMC6354026 DOI: 10.1093/database/baz005] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 01/07/2019] [Indexed: 12/20/2022]
Abstract
The collection and integration of all the known protein–protein physical interactions within a proteome framework are critical to allow proper exploration of the protein interaction networks that drive biological processes in cells at molecular level. APID Interactomes is a public resource of biological data (http://apid.dep.usal.es) that provides a comprehensive and curated collection of `protein interactomes’ for more than 1100 organisms, including 30 species with more than 500 interactions, derived from the integration of experimentally detected protein-to-protein physical interactions (PPIs). We have performed an update of APID database including a redefinition of several key properties of the PPIs to provide a more precise data integration and to avoid false duplicated records. This includes the unification of all the PPIs from five primary databases of molecular interactions (BioGRID, DIP, HPRD, IntAct and MINT), plus the information from two original systematic sources of human data and from experimentally resolved 3D structures (i.e. PDBs, Protein Data Bank files, where more than two distinct proteins have been identified). Thus, APID provides PPIs reported in published research articles (with traceable PMIDs) and detected by valid experimental interaction methods that give evidences about such protein interactions (following the `ontology and controlled vocabulary’: www.ebi.ac.uk/ols/ontologies/mi; developed by `HUPO PSI-MI’). Within this data mining framework, all interaction detection methods have been grouped into two main types: (i) `binary’ physical direct detection methods and (ii) `indirect’ methods. As a result of these redefinitions, APID provides unified protein interactomes including the specific `experimental evidences’ that support each PPI, indicating whether the interactions can be considered `binary’ (i.e. supported by at least one binary detection method) or not.
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Affiliation(s)
- Diego Alonso-López
- Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas and University of Salamanca, Salamanca, Spain
| | - Francisco J Campos-Laborie
- Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas and University of Salamanca, Salamanca, Spain
| | - Miguel A Gutiérrez
- Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas and University of Salamanca, Salamanca, Spain
| | - Luke Lambourne
- Center for Cancer Systems Biology, Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology, Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Marc Vidal
- Center for Cancer Systems Biology, Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas and University of Salamanca, Salamanca, Spain
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159
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Boyle EA, Pritchard JK, Greenleaf WJ. High-resolution mapping of cancer cell networks using co-functional interactions. Mol Syst Biol 2018; 14:e8594. [PMID: 30573688 PMCID: PMC6300813 DOI: 10.15252/msb.20188594] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 11/26/2018] [Accepted: 11/30/2018] [Indexed: 12/26/2022] Open
Abstract
Powerful new technologies for perturbing genetic elements have recently expanded the study of genetic interactions in model systems ranging from yeast to human cell lines. However, technical artifacts can confound signal across genetic screens and limit the immense potential of parallel screening approaches. To address this problem, we devised a novel PCA-based method for correcting genome-wide screening data, bolstering the sensitivity and specificity of detection for genetic interactions. Applying this strategy to a set of 436 whole genome CRISPR screens, we report more than 1.5 million pairs of correlated "co-functional" genes that provide finer-scale information about cell compartments, biological pathways, and protein complexes than traditional gene sets. Lastly, we employed a gene community detection approach to implicate core genes for cancer growth and compress signal from functionally related genes in the same community into a single score. This work establishes new algorithms for probing cancer cell networks and motivates the acquisition of further CRISPR screen data across diverse genotypes and cell types to further resolve complex cellular processes.
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Affiliation(s)
- Evan A Boyle
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Biology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford, CA, USA
| | - William J Greenleaf
- Department of Genetics, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
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160
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Halu A, Wang JG, Iwata H, Mojcher A, Abib AL, Singh SA, Aikawa M, Sharma A. Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation. eLife 2018; 7:37059. [PMID: 30303482 PMCID: PMC6179386 DOI: 10.7554/elife.37059] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 08/30/2018] [Indexed: 02/06/2023] Open
Abstract
The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling. When human cells or tissues are injured, the body triggers a response known as inflammation to repair the damage and protect itself from further harm. However, if the same issue keeps recurring, the tissues become inflamed for longer periods of time, which may ultimately lead to health problems. This is what could be happening in cardiovascular diseases, where long-term inflammation could damage the heart and blood vessels. Many different proteins interact with each other to control inflammation; gaining an insight into the nature of these interactions could help to pinpoint the role of each molecular actor. Researchers have used a combination of unbiased, large-scale experimental and computational approaches to develop the interactome, a map of the known interactions between all proteins in humans. However, interactions between proteins can change between cell types, or during disease. Here, Halu et al. aimed to refine the human interactome and identify new proteins involved in inflammation, especially in the context of cardiovascular disease. Cells called macrophages produce signals that trigger inflammation whey they detect damage in other cells or tissues. The experiments used a technique called proteomics to measure the amounts of all the proteins in human macrophages. Combining these data with the human interactome made it possible to predict new links between proteins known to have a role in inflammation and other proteins in the interactome. Further analysis using other sets of data from macrophages helped identify two new candidate proteins – GBP1 and WARS – that may promote inflammation. Halu et al. then used a genetic approach to deactivate the genes and decrease the levels of these two proteins in macrophages, which caused the signals that encourage inflammation to drop. These findings suggest that GBP1 and WARS regulate the activity of macrophages to promote inflammation. The two proteins could therefore be used as drug targets to treat cardiovascular diseases and other disorders linked to inflammation, but further studies will be needed to precisely dissect how GBP1 and WARS work in humans.
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Affiliation(s)
- Arda Halu
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States.,Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Jian-Guo Wang
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Hiroshi Iwata
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Alexander Mojcher
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Ana Luisa Abib
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Sasha A Singh
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
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161
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Podder A, Pandit M, Narayanan L. Drug Target Prioritization for Alzheimer's Disease Using Protein Interaction Network Analysis. ACTA ACUST UNITED AC 2018; 22:665-677. [DOI: 10.1089/omi.2018.0131] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Avijit Podder
- Bioinformatics Infrastructure Facility, Sri Venkateswara College (University of Delhi), Delhi, India
| | - Mansi Pandit
- Bioinformatics Infrastructure Facility, Sri Venkateswara College (University of Delhi), Delhi, India
| | - Latha Narayanan
- Bioinformatics Infrastructure Facility, Sri Venkateswara College (University of Delhi), Delhi, India
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162
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Rhodes CA, Dougherty PG, Cooper JK, Qian Z, Lindert S, Wang QE, Pei D. Cell-Permeable Bicyclic Peptidyl Inhibitors against NEMO-IκB Kinase Interaction Directly from a Combinatorial Library. J Am Chem Soc 2018; 140:12102-12110. [PMID: 30176143 PMCID: PMC6231237 DOI: 10.1021/jacs.8b06738] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Macrocyclic peptides are capable of binding to flat protein surfaces such as the interfaces of protein-protein interactions with antibody-like affinity and specificity, but generally lack cell permeability in order to access intracellular targets. In this work, we designed and synthesized a large combinatorial library of cell-permeable bicyclic peptides, in which the first ring consisted of randomized peptide sequences for potential binding to a target of interest, while the second ring featured a family of different cell-penetrating motifs, for both cell penetration and target binding. The library was screened against the IκB kinase α/β (IKKα/β)-binding domain of NF-κB essential modulator (NEMO), resulting in the discovery of several cell-permeable bicyclic peptides, which inhibited the NEMO-IKKβ interaction with low μM IC50 values. Further optimization of one of the hits led to a relatively potent and cell-permeable NEMO inhibitor (IC50 = 1.0 μM), which selectively inhibited canonical NF-κB signaling in mammalian cells and the proliferation of cisplatin-resistant ovarian cancer cells. The inhibitor provides a useful tool for investigating the biological functions of NEMO/NF-κB and a potential lead for further development of a novel class of anti-inflammatory and anticancer drugs.
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Affiliation(s)
- Curran A. Rhodes
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Patrick G. Dougherty
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Jahan K. Cooper
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Ziqing Qian
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Qi-En Wang
- Department of Radiology, James Cancer Hospital and Solove Research Institute, The Ohio State University Wexner Medical Center, Columbus, Ohio 43210, United States
| | - Dehua Pei
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio 43210, United States
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163
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Bertoni M, Aloy P. DynBench3D, a Web-Resource to Dynamically Generate Benchmark Sets of Large Heteromeric Protein Complexes. J Mol Biol 2018; 430:4431-4438. [PMID: 30274705 DOI: 10.1016/j.jmb.2018.09.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 08/21/2018] [Accepted: 09/11/2018] [Indexed: 11/24/2022]
Abstract
Multi-protein machines are responsible for most cellular tasks, and many efforts have been invested in the systematic identification and characterization of thousands of these macromolecular assemblies. However, unfortunately, the (quasi) atomic details necessary to understand their function are available only for a tiny fraction of the known complexes. The computational biology community is developing strategies to integrate structural data of different nature, from electron microscopy to X-ray crystallography, to model large molecular machines, as it has been done for individual proteins and interactions with remarkable success. However, unlike for binary interactions, there is no reliable gold-standard set of three-dimensional (3D) complexes to benchmark the performance of these methodologies and detect their limitations. Here, we present a strategy to dynamically generate non-redundant sets of 3D heteromeric complexes with three or more components. By changing the values of sequence identity and component overlap between assemblies required to define complex redundancy, we can create sets of representative complexes with known 3D structure (i.e., target complexes). Using an identity threshold of 20% and imposing a fraction of component overlap of <0.5, we identify 495 unique target complexes, which represent a real non-redundant set of heteromeric assemblies with known 3D structure. Moreover, for each target complex, we also identify a set of assemblies, of varying degrees of identity and component overlap, that can be readily used as input in a complex modeling exercise (i.e., template subcomplexes). We hope that resources like this will significantly help the development and progress assessment of novel methodologies, as docking benchmarks and blind prediction contests did. The interactive resource is accessible at https://DynBench3D.irbbarcelona.org.
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Affiliation(s)
- Martino Bertoni
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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164
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Yang JS, Garriga-Canut M, Link N, Carolis C, Broadbent K, Beltran-Sastre V, Serrano L, Maurer SP. rec-YnH enables simultaneous many-by-many detection of direct protein-protein and protein-RNA interactions. Nat Commun 2018; 9:3747. [PMID: 30217970 PMCID: PMC6138660 DOI: 10.1038/s41467-018-06128-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 08/13/2018] [Indexed: 01/06/2023] Open
Abstract
Knowing which proteins and RNAs directly interact is essential for understanding cellular mechanisms. Unfortunately, discovering such interactions is costly and often unreliable. To overcome these limitations, we developed rec-YnH, a new yeast two and three-hybrid-based screening pipeline capable of detecting interactions within protein libraries or between protein libraries and RNA fragment pools. rec-YnH combines batch cloning and transformation with intracellular homologous recombination to generate bait-prey fusion libraries. By developing interaction selection in liquid-gels and using an ORF sequence-based readout of interactions via next-generation sequencing, we eliminate laborious plating and barcoding steps required by existing methods. We use rec-Y2H to simultaneously map interactions of protein domains and reveal novel putative interactors of PAR proteins. We further employ rec-Y2H to predict the architecture of published coprecipitated complexes. Finally, we use rec-Y3H to map interactions between multiple RNA-binding proteins and RNAs-the first time interactions between protein and RNA pools are simultaneously detected.
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Affiliation(s)
- Jae-Seong Yang
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, 08003, Barcelona, Spain
| | - Mireia Garriga-Canut
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, 08003, Barcelona, Spain
| | - Nele Link
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, 08003, Barcelona, Spain
| | - Carlo Carolis
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, 08003, Barcelona, Spain
| | - Katrina Broadbent
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, 08003, Barcelona, Spain
| | - Violeta Beltran-Sastre
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, 08003, Barcelona, Spain
| | - Luis Serrano
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, 08003, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), 08002, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, 08010, Barcelona, Spain
| | - Sebastian P Maurer
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, 08003, Barcelona, Spain. .,Universitat Pompeu Fabra (UPF), 08002, Barcelona, Spain.
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165
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Jiménez-García B, Roel-Touris J, Romero-Durana M, Vidal M, Jiménez-González D, Fernández-Recio J. LightDock: a new multi-scale approach to protein-protein docking. Bioinformatics 2018; 34:49-55. [PMID: 28968719 DOI: 10.1093/bioinformatics/btx555] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 09/01/2017] [Indexed: 12/18/2022] Open
Abstract
Motivation Computational prediction of protein-protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. Results We describe here a new multi-scale protein-protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases. Availability and implementation The source code of the software and installation instructions are available for download at https://life.bsc.es/pid/lightdock/. Contact juanf@bsc.es. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Brian Jiménez-García
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Jorge Roel-Touris
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Miguel Romero-Durana
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Miquel Vidal
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Daniel Jiménez-González
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.,Department of Computer Architecture, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
| | - Juan Fernández-Recio
- Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.,Structural Biology Unit, IBMB-CSIC, 08028 Barcelona, Spain
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166
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Moreno-Ramírez CE, Gutiérrez-Garzón E, Barreto GE, Forero DA. Genome-Wide Expression Profiles for Ischemic Stroke: A Meta-Analysis. J Stroke Cerebrovasc Dis 2018; 27:3336-3341. [PMID: 30166211 DOI: 10.1016/j.jstrokecerebrovasdis.2018.07.035] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 07/07/2018] [Accepted: 07/22/2018] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Genome-wide expression studies (GWES), using microarray platforms, have allowed a deeper understanding of the molecular factors involved in the pathophysiology of ischemic stroke (IS), one of the main global causes of mortality and disability. METHODS In the current work, we carried out a meta-analysis of available GWES for IS. Bioinformatics and computational biology analyses were applied to identify enriched functional categories and convergence with other genomic datasets for IS. RESULTS Three primary datasets were included and in the meta-analyses for GWES and IS, 41 differentially expressed (DE) genes were identified using a random effects model. Thirteen of these genes were downregulated and 28 were upregulated. An analysis of functional categories found a significant enrichment for the Gene Ontology Term "Inflammatory Response" and for binding sites for the PAX2 transcription factor. CONCLUSIONS The list of DE genes identified in this meta-analysis of GWES for IS is useful for future genetic and molecular studies, which would allow the identification of novel mechanisms involved in the pathophysiology of IS. Several of the DE genes found in this meta-analysis have known functional roles related to mechanisms involved in the pathophysiology of IS. It is recognized the role of the inflammatory response in the pathophysiology of IS.
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Affiliation(s)
- Carlos E Moreno-Ramírez
- Laboratory of Neuropsychiatric Genetics, Biomedical Sciences Research Group, School of Medicine, Universidad Antonio Nariño, Bogotá, Colombia
| | - Eulogia Gutiérrez-Garzón
- Laboratory of Neuropsychiatric Genetics, Biomedical Sciences Research Group, School of Medicine, Universidad Antonio Nariño, Bogotá, Colombia
| | - George E Barreto
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Diego A Forero
- Laboratory of Neuropsychiatric Genetics, Biomedical Sciences Research Group, School of Medicine, Universidad Antonio Nariño, Bogotá, Colombia.
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167
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Macalino SJY, Basith S, Clavio NAB, Chang H, Kang S, Choi S. Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules 2018; 23:E1963. [PMID: 30082644 PMCID: PMC6222862 DOI: 10.3390/molecules23081963] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/03/2018] [Accepted: 08/04/2018] [Indexed: 12/14/2022] Open
Abstract
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.
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Affiliation(s)
- Stephani Joy Y Macalino
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Shaherin Basith
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Nina Abigail B Clavio
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Hyerim Chang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
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168
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Garcia-Vaquero ML, Gama-Carvalho M, Rivas JDL, Pinto FR. Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis. Sci Rep 2018; 8:11555. [PMID: 30068933 PMCID: PMC6070533 DOI: 10.1038/s41598-018-29990-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 07/23/2018] [Indexed: 12/14/2022] Open
Abstract
Discovering disease-associated genes (DG) is strategic for understanding pathological mechanisms. DGs form modules in protein interaction networks and diseases with common phenotypes share more DGs or have more closely interacting DGs. This prompted the development of Specific Betweenness (S2B) to find genes associated with two related diseases. S2B prioritizes genes frequently and specifically present in shortest paths linking two disease modules. Top S2B scores identified genes in the overlap of artificial network modules more than 80% of the times, even with incomplete or noisy knowledge. Applied to Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy, S2B candidates were enriched in biological processes previously associated with motor neuron degeneration. Some S2B candidates closely interacted in network cliques, suggesting common molecular mechanisms for the two diseases. S2B is a valuable tool for DG prediction, bringing new insights into pathological mechanisms. More generally, S2B can be applied to infer the overlap between other types of network modules, such as functional modules or context-specific subnetworks. An R package implementing S2B is publicly available at https://github.com/frpinto/S2B .
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Affiliation(s)
- Marina L Garcia-Vaquero
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, 1749-016, Lisboa, Portugal
| | - Margarida Gama-Carvalho
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, 1749-016, Lisboa, Portugal
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and Universidad de Salamanca (USAL), Salamanca, Spain
| | - Francisco R Pinto
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, 1749-016, Lisboa, Portugal.
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169
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Che Y, Gilbert AM, Shanmugasundaram V, Noe MC. Inducing protein-protein interactions with molecular glues. Bioorg Med Chem Lett 2018; 28:2585-2592. [DOI: 10.1016/j.bmcl.2018.04.046] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 04/16/2018] [Accepted: 04/18/2018] [Indexed: 12/27/2022]
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170
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Goodacre N, Devkota P, Bae E, Wuchty S, Uetz P. Protein-protein interactions of human viruses. Semin Cell Dev Biol 2018; 99:31-39. [PMID: 30031213 PMCID: PMC7102568 DOI: 10.1016/j.semcdb.2018.07.018] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 04/02/2018] [Accepted: 07/17/2018] [Indexed: 12/16/2022]
Abstract
Viruses infect their human hosts by a series of interactions between viral and host proteins, indicating that detailed knowledge of such virus-host interaction interfaces are critical for our understanding of viral infection mechanisms, disease etiology and the development of new drugs. In this review, we primarily survey human host-virus interaction data that are available from public databases following the standardized PSI-MS format. Notably, available host-virus protein interaction information is strongly biased toward a small number of virus families including herpesviridae, papillomaviridae, orthomyxoviridae and retroviridae. While we explore the reliability and relevance of these protein interactions we also survey the current knowledge about viruses functional and topological targets. Furthermore, we assess emerging frontiers of host-virus protein interaction research, focusing on protein interaction interfaces of hosts that are infected by different viruses and viruses that infect multiple hosts. Finally, we cover the current status of research that investigates the relationships of virus-targeted host proteins to other comorbidities as well as the influence of host-virus protein interactions on human metabolism.
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Affiliation(s)
- Norman Goodacre
- Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Prajwal Devkota
- Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, 33146, USA
| | - Eunhae Bae
- Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Stefan Wuchty
- Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, 33146, USA; Center for Computational Science, Univ. of Miami, Coral Gables, FL, 33146, USA; Dept. of Biology, Univ. of Miami, Coral Gables, FL, 33146, USA; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.
| | - Peter Uetz
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA, 23284, USA.
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171
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Trepte P, Kruse S, Kostova S, Hoffmann S, Buntru A, Tempelmeier A, Secker C, Diez L, Schulz A, Klockmeier K, Zenkner M, Golusik S, Rau K, Schnoegl S, Garner CC, Wanker EE. LuTHy: a double-readout bioluminescence-based two-hybrid technology for quantitative mapping of protein-protein interactions in mammalian cells. Mol Syst Biol 2018; 14:e8071. [PMID: 29997244 PMCID: PMC6039870 DOI: 10.15252/msb.20178071] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 06/08/2018] [Accepted: 06/15/2018] [Indexed: 12/12/2022] Open
Abstract
Information on protein-protein interactions (PPIs) is of critical importance for studying complex biological systems and developing therapeutic strategies. Here, we present a double-readout bioluminescence-based two-hybrid technology, termed LuTHy, which provides two quantitative scores in one experimental procedure when testing binary interactions. PPIs are first monitored in cells by quantification of bioluminescence resonance energy transfer (BRET) and, following cell lysis, are again quantitatively assessed by luminescence-based co-precipitation (LuC). The double-readout procedure detects interactions with higher sensitivity than traditional single-readout methods and is broadly applicable, for example, for detecting the effects of small molecules or disease-causing mutations on PPIs. Applying LuTHy in a focused screen, we identified 42 interactions for the presynaptic chaperone CSPα, causative to adult-onset neuronal ceroid lipofuscinosis (ANCL), a progressive neurodegenerative disease. Nearly 50% of PPIs were found to be affected when studying the effect of the disease-causing missense mutations L115R and ∆L116 in CSPα with LuTHy. Our study presents a robust, sensitive research tool with high utility for investigating the molecular mechanisms by which disease-associated mutations impair protein activity in biological systems.
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Affiliation(s)
- Philipp Trepte
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Sabrina Kruse
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Simona Kostova
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Sheila Hoffmann
- Synaptopathy, German Center for Neurodegenerative Diseases, Berlin, Germany
| | - Alexander Buntru
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Anne Tempelmeier
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Christopher Secker
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
- Department of Neurology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Lisa Diez
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Aline Schulz
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Konrad Klockmeier
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Martina Zenkner
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Sabrina Golusik
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Kirstin Rau
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Sigrid Schnoegl
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
| | - Craig C Garner
- Synaptopathy, German Center for Neurodegenerative Diseases, Berlin, Germany
| | - Erich E Wanker
- Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
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172
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Reece-Hoyes JS, Walhout AJM. Gateway-Compatible Yeast One-Hybrid and Two-Hybrid Assays. Cold Spring Harb Protoc 2018; 2018:2018/7/pdb.top094953. [PMID: 29967278 DOI: 10.1101/pdb.top094953] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In the first section of this introduction, we provide background information for yeast two-hybrid (Y2H) assays that provide a genetic method for the identification and analysis of binary protein-protein interactions and that are complementary to biochemical methods such as immunoprecipitation. In the second section, we discuss yeast one-hybrid (Y1H) assays that provide a "gene-centered" (DNA-to-protein) genetic method to identify and study protein-DNA interactions between cis-regulatory elements and transcription factors (TFs). This method is complementary to "TF-centered" (protein-to-DNA) biochemical methods such as chromatin immunoprecipitation.
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173
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Kauppi K, Rosenthal SB, Lo MT, Sanyal N, Jiang M, Abagyan R, McEvoy LK, Andreassen OA, Chen CH. Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia. Am J Psychiatry 2018; 175:674-682. [PMID: 29495895 PMCID: PMC6028303 DOI: 10.1176/appi.ajp.2017.17040410] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Antipsychotic drugs were incidentally discovered in the 1950s, but their mechanisms of action are still not understood. Better understanding of schizophrenia pathogenesis could shed light on actions of current drugs and reveal novel "druggable" pathways for unmet therapeutic needs. Recent genome-wide association studies offer unprecedented opportunities to characterize disease gene networks and uncover drug-disease relationships. Polygenic overlap between schizophrenia risk genes and antipsychotic drug targets has been demonstrated, but specific genes and pathways constituting this overlap are undetermined. Risk genes of polygenic disorders do not operate in isolation but in combination with other genes through protein-protein interactions among gene product. METHOD The protein interactome was used to map antipsychotic drug targets (N=88) to networks of schizophrenia risk genes (N=328). RESULTS Schizophrenia risk genes were significantly localized in the interactome, forming a distinct disease module. Core genes of the module were enriched for genes involved in developmental biology and cognition, which may have a central role in schizophrenia etiology. Antipsychotic drug targets overlapped with the core disease module and comprised multiple pathways beyond dopamine. Some important risk genes like CHRN, PCDH, and HCN families were not connected to existing antipsychotics but may be suitable targets for novel drugs or drug repurposing opportunities to treat other aspects of schizophrenia, such as cognitive or negative symptoms. CONCLUSIONS The network medicine approach provides a platform to collate information of disease genetics and drug-gene interactions to shift focus from development of antipsychotics to multitarget antischizophrenia drugs. This approach is transferable to other diseases.
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Affiliation(s)
- Karolina Kauppi
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Sara Brin Rosenthal
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Min-Tzu Lo
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Nilotpal Sanyal
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Mian Jiang
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Ruben Abagyan
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Linda K McEvoy
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Ole A Andreassen
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Chi-Hua Chen
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
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174
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Altmann M, Altmann S, Falter C, Falter-Braun P. High-Quality Yeast-2-Hybrid Interaction Network Mapping. ACTA ACUST UNITED AC 2018; 3:e20067. [PMID: 29944780 DOI: 10.1002/cppb.20067] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In this article, we describe a Y2H interaction mapping protocol for systematically screening medium-to-large collections of open reading frames (ORFs) against each other. The protocol is well suited for analysis of focused networks, for modules of interest, assembling genome-scale interactome maps, and investigating host-microbe interactions. The four-step high-throughput protocol has a demonstrated low false-discovery rate that is essential for producing meaningful network maps. Following the assembly of yeast expression clones from an existing ORFeome collection, we describe in detail the four-step procedure that begins with the primary screen using minipools, followed by secondary verification of primary positives, identification of candidate interaction pairs by sequencing, and a final verification step using fresh inoculants. In addition to this core protocol, aspects of network mapping and quality control will be discussed briefly. © 2018 by John Wiley & Sons, Inc.
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Affiliation(s)
- Melina Altmann
- Helmholtz Zentrum München, Institute of Network Biology (INET), Munich, Germany
| | - Stefan Altmann
- Helmholtz Zentrum München, Institute of Network Biology (INET), Munich, Germany
| | - Claudia Falter
- Helmholtz Zentrum München, Institute of Network Biology (INET), Munich, Germany
| | - Pascal Falter-Braun
- Helmholtz Zentrum München, Institute of Network Biology (INET), Munich, Germany.,Ludwig-Maximilians-Universität München, Microbe-Host Interactions, Munich, Germany
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175
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Predicting perturbation patterns from the topology of biological networks. Proc Natl Acad Sci U S A 2018; 115:E6375-E6383. [PMID: 29925605 DOI: 10.1073/pnas.1720589115] [Citation(s) in RCA: 122] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
High-throughput technologies, offering an unprecedented wealth of quantitative data underlying the makeup of living systems, are changing biology. Notably, the systematic mapping of the relationships between biochemical entities has fueled the rapid development of network biology, offering a suitable framework to describe disease phenotypes and predict potential drug targets. However, our ability to develop accurate dynamical models remains limited, due in part to the limited knowledge of the kinetic parameters underlying these interactions. Here, we explore the degree to which we can make reasonably accurate predictions in the absence of the kinetic parameters. We find that simple dynamically agnostic models are sufficient to recover the strength and sign of the biochemical perturbation patterns observed in 87 biological models for which the underlying kinetics are known. Surprisingly, a simple distance-based model achieves 65% accuracy. We show that this predictive power is robust to topological and kinetic parameter perturbations, and we identify key network properties that can increase up to 80% the recovery rate of the true perturbation patterns. We validate our approach using experimental data on the chemotactic pathway in bacteria, finding that a network model of perturbation spreading predicts with ∼80% accuracy the directionality of gene expression and phenotype changes in knock-out and overproduction experiments. These findings show that the steady advances in mapping out the topology of biochemical interaction networks opens avenues for accurate perturbation spread modeling, with direct implications for medicine and drug development.
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176
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Härtner F, Andrade-Navarro MA, Alanis-Lobato G. Geometric characterisation of disease modules. APPLIED NETWORK SCIENCE 2018; 3:10. [PMID: 30839777 PMCID: PMC6214295 DOI: 10.1007/s41109-018-0066-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 05/28/2018] [Indexed: 05/07/2023]
Abstract
There is an increasing accumulation of evidence supporting the existence of a hyperbolic geometry underlying the network representation of complex systems. In particular, it has been shown that the latent geometry of the human protein network (hPIN) captures biologically relevant information, leading to a meaningful visual representation of protein-protein interactions and translating challenging systems biology problems into measuring distances between proteins. Moreover, proteins can efficiently communicate with each other, without global knowledge of the hPIN structure, via a greedy routing (GR) process in which hyperbolic distances guide biological signals from source to target proteins. It is thanks to this effective information routing throughout the hPIN that the cell operates, communicates with other cells and reacts to environmental changes. As a result, the malfunction of one or a few members of this intricate system can disturb its dynamics and derive in disease phenotypes. In fact, it is known that the proteins associated with a single disease agglomerate non-randomly in the same region of the hPIN, forming one or several connected components known as the disease module (DM). Here, we present a geometric characterisation of DMs. First, we found that DM positions on the two-dimensional hyperbolic plane reflect their fragmentation and functional heterogeneity, rendering an informative picture of the cellular processes that the disease is affecting. Second, we used a distance-based dissimilarity measure to cluster DMs with shared clinical features. Finally, we took advantage of the GR strategy to study how defective proteins affect the transduction of signals throughout the hPIN.
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Affiliation(s)
- Franziska Härtner
- Faculty for Physics, Mathematics and Computer Science, Johannes Gutenberg Universität, Institute of Computer Science, Staudingerweg 7, Mainz, 55128 Germany
| | - Miguel A. Andrade-Navarro
- Faculty of Biology, Johannes Gutenberg Universität, Institute of Molecular Biology, Ackermannweg 4, Mainz, 55128 Germany
| | - Gregorio Alanis-Lobato
- Faculty of Biology, Johannes Gutenberg Universität, Institute of Molecular Biology, Ackermannweg 4, Mainz, 55128 Germany
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177
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An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders. Nat Genet 2018; 50:1032-1040. [PMID: 29892012 DOI: 10.1038/s41588-018-0130-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 04/06/2018] [Indexed: 01/20/2023]
Abstract
Identifying disease-associated missense mutations remains a challenge, especially in large-scale sequencing studies. Here we establish an experimentally and computationally integrated approach to investigate the functional impact of missense mutations in the context of the human interactome network and test our approach by analyzing ~2,000 de novo missense mutations found in autism subjects and their unaffected siblings. Interaction-disrupting de novo missense mutations are more common in autism probands, principally affect hub proteins, and disrupt a significantly higher fraction of hub interactions than in unaffected siblings. Moreover, they tend to disrupt interactions involving genes previously implicated in autism, providing complementary evidence that strengthens previously identified associations and enhances the discovery of new ones. Importantly, by analyzing de novo missense mutation data from six disorders, we demonstrate that our interactome perturbation approach offers a generalizable framework for identifying and prioritizing missense mutations that contribute to the risk of human disease.
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178
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Kılıç A, Santolini M, Nakano T, Schiller M, Teranishi M, Gellert P, Ponomareva Y, Braun T, Uchida S, Weiss ST, Sharma A, Renz H. A systems immunology approach identifies the collective impact of 5 miRs in Th2 inflammation. JCI Insight 2018; 3:97503. [PMID: 29875322 DOI: 10.1172/jci.insight.97503] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 03/29/2018] [Indexed: 12/20/2022] Open
Abstract
Allergic asthma is a chronic inflammatory disease dominated by a CD4+ T helper 2 (Th2) cell signature. The immune response amplifies in self-enforcing loops, promoting Th2-driven cellular immunity and leaving the host unable to terminate inflammation. Posttranscriptional mechanisms, including microRNAs (miRs), are pivotal in maintaining immune homeostasis. Since an altered expression of various miRs has been associated with T cell-driven diseases, including asthma, we hypothesized that miRs control mechanisms ensuring Th2 stability and maintenance in the lung. We isolated murine CD4+ Th2 cells from allergic inflamed lungs and profiled gene and miR expression. Instead of focusing on the magnitude of miR differential expression, here we addressed the secondary consequences for the set of molecular interactions in the cell, the interactome. We developed the Impact of Differential Expression Across Layers, a network-based algorithm to prioritize disease-relevant miRs based on the central role of their targets in the molecular interactome. This method identified 5 Th2-related miRs (mir27b, mir206, mir106b, mir203, and mir23b) whose antagonization led to a sharp reduction of the Th2 phenotype. Overall, a systems biology tool was developed and validated, highlighting the role of miRs in Th2-driven immune response. This result offers potentially novel approaches for therapeutic interventions.
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Affiliation(s)
- Ayşe Kılıç
- Institute of Laboratory Medicine and Pathobiochemistry, Molecular Diagnostics, Philipps University Marburg, Marburg, Germany
| | - Marc Santolini
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA.,Brigham and Women's Hospital, Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Taiji Nakano
- Institute of Laboratory Medicine and Pathobiochemistry, Molecular Diagnostics, Philipps University Marburg, Marburg, Germany
| | - Matthias Schiller
- Clinic for Dermatology and Venereology, University Medical Center Rostock, Rostock, Germany
| | - Mizue Teranishi
- Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Pascal Gellert
- Breast Cancer Now Research Centre at The Institute of Cancer Research, London, United Kingdom
| | - Yuliya Ponomareva
- Institute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe- University Frankfurt, Frankfurt Germany
| | - Thomas Braun
- Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Shizuka Uchida
- Cardiovascular Innovation Institute, University of Louisville, Louisville, Kentucky, USA
| | - Scott T Weiss
- Brigham and Women's Hospital, Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Amitabh Sharma
- Brigham and Women's Hospital, Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Harald Renz
- Institute of Laboratory Medicine and Pathobiochemistry, Molecular Diagnostics, Philipps University Marburg, Marburg, Germany
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179
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Li W, Wang M, Sun J, Wang Y, Jiang R. Gene co-opening network deciphers gene functional relationships. MOLECULAR BIOSYSTEMS 2018; 13:2428-2439. [PMID: 28976510 DOI: 10.1039/c7mb00430c] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Genome sequencing technology has generated a vast amount of genomic and epigenomic data, and has provided us a great opportunity to study gene functions on a global scale from an epigenomic view. In the last decade, network-based studies, such as those based on PPI networks and co-expression networks, have shown good performance in capturing functional relationships between genes. However, the functions of a gene and the mechanism of interaction of genes with each other to elucidate their functions are still not entirely clear. Here, we construct a gene co-opening network based on chromatin accessibility of genes. We show that genes related to a specific biological process or the same disease tend to be clustered in the co-opening network. This understanding allows us to detect functional clusters from the network and to predict new functions for genes. We further apply the network to prioritize disease genes for Psoriasis, and demonstrate the power of the joint analysis of the co-opening network and GWAS data in identifying disease genes. Taken together, the co-opening network provides a new viewpoint for the elucidation of gene associations and the interpretation of disease mechanisms.
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Affiliation(s)
- Wenran Li
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China.
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180
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Perricone U, Gulotta MR, Lombino J, Parrino B, Cascioferro S, Diana P, Cirrincione G, Padova A. An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge. MEDCHEMCOMM 2018; 9:920-936. [PMID: 30108981 PMCID: PMC6072422 DOI: 10.1039/c8md00166a] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 04/19/2018] [Indexed: 12/14/2022]
Abstract
Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.
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Affiliation(s)
- Ugo Perricone
- Computational and Medicinal Chemistry Group , Fondazione Ri.MED , Via Bandiera 11 , 90133 Palermo , Italy .
| | - Maria Rita Gulotta
- Computational and Medicinal Chemistry Group , Fondazione Ri.MED , Via Bandiera 11 , 90133 Palermo , Italy .
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Jessica Lombino
- Computational and Medicinal Chemistry Group , Fondazione Ri.MED , Via Bandiera 11 , 90133 Palermo , Italy .
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Barbara Parrino
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Stella Cascioferro
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Patrizia Diana
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Girolamo Cirrincione
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF) , Università degli Studi di Palermo , Via Archirafi 32 , 90123 Palermo , Italy
| | - Alessandro Padova
- Computational and Medicinal Chemistry Group , Fondazione Ri.MED , Via Bandiera 11 , 90133 Palermo , Italy .
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181
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Wang RS, Loscalzo J. Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications. J Mol Biol 2018; 430:2939-2950. [PMID: 29791871 DOI: 10.1016/j.jmb.2018.05.016] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 04/30/2018] [Accepted: 05/10/2018] [Indexed: 01/09/2023]
Abstract
Understanding the genetic basis of complex diseases is challenging. Prior work shows that disease-related proteins do not typically function in isolation. Rather, they often interact with each other to form a network module that underlies dysfunctional mechanistic pathways. Identifying such disease modules will provide insights into a systems-level understanding of molecular mechanisms of diseases. Owing to the incompleteness of our knowledge of disease proteins and limited information on the biological mediators of pathobiological processes, the key proteins (seed proteins) for many diseases appear scattered over the human protein-protein interactome and form a few small branches, rather than coherent network modules. In this paper, we develop a network-based algorithm, called the Seed Connector algorithm (SCA), to pinpoint disease modules by adding as few additional linking proteins (seed connectors) to the seed protein pool as possible. Such seed connectors are hidden disease module elements that are critical for interpreting the functional context of disease proteins. The SCA aims to connect seed disease proteins so that disease mechanisms and pathways can be decoded based on predicted coherent network modules. We validate the algorithm using a large corpus of 70 complex diseases and binding targets of over 200 drugs, and demonstrate the biological relevance of the seed connectors. Lastly, as a specific proof of concept, we apply the SCA to a set of seed proteins for coronary artery disease derived from a meta-analysis of large-scale genome-wide association studies and obtain a coronary artery disease module enriched with important disease-related signaling pathways and drug targets not previously recognized.
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Affiliation(s)
- Rui-Sheng Wang
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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182
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Lin GN, Corominas R, Nam HJ, Urresti J, Iakoucheva LM. Comprehensive Analyses of Tissue-Specific Networks with Implications to Psychiatric Diseases. Methods Mol Biol 2018; 1613:371-402. [PMID: 28849569 DOI: 10.1007/978-1-4939-7027-8_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Recent advances in genome sequencing and "omics" technologies are opening new opportunities for improving diagnosis and treatment of human diseases. The precision medicine initiative in particular aims at developing individualized treatment options that take into account individual variability in genes and environment of each person. Systems biology approaches that group genes, transcripts and proteins into functionally meaningful networks will play crucial role in the future of personalized medicine. They will allow comparison of healthy and disease-affected tissues and organs from the same individual, as well as between healthy and disease-afflicted individuals. However, the field faces a multitude of challenges ranging from data integration to statistical and combinatorial issues in data analyses. This chapter describes computational approaches developed by us and the others to tackle challenges in tissue-specific network analyses, with the main focus on psychiatric diseases.
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Affiliation(s)
- Guan Ning Lin
- Department of Psychiatry, University of California San Diego, 9500 Gilman Drive #0603, La Jolla, CA, 92093, USA.,Shanghai Mental Health Center, Shanghai Key Laboratory of Psychotic Disorders and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Roser Corominas
- Department of Psychiatry, University of California San Diego, 9500 Gilman Drive #0603, La Jolla, CA, 92093, USA.,Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.,Hospital del Mar Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain
| | - Hyun-Jun Nam
- Department of Psychiatry, University of California San Diego, 9500 Gilman Drive #0603, La Jolla, CA, 92093, USA
| | - Jorge Urresti
- Department of Psychiatry, University of California San Diego, 9500 Gilman Drive #0603, La Jolla, CA, 92093, USA
| | - Lilia M Iakoucheva
- Department of Psychiatry, University of California San Diego, 9500 Gilman Drive #0603, La Jolla, CA, 92093, USA.
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183
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Robertson NS, Spring DR. Using Peptidomimetics and Constrained Peptides as Valuable Tools for Inhibiting Protein⁻Protein Interactions. Molecules 2018; 23:molecules23040959. [PMID: 29671834 PMCID: PMC6017787 DOI: 10.3390/molecules23040959] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 04/16/2018] [Accepted: 04/18/2018] [Indexed: 02/07/2023] Open
Abstract
Protein–protein interactions (PPIs) are tremendously important for the function of many biological processes. However, because of the structure of many protein–protein interfaces (flat, featureless and relatively large), they have largely been overlooked as potential drug targets. In this review, we highlight the current tools used to study the molecular recognition of PPIs through the use of different peptidomimetics, from small molecules and scaffolds to peptides. Then, we focus on constrained peptides, and in particular, ways to constrain α-helices through stapling using both one- and two-component techniques.
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Affiliation(s)
- Naomi S Robertson
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - David R Spring
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
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184
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Abstract
BACKGROUND Recently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity. RESULTS We proposed a network-based method PhenoNet to calculate the similarity between phenotypes. We localized phenotypes in the network and calculated the similarity between phenotype-associated modules by modeling both the inter- and intra-similarity. CONCLUSIONS PhenoNet was evaluated on two independent evaluation datasets: gene ontology and gene expression data. The result shows that PhenoNet performs better than the state-of-art methods on all evaluation tests.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Weiwei Hui
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
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185
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Pyrogova I, Wong L. Protein complex prediction by date hub removal. Comput Biol Chem 2018; 74:407-419. [PMID: 29602640 DOI: 10.1016/j.compbiolchem.2018.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 03/13/2018] [Indexed: 02/03/2023]
Abstract
Proteins physically interact with each other and form protein complexes to perform their biological functions. The prediction of protein complexes from protein-protein interaction (PPI) network is usually difficult when the complexes are overlapping with each other in a dense region of the network. To address the problem of predicting overlapping complexes, a previously proposed network-decomposition approach is promising. It decomposes a PPI network by e.g. removing proteins with high degree (hubs) which may participate in different complexes. This motivates us to examine a list of proteins, which bind their different partners at different time or at different location (viz. date hubs), manually collected from literature, for network decomposition. Results show that the CMC complex discovery algorithm after removing date hubs recalls more overlapping complexes that were missed earlier. Further improvement in performance is achieved when we predict date hub proteins based on simple network features and remove them from PPI networks.
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Affiliation(s)
- Iana Pyrogova
- Department of Computer Science, National University of Singapore, Singapore.
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore.
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186
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Martínez-Noël G, Luck K, Kühnle S, Desbuleux A, Szajner P, Galligan JT, Rodriguez D, Zheng L, Boyland K, Leclere F, Zhong Q, Hill DE, Vidal M, Howley PM. Network Analysis of UBE3A/E6AP-Associated Proteins Provides Connections to Several Distinct Cellular Processes. J Mol Biol 2018; 430:1024-1050. [PMID: 29426014 PMCID: PMC5866790 DOI: 10.1016/j.jmb.2018.01.021] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 01/28/2018] [Accepted: 01/30/2018] [Indexed: 12/18/2022]
Abstract
Perturbations in activity and dosage of the UBE3A ubiquitin-ligase have been linked to Angelman syndrome and autism spectrum disorders. UBE3A was initially identified as the cellular protein hijacked by the human papillomavirus E6 protein to mediate the ubiquitylation of p53, a function critical to the oncogenic potential of these viruses. Although a number of substrates have been identified, the normal cellular functions and pathways affected by UBE3A are largely unknown. Previously, we showed that UBE3A associates with HERC2, NEURL4, and MAPK6/ERK3 in a high-molecular-weight complex of unknown function that we refer to as the HUN complex (HERC2, UBE3A, and NEURL4). In this study, the combination of two complementary proteomic approaches with a rigorous network analysis revealed cellular functions and pathways in which UBE3A and the HUN complex are involved. In addition to finding new UBE3A-associated proteins, such as MCM6, SUGT1, EIF3C, and ASPP2, network analysis revealed that UBE3A-associated proteins are connected to several fundamental cellular processes including translation, DNA replication, intracellular trafficking, and centrosome regulation. Our analysis suggests that UBE3A could be involved in the control and/or integration of these cellular processes, in some cases as a component of the HUN complex, and also provides evidence for crosstalk between the HUN complex and CAMKII interaction networks. This study contributes to a deeper understanding of the cellular functions of UBE3A and its potential role in pathways that may be affected in Angelman syndrome, UBE3A-associated autism spectrum disorders, and human papillomavirus-associated cancers.
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Affiliation(s)
- Gustavo Martínez-Noël
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Simone Kühnle
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Alice Desbuleux
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA; GIGA-R, University of Liège, Liège 4000, Belgium
| | - Patricia Szajner
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey T Galligan
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Diana Rodriguez
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Leon Zheng
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Kathleen Boyland
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Flavian Leclere
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Quan Zhong
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Peter M Howley
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, USA.
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187
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Fernandes IR, Cruz ACP, Ferrasa A, Phan D, Herai RH, Muotri AR. Genetic variations on SETD5 underlying autistic conditions. Dev Neurobiol 2018; 78:500-518. [PMID: 29484850 DOI: 10.1002/dneu.22584] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 02/21/2018] [Accepted: 02/21/2018] [Indexed: 12/25/2022]
Abstract
The prevalence of autism spectrum disorders (ASD) and the number of identified ASD-related genes have increased in recent years. The SETD5 gene encodes a SET-containing-domain 5 protein, a likely reader enzyme. Genetic evidences suggest that SETD5 malfunction contributes to ASD phenotype, such as on intellectual disability (ID) and facial dysmorphism. In this review, we mapped the clinical phenotypes of individuals carrying mutations on the SETD5 gene that are associated with ASD and other chromatinopathies (mutation in epigenetic modifiers that leads to the development of neurodevelopmental disorders such as ASD). After a detailed systematic literature review and analysis of public disease-related databank, we found so far 42 individuals carrying mutations on the SETD5 gene, with 23.8% presenting autistic-like features. Furthermore, most of mutations occurred between positions 9,480,000-9,500,000 bp on chromosome 3 (3p25.3) at the SETD5 gene locus. In all males, mutations in SETD5 presented high penetrance, while in females the clinical phenotype seems more variable with two reported cases showing normal female carriers and not presenting ASD or any ID-like symptoms. At the molecular level, SETD5 interacts with proteins of PAF1C and N-CoR complexes, leading to a possible involvement with chromatin modification pathway, which plays important roles for brain development. Together, we propose that mutations on the SETD5 gene could lead to a new syndromic condition in males, which is linked to 3p25 syndrome, and can leads to ASD-related intellectual disability and facial dysmorphism. © 2018 Wiley Periodicals, Inc. Develop Neurobiol 78: 500-518, 2018.
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Affiliation(s)
- Isabella R Fernandes
- Department of Pediatrics/Rady Children's Hospital San Diego, Department of Cellular & Molecular Medicine, Stem Cell Program, University of California San Diego, School of Medicine, La Jolla, California, 92037-0695
| | - Ana C P Cruz
- Experimental Multiuser Laboratory (LEM), Graduate Program in Health Sciences (PPGCS), School of Medicine, Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, Paraná, 80215-901, Brazil
| | - Adriano Ferrasa
- Experimental Multiuser Laboratory (LEM), Graduate Program in Health Sciences (PPGCS), School of Medicine, Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, Paraná, 80215-901, Brazil.,Department of Informatics (DEINFO), Universidade Estadual de Ponta Grossa (UEPG), Ponta Grossa, Paraná, 84030-900, Brazil
| | - Dylan Phan
- Department of Pediatrics/Rady Children's Hospital San Diego, Department of Cellular & Molecular Medicine, Stem Cell Program, University of California San Diego, School of Medicine, La Jolla, California, 92037-0695
| | - Roberto H Herai
- Experimental Multiuser Laboratory (LEM), Graduate Program in Health Sciences (PPGCS), School of Medicine, Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, Paraná, 80215-901, Brazil.,Lico Kaesemodel Institute (ILK), Curitiba, Paraná, 80240-000, Brazil
| | - Alysson R Muotri
- Department of Pediatrics/Rady Children's Hospital San Diego, Department of Cellular & Molecular Medicine, Stem Cell Program, University of California San Diego, School of Medicine, La Jolla, California, 92037-0695
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188
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Rosell M, Fernández-Recio J. Hot-spot analysis for drug discovery targeting protein-protein interactions. Expert Opin Drug Discov 2018; 13:327-338. [PMID: 29376444 DOI: 10.1080/17460441.2018.1430763] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.
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Affiliation(s)
- Mireia Rosell
- a Department of Life Sciences , Barcelona Supercomputing Center (BSC) , Barcelona , Spain
| | - Juan Fernández-Recio
- a Department of Life Sciences , Barcelona Supercomputing Center (BSC) , Barcelona , Spain.,b Structural Biology Unit , Institut de Biologia Molecular de Barcelona (IBMB), CSIC , Barcelona , Spain
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189
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Ribeiro DM, Zanzoni A, Cipriano A, Delli Ponti R, Spinelli L, Ballarino M, Bozzoni I, Tartaglia GG, Brun C. Protein complex scaffolding predicted as a prevalent function of long non-coding RNAs. Nucleic Acids Res 2018; 46:917-928. [PMID: 29165713 PMCID: PMC5778612 DOI: 10.1093/nar/gkx1169] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 11/03/2017] [Accepted: 11/07/2017] [Indexed: 11/14/2022] Open
Abstract
The human transcriptome contains thousands of long non-coding RNAs (lncRNAs). Characterizing their function is a current challenge. An emerging concept is that lncRNAs serve as protein scaffolds, forming ribonucleoproteins and bringing proteins in proximity. However, only few scaffolding lncRNAs have been characterized and the prevalence of this function is unknown. Here, we propose the first computational approach aimed at predicting scaffolding lncRNAs at large scale. We predicted the largest human lncRNA-protein interaction network to date using the catRAPID omics algorithm. In combination with tissue expression and statistical approaches, we identified 847 lncRNAs (∼5% of the long non-coding transcriptome) predicted to scaffold half of the known protein complexes and network modules. Lastly, we show that the association of certain lncRNAs to disease may involve their scaffolding ability. Overall, our results suggest for the first time that RNA-mediated scaffolding of protein complexes and modules may be a common mechanism in human cells.
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Affiliation(s)
- Diogo M Ribeiro
- Aix-Marseille Université, Inserm, TAGC UMR_S1090, Marseille, France
| | - Andreas Zanzoni
- Aix-Marseille Université, Inserm, TAGC UMR_S1090, Marseille, France
| | - Andrea Cipriano
- Dept. of Biology and Biotechnology Charles Darwin, Sapienza University, Rome, Italy
| | - Riccardo Delli Ponti
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
| | - Lionel Spinelli
- Aix-Marseille Université, Inserm, TAGC UMR_S1090, Marseille, France
| | - Monica Ballarino
- Dept. of Biology and Biotechnology Charles Darwin, Sapienza University, Rome, Italy
| | - Irene Bozzoni
- Dept. of Biology and Biotechnology Charles Darwin, Sapienza University, Rome, Italy
| | - Gian Gaetano Tartaglia
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- Institucio Catalana de Recerca i Estudis Avançats (ICREA), 23 Passeig Lluıs Companys, 08010 Barcelona, Spain
| | - Christine Brun
- Aix-Marseille Université, Inserm, TAGC UMR_S1090, Marseille, France
- CNRS, Marseille, France
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190
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Xu WM, Yang K, Jiang LJ, Hu JQ, Zhou XZ. Integrated Modules Analysis to Explore the Molecular Mechanisms of Phlegm-Stasis Cementation Syndrome with Ischemic Heart Disease. Front Physiol 2018; 9:7. [PMID: 29403392 PMCID: PMC5786858 DOI: 10.3389/fphys.2018.00007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 01/04/2018] [Indexed: 12/15/2022] Open
Abstract
Background: Ischemic heart disease (IHD) has been the leading cause of death for several decades globally, IHD patients usually hold the symptoms of phlegm-stasis cementation syndrome (PSCS) as significant complications. However, the underlying molecular mechanisms of PSCS complicated with IHD have not yet been fully elucidated. Materials and Methods: Network medicine methods were utilized to elucidate the underlying molecular mechanisms of IHD phenotypes. Firstly, high-quality IHD-associated genes from both human curated disease-gene association database and biomedical literatures were integrated. Secondly, the IHD disease modules were obtained by dissecting the protein-protein interaction (PPI) topological modules in the String V9.1 database and the mapping of IHD-associated genes to the PPI topological modules. After that, molecular functional analyses (e.g., Gene Ontology and pathway enrichment analyses) for these IHD disease modules were conducted. Finally, the PSCS syndrome modules were identified by mapping the PSCS related symptom-genes to the IHD disease modules, which were further validated by both pharmacological and physiological evidences derived from published literatures. Results: The total of 1,056 high-quality IHD-associated genes were integrated and evaluated. In addition, eight IHD disease modules (the PPI sub-networks significantly relevant to IHD) were identified, in which two disease modules were relevant to PSCS syndrome (i.e., two PSCS syndrome modules). These two modules had enriched pathways on Toll-like receptor signaling pathway (hsa04620) and Renin-angiotensin system (hsa04614), with the molecular functions of angiotensin maturation (GO:0002003) and response to bacterium (GO:0009617), which had been validated by classical Chinese herbal formulas-related targets, IHD-related drug targets, and the phenotype features derived from human phenotype ontology (HPO) and published biomedical literatures. Conclusion: A network medicine-based approach was proposed to identify the underlying molecular modules of PSCS complicated with IHD, which could be used for interpreting the pharmacological mechanisms of well-established Chinese herbal formulas (e.g., Tao Hong Si Wu Tang, Dan Shen Yin, Hunag Lian Wen Dan Tang and Gua Lou Xie Bai Ban Xia Tang). In addition, these results delivered novel understandings of the molecular network mechanisms of IHD phenotype subtypes with PSCS complications, which would be both insightful for IHD precision medicine and the integration of disease and TCM syndrome diagnoses.
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Affiliation(s)
- Wei-Ming Xu
- Research Centre for Disease and Syndrome, Institute of Basic Theory for Traditional Chinese Medicine, China Academy of Chinese Medicine Sciences, Beijing, China
| | - Kuo Yang
- School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China
| | - Li-Jie Jiang
- Research Centre for Disease and Syndrome, Institute of Basic Theory for Traditional Chinese Medicine, China Academy of Chinese Medicine Sciences, Beijing, China
| | - Jing-Qing Hu
- Research Centre for Disease and Syndrome, Institute of Basic Theory for Traditional Chinese Medicine, China Academy of Chinese Medicine Sciences, Beijing, China
| | - Xue-Zhong Zhou
- School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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191
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Lim SH, Legere EA, Snider J, Stagljar I. Recent Progress in CFTR Interactome Mapping and Its Importance for Cystic Fibrosis. Front Pharmacol 2018; 8:997. [PMID: 29403380 PMCID: PMC5785726 DOI: 10.3389/fphar.2017.00997] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 12/26/2017] [Indexed: 12/25/2022] Open
Abstract
Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a chloride channel found in secretory epithelia with a plethora of known interacting proteins. Mutations in the CFTR gene cause cystic fibrosis (CF), a disease that leads to progressive respiratory illness and other complications of phenotypic variance resulting from perturbations of this protein interaction network. Studying the collection of CFTR interacting proteins and the differences between the interactomes of mutant and wild type CFTR provides insight into the molecular machinery of the disease and highlights possible therapeutic targets. This mini review focuses on functional genomics and proteomics approaches used for systematic, high-throughput identification of CFTR-interacting proteins to provide comprehensive insight into CFTR regulation and function.
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Affiliation(s)
- Sang Hyun Lim
- Department of Biochemistry, University of Toronto, Toronto, ON, Canada
| | | | - Jamie Snider
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Igor Stagljar
- Department of Biochemistry, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
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192
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193
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Abstract
The knowledge of protein-protein interactions (PPIs) and PPI networks (PPINs) is the key to starting to understand the biological processes inside the cell. Many computational tools have been designed to help explore PPIs and PPINs, such as those for interaction detection, reliability assessment and interaction network construction. Here, the application of computational tools is reviewed from three perspectives: PPI database construction, PPI prediction, and interaction network construction and analysis. This overview will provide researchers guidance on choosing appropriate methods for exploring PPIs.
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Affiliation(s)
- Shaowei Dong
- Department of Cell and System Biology, Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada
| | - Nicholas J Provart
- Department of Cell and System Biology, Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada.
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194
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Recabarren-Leiva D, Alarcón M. New insights into the gene expression associated to amyotrophic lateral sclerosis. Life Sci 2018; 193:110-123. [DOI: 10.1016/j.lfs.2017.12.016] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Revised: 12/01/2017] [Accepted: 12/10/2017] [Indexed: 12/11/2022]
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195
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Abstract
Comprehensive identification of direct, physical interactions between biological macromolecules, such as protein-protein, protein-DNA, and protein-RNA interactions, is critical for our understanding of the function of gene products as well as the global organization and interworkings of various molecular machines within the cell. The accurate and comprehensive detection of direct interactions, however, remains a huge challenge due to the inherent structural complexity arising from various post-transcriptional and translational modifications coupled with huge heterogeneity in concentration, affinity, and subcellular location differences existing for any interacting molecules. This has created a need for developing multiple orthogonal and complementary assays for detecting various types of biological interactions. In this introduction, we discuss the methods developed for measuring different types of molecular interactions with an emphasis on direct protein-protein interactions, critical issues for generating high-quality interactome datasets, and the insights into biological networks and human diseases that current interaction mapping efforts provide. Further, we will discuss what future might lie ahead for the continued evolution of two-hybrid methods and the role of interactomics for expanding the advancement of biomedical science.
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Affiliation(s)
- Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Aaron Richardson
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Luke Lambourne
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
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196
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Synonymous mutations in oncogenesis and apoptosis versus survival unveiled by network modeling. Oncotarget 2017; 7:34599-616. [PMID: 27129147 PMCID: PMC5085179 DOI: 10.18632/oncotarget.8963] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Accepted: 04/11/2016] [Indexed: 12/11/2022] Open
Abstract
Synonymous mutations, which do not alter the encoded amino acid, have been routinely assumed to be ‘neutral’ and would have no effect on phenotype or fitness. Yet increasing observations have emerged to overturn this conventional concept. However, convicted elucidation of how synonymous mutations exert biological consequences in oncogenesis is still lacking. By performing systematic analysis of the TNF-α signaling network model, we identify the critical dose which separates the cell survival and apoptosis regions and define the sensitive parameters with single-parameter sensitivity analysis. Combining with the cancer-related mutation spectra obtained from 9 cancers, our results hint that, similar as missense and nonsense mutations, synonymous mutations are also strongly correlated with the parameter sensitivity of the critical dose, providing possible causal mechanism of the mutations in cancer development. Based on such a correlation, we furthermore dissect that members of caspases family proteases (caspase3, 6, 8) could jointly inhibit NFκB activation, providing efficient pro-apoptotic behavior. Thus, we argue that apoptosis module could suppress survival module through negative feedback of caspases family on NFκB.
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197
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Shin WH, Christoffer CW, Kihara D. In silico structure-based approaches to discover protein-protein interaction-targeting drugs. Methods 2017; 131:22-32. [PMID: 28802714 PMCID: PMC5683929 DOI: 10.1016/j.ymeth.2017.08.006] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Revised: 08/08/2017] [Accepted: 08/08/2017] [Indexed: 02/07/2023] Open
Abstract
A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.
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Affiliation(s)
- Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | | | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
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198
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Bruzzoni-Giovanelli H, Alezra V, Wolff N, Dong CZ, Tuffery P, Rebollo A. Interfering peptides targeting protein-protein interactions: the next generation of drugs? Drug Discov Today 2017; 23:272-285. [PMID: 29097277 DOI: 10.1016/j.drudis.2017.10.016] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 09/22/2017] [Accepted: 10/17/2017] [Indexed: 12/28/2022]
Abstract
Protein-protein interactions (PPIs) are well recognized as promising therapeutic targets. Consequently, interfering peptides (IPs) - natural or synthetic peptides capable of interfering with PPIs - are receiving increasing attention. Given their physicochemical characteristics, IPs seem better suited than small molecules to interfere with the large surfaces implicated in PPIs. Progress on peptide administration, stability, biodelivery and safety are also encouraging the interest in peptide drug development. The concept of IPs has been validated for several PPIs, generating great expectations for their therapeutic potential. Here, we describe approaches and methods useful for IPs identification and in silico, physicochemical and biological-based strategies for their design and optimization. Selected promising in-vivo-validated examples are described and advantages, limitations and potential of IPs as therapeutic tools are discussed.
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Affiliation(s)
- Heriberto Bruzzoni-Giovanelli
- Université Paris 7 Denis Diderot, Université Sorbonne Paris Cité, Paris, France; UMRS 1160 Inserm, Paris, France; Centre d'Investigation Clinique 1427 Inserm/AP-HP Hôpital Saint Louis, Paris, France
| | - Valerie Alezra
- Université Paris-Sud, Laboratoire de Méthodologie, Synthèse et Molécules Thérapeutiques, ICMMO, UMR 8182, CNRS, Université Paris-Saclay, Faculté des Sciences d'Orsay, France
| | - Nicolas Wolff
- Unité de Résonance Magnétique Nucléaire des Biomolécules, CNRS, UMR 3528, Institut Pasteur, F-75015 Paris, France
| | - Chang-Zhi Dong
- Université Paris 7 Denis Diderot, Université Sorbonne Paris Cité, Paris, France; ITODYS, UMR 7086 CNRS, Paris, France
| | - Pierre Tuffery
- Université Paris 7 Denis Diderot, Université Sorbonne Paris Cité, Paris, France; Inserm UMR-S 973, RPBS, Paris, France
| | - Angelita Rebollo
- CIMI Paris, UPMC, Inserm U1135, Hôpital Pitié Salpétrière, Paris, France.
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199
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Stevers LM, Sijbesma E, Botta M, MacKintosh C, Obsil T, Landrieu I, Cau Y, Wilson AJ, Karawajczyk A, Eickhoff J, Davis J, Hann M, O'Mahony G, Doveston RG, Brunsveld L, Ottmann C. Modulators of 14-3-3 Protein-Protein Interactions. J Med Chem 2017; 61:3755-3778. [PMID: 28968506 PMCID: PMC5949722 DOI: 10.1021/acs.jmedchem.7b00574] [Citation(s) in RCA: 184] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
![]()
Direct
interactions between proteins are essential for the regulation
of their functions in biological pathways. Targeting the complex network
of protein–protein interactions (PPIs) has now been widely
recognized as an attractive means to therapeutically intervene in
disease states. Even though this is a challenging endeavor and PPIs
have long been regarded as “undruggable” targets, the
last two decades have seen an increasing number of successful examples
of PPI modulators, resulting in growing interest in this field. PPI
modulation requires novel approaches and the integrated efforts of
multiple disciplines to be a fruitful strategy. This perspective focuses
on the hub-protein 14-3-3, which has several hundred identified protein
interaction partners, and is therefore involved in a wide range of
cellular processes and diseases. Here, we aim to provide an integrated
overview of the approaches explored for the modulation of 14-3-3 PPIs
and review the examples resulting from these efforts in both inhibiting
and stabilizing specific 14-3-3 protein complexes by small molecules,
peptide mimetics, and natural products.
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Affiliation(s)
- Loes M Stevers
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems (ICMS) , Eindhoven University of Technology , P.O. Box 513, 5600 MB , Eindhoven , The Netherlands
| | - Eline Sijbesma
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems (ICMS) , Eindhoven University of Technology , P.O. Box 513, 5600 MB , Eindhoven , The Netherlands
| | - Maurizio Botta
- Department of Biotechnology, Chemistry and Pharmacy , University of Siena , Via Aldo Moro 2 , 53100 Siena , Italy
| | - Carol MacKintosh
- Division of Cell and Developmental Biology, School of Life Sciences , University of Dundee , Dundee DD1 4HN , United Kingdom
| | - Tomas Obsil
- Department of Physical and Macromolecular Chemistry, Faculty of Science , Charles University , Prague 116 36 , Czech Republic
| | | | - Ylenia Cau
- Department of Biotechnology, Chemistry and Pharmacy , University of Siena , Via Aldo Moro 2 , 53100 Siena , Italy
| | - Andrew J Wilson
- School of Chemistry , University of Leeds , Woodhouse Lane , Leeds LS2 9JT , United Kingdom.,Astbury Center For Structural Molecular Biology , University of Leeds , Woodhouse Lane , Leeds LS2 9JT , United Kingdom
| | | | - Jan Eickhoff
- Lead Discovery Center GmbH , Dortmund 44227 , Germany
| | - Jeremy Davis
- UCB Celltech , 216 Bath Road , Slough SL1 3WE , United Kingdom
| | - Michael Hann
- GlaxoSmithKline , Gunnels Wood Road , Stevenage, Hertfordshire SG1 2NY , United Kingdom
| | - Gavin O'Mahony
- Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development Biotech Unit , AstraZeneca Gothenburg , Pepparedsleden 1 , SE-431 83 Mölndal , Sweden
| | - Richard G Doveston
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems (ICMS) , Eindhoven University of Technology , P.O. Box 513, 5600 MB , Eindhoven , The Netherlands
| | - Luc Brunsveld
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems (ICMS) , Eindhoven University of Technology , P.O. Box 513, 5600 MB , Eindhoven , The Netherlands
| | - Christian Ottmann
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems (ICMS) , Eindhoven University of Technology , P.O. Box 513, 5600 MB , Eindhoven , The Netherlands.,Department of Chemistry , University of Duisburg-Essen , Universitätstraße 7 , 45141 Essen , Germany
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200
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Drew K, Müller CL, Bonneau R, Marcotte EM. Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets. PLoS Comput Biol 2017; 13:e1005625. [PMID: 29023445 PMCID: PMC5638211 DOI: 10.1371/journal.pcbi.1005625] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 06/06/2017] [Indexed: 12/21/2022] Open
Abstract
Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method’s value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available. Proteins physically associate into complexes in order to carry out the essential functions of life. Knowing how proteins are physically arranged three dimensionally in these complexes provides clues towards how they work. In principle, the associations between proteins in large-scale proteomics datasets should often reflect direct physical contacts between proteins in each complex. Here, we describe a statistical method to discover which subunits within complexes directly contact each other based on their co-purification behavior in published co-fractionation mass spectrometry datasets. Within our predictions, we recover many known protein-protein contacts, serving to validate our method, as well as unknown contacts that can inform future studies of these complexes. Specifically, we observe confident contacts between subunits within the exocyst and tRNA multi-synthetase complexes, two complexes that have incomplete structural information. Using our method, we further provide structural information for a previously missing subunit of the EKC/KEOPS complex. We anticipate that this method and the associated predictions will help to better inform our understanding of the functions and structures of diverse protein complexes.
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Affiliation(s)
- Kevin Drew
- Center for Systems and Synthetic Biology, Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, United States of America
- * E-mail: (KD); (CLM); (EMM)
| | - Christian L. Müller
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, United States of America
- * E-mail: (KD); (CLM); (EMM)
| | - Richard Bonneau
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, United States of America
- New York University Center for Genomics and Systems Biology, New York University, New York, NY, United States of America
| | - Edward M. Marcotte
- Center for Systems and Synthetic Biology, Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, United States of America
- * E-mail: (KD); (CLM); (EMM)
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