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Idrees S, Paudel KR. Proteome-wide assessment of human interactome as a source of capturing domain-motif and domain-domain interactions. J Cell Commun Signal 2024; 18:e12014. [PMID: 38545252 PMCID: PMC10964934 DOI: 10.1002/ccs3.12014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 12/11/2023] [Indexed: 06/29/2024] Open
Abstract
Protein-protein interactions (PPIs) play a crucial role in various biological processes by establishing domain-motif (DMI) and domain-domain interactions (DDIs). While the existence of real DMIs/DDIs is generally assumed, it is rarely tested; therefore, this study extensively compared high-throughput methods and public PPI repositories as sources for DMI and DDI prediction based on the assumption that the human interactome provides sufficient data for the reliable identification of DMIs and DDIs. Different datasets from leading high-throughput methods (Yeast two-hybrid [Y2H], Affinity Purification coupled Mass Spectrometry [AP-MS], and Co-fractionation-coupled Mass Spectrometry) were assessed for their ability to capture DMIs and DDIs using known DMI/DDI information. High-throughput methods were not notably worse than PPI databases and, in some cases, appeared better. In conclusion, all PPI datasets demonstrated significant enrichment in DMIs and DDIs (p-value <0.001), establishing Y2H and AP-MS as reliable methods for predicting these interactions. This study provides valuable insights for biologists in selecting appropriate methods for predicting DMIs, ultimately aiding in SLiM discovery.
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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular SciencesUniversity of New South WalesSydneyNew South WalesAustralia
- Centre for InflammationCentenary Institute and the University of Technology SydneySchool of Life SciencesFaculty of ScienceSydneyNew South WalesAustralia
| | - Keshav Raj Paudel
- Centre for InflammationCentenary Institute and the University of Technology SydneySchool of Life SciencesFaculty of ScienceSydneyNew South WalesAustralia
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2
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Kosoglu K, Aydin Z, Tuncbag N, Gursoy A, Keskin O. Structural coverage of the human interactome. Brief Bioinform 2023; 25:bbad496. [PMID: 38180828 PMCID: PMC10768791 DOI: 10.1093/bib/bbad496] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/16/2023] [Accepted: 11/30/2023] [Indexed: 01/07/2024] Open
Abstract
Complex biological processes in cells are embedded in the interactome, representing the complete set of protein-protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes' molecular details. Therefore, knowing the structural coverage of the interactome is important to show the current limitations. Structural modeling of protein-protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) were included. We then collected the interactions from the literature and databases to form the reference human interactome, resulting in 117 897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI and 73.62% and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome.
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Affiliation(s)
- Kayra Kosoglu
- Computational Sciences and Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Zeynep Aydin
- Computational Sciences and Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Nurcan Tuncbag
- School of Medicine, Koc University, 34450 Istanbul, Turkey
- Department of Chemical and Biological Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Attila Gursoy
- Department of Computer Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
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3
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Idrees S, Paudel KR, Sadaf T, Hansbro PM. How different viruses perturb host cellular machinery via short linear motifs. EXCLI JOURNAL 2023; 22:1113-1128. [PMID: 38054205 PMCID: PMC10694346 DOI: 10.17179/excli2023-6328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/18/2023] [Indexed: 12/07/2023]
Abstract
The virus interacts with its hosts by developing protein-protein interactions. Most viruses employ protein interactions to imitate the host protein: A viral protein with the same amino acid sequence or structure as the host protein attaches to the host protein's binding partner and interferes with the host protein's pathways. Being opportunistic, viruses have evolved to manipulate host cellular mechanisms by mimicking short linear motifs. In this review, we shed light on the current understanding of mimicry via short linear motifs and focus on viral mimicry by genetically different viral subtypes by providing recent examples of mimicry evidence and how high-throughput methods can be a reliable source to study SLiM-mediated viral mimicry.
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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Tayyaba Sadaf
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Philip M. Hansbro
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
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4
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Valiente G. The Landscape of Virus-Host Protein–Protein Interaction Databases. Front Microbiol 2022; 13:827742. [PMID: 35910656 PMCID: PMC9335289 DOI: 10.3389/fmicb.2022.827742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/17/2022] [Indexed: 11/25/2022] Open
Abstract
Knowledge of virus-host interactomes has advanced exponentially in the last decade by the use of high-throughput screening technologies to obtain a more comprehensive landscape of virus-host protein–protein interactions. In this article, we present a systematic review of the available virus-host protein–protein interaction database resources. The resources covered in this review are both generic virus-host protein–protein interaction databases and databases of protein–protein interactions for a specific virus or for those viruses that infect a particular host. The databases are reviewed on the basis of the specificity for a particular virus or host, the number of virus-host protein–protein interactions included, and the functionality in terms of browse, search, visualization, and download. Further, we also analyze the overlap of the databases, that is, the number of virus-host protein–protein interactions shared by the various databases, as well as the structure of the virus-host protein–protein interaction network, across viruses and hosts.
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5
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Sharma A, Singh B. AE-LGBM: Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and LightGBM. Comput Biol Med 2020; 125:103964. [DOI: 10.1016/j.compbiomed.2020.103964] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/03/2020] [Accepted: 08/07/2020] [Indexed: 01/28/2023]
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6
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Taha MS, Haghighi F, Stefanski A, Nakhaei-Rad S, Kazemein Jasemi NS, Al Kabbani MA, Görg B, Fujii M, Lang PA, Häussinger D, Piekorz RP, Stühler K, Ahmadian MR. Novel FMRP interaction networks linked to cellular stress. FEBS J 2020; 288:837-860. [PMID: 32525608 DOI: 10.1111/febs.15443] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 04/09/2020] [Accepted: 06/03/2020] [Indexed: 12/12/2022]
Abstract
Silencing of the fragile X mental retardation 1 (FMR1) gene and consequently lack of synthesis of FMR protein (FMRP) are associated with fragile X syndrome, which is one of the most prevalent inherited intellectual disabilities, with additional roles in increased viral infection, liver disease, and reduced cancer risk. FMRP plays critical roles in chromatin dynamics, RNA binding, mRNA transport, and mRNA translation. However, the underlying molecular mechanisms, including the (sub)cellular FMRP protein networks, remain elusive. Here, we employed affinity pull-down and quantitative LC-MS/MS analyses with FMRP. We identified known and novel candidate FMRP-binding proteins as well as protein complexes. FMRP interacted with 180 proteins, 28 of which interacted with its N terminus. Interaction with the C terminus of FMRP was observed for 102 proteins, and 48 proteins interacted with both termini. This FMRP interactome comprises known FMRP-binding proteins, including the ribosomal proteins FXR1P, NUFIP2, Caprin-1, and numerous novel FMRP candidate interacting proteins that localize to different subcellular compartments, including CARF, LARP1, LEO1, NOG2, G3BP1, NONO, NPM1, SKIP, SND1, SQSTM1, and TRIM28. Our data considerably expand the protein and RNA interaction networks of FMRP, which thereby suggest that, in addition to its known functions, FMRP participates in transcription, RNA metabolism, ribonucleoprotein stress granule formation, translation, DNA damage response, chromatin dynamics, cell cycle regulation, ribosome biogenesis, miRNA biogenesis, and mitochondrial organization. Thus, FMRP seems associated with multiple cellular processes both under normal and cell stress conditions in neuronal as well as non-neuronal cell types, as exemplified by its role in the formation of stress granules.
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Affiliation(s)
- Mohamed S Taha
- Institute of Biochemistry and Molecular Biology II, Medical Faculty of the Heinrich Heine University, Düsseldorf, Germany.,Research on Children with Special Needs Department, Medical Research Branch, National Research Centre, Cairo, Egypt
| | - Fereshteh Haghighi
- Institute of Biochemistry and Molecular Biology II, Medical Faculty of the Heinrich Heine University, Düsseldorf, Germany
| | - Anja Stefanski
- Molecular Proteomics Laboratory, Heinrich Heine-University, Düsseldorf, Germany
| | - Saeideh Nakhaei-Rad
- Institute of Biochemistry and Molecular Biology II, Medical Faculty of the Heinrich Heine University, Düsseldorf, Germany
| | - Neda S Kazemein Jasemi
- Institute of Biochemistry and Molecular Biology II, Medical Faculty of the Heinrich Heine University, Düsseldorf, Germany
| | - Mohamed Aghyad Al Kabbani
- Institute of Biochemistry and Molecular Biology II, Medical Faculty of the Heinrich Heine University, Düsseldorf, Germany
| | - Boris Görg
- Clinic of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty of the Heinrich Heine-University, Düsseldorf, Germany
| | - Masahiro Fujii
- Division of Virology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Phillip A Lang
- Department of Molecular Medicine II, Medical Faculty, Heinrich Heine-University, Düsseldorf, Germany
| | - Dieter Häussinger
- Clinic of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty of the Heinrich Heine-University, Düsseldorf, Germany
| | - Roland P Piekorz
- Institute of Biochemistry and Molecular Biology II, Medical Faculty of the Heinrich Heine University, Düsseldorf, Germany
| | - Kai Stühler
- Molecular Proteomics Laboratory, Heinrich Heine-University, Düsseldorf, Germany
| | - Mohammad R Ahmadian
- Institute of Biochemistry and Molecular Biology II, Medical Faculty of the Heinrich Heine University, Düsseldorf, Germany
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7
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Profiling Optimal Conditions for Capturing EDEM Proteins Complexes in Melanoma Using Mass Spectrometry. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1140:155-167. [PMID: 31347047 DOI: 10.1007/978-3-030-15950-4_9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Endoplasmic reticulum (ER) resident and secretory proteins that fail to reach their native conformation are selected for degradation through the ER-Associated Degradation (ERAD) pathway. The ER degradation-enhancing alpha-mannosidase-like proteins (EDEMs) were shown to be involved in this pathway but their precise role is still under investigation. Mass spectrometry analysis has contributed significantly to the characterization of protein complexes in the last years. The recent advancements in instrumentation, especially within resolution and speed can provide unique insights concerning the molecular architecture of protein-protein interactions in systems biology. Previous reports have suggested that several protein complexes in ERAD are sensitive to the extraction conditions. Indeed, whilst EDEM proteins can be recovered in most detergents, some of their partners are not solubilized, which further emphasizes the importance of the experimental setup. Here, we define such dynamic interactions of EDEM proteins by employing offline protein fractionation, nanoLC-MS/MS and describe how mass spectrometry can contribute to the characterization of such complexes, particularly within a disease context like melanoma.
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8
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Mei S, Flemington EK, Zhang K. A computational framework for distinguishing direct versus indirect interactions in human functional protein-protein interaction networks. Integr Biol (Camb) 2018; 9:595-606. [PMID: 28524201 DOI: 10.1039/c7ib00013h] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Recognition of indirect interactions is instrumental to in silico reconstruction of signaling pathways and sheds light on the exploration of unknown physical paths between two indirectly interacting genes. However, very limited computational methods have explicitly exploited the indirect interactions with experimental evidence thus far. In this work, we attempt to distinguish direct versus indirect interactions in human functional protein-protein interaction (PPI) networks via a predictive l2-regularized logistic regression model built on the experimental data. The l2-regularized logistic regression method is adopted to counteract the potential homolog noise and reduce the computational complexity on large training data. Computational results show that the proposed model demonstrates promising performance even though the training data are highly skewed. From the 304 799 PPIs that are curated in several databases, the proposed method detects 23 131 indirect interactions, most of which have been verified by the breadth-first graph search algorithm to find dozens of physical paths between the interacting partners. Pathway enrichment analysis shows that most of the physical paths can be mapped onto more than one human signaling pathway, indicating that there do exist a series of biochemical signals between the two indirectly interacting genes. The interactome-scale computational results promise to provide useful cues to the following applications: (1) exploration of unknown physical PPIs or physical paths between two indirectly interacting genes; (2) amending or extending the existing signaling pathways; (3) recognition of the physical PPIs for druggable target discovery.
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Affiliation(s)
- Suyu Mei
- Software College, Shenyang Normal University, Shenyang, 110034, China.
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9
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Martins F, Marafona AM, Pereira CD, Müller T, Loosse C, Kolbe K, da Cruz E Silva OAB, Rebelo S. Identification and characterization of the BRI2 interactome in the brain. Sci Rep 2018; 8:3548. [PMID: 29476059 PMCID: PMC5824958 DOI: 10.1038/s41598-018-21453-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 01/05/2018] [Indexed: 01/16/2023] Open
Abstract
BRI family proteins are ubiquitous type II transmembrane proteins but BRI2 is highly expressed in some neuronal tissues. Possible BRI2 functions include neuronal maturation and differentiation. Protein complexes appear to be important in mediating its functions. Previously described BRI2 interactors include the Alzheimer's amyloid precursor protein and protein phosphatase 1, but clearly the identification of novel interactors provides an important tool to understand the role and function of BRI2. To this end three rat brain regions (cerebellum, hippocampus, and cerebral cortex) were processed by BRI2 immunoprecipitation; co-precipitating proteins were identified by Nano-HPLC-MS/MS. The pool of the brain regions resulted in 511 BRI2 interacting proteins (BRI2 brain interactome) of which 120 were brain specific and 49 involved in neuronal differentiation. Brain region-specific analyses were also carried out for cerebellum, hippocampus, and cerebral cortex. Several novel BRI2 interactors were identified among them DLG4/PSD-95, which is singularly important as it places BRI2 in the postsynaptic compartment. This interaction was validated as well as the interaction with GAP-43 and synaptophysin. In essence, the resulting BRI2 brain interactome, associates this protein with neurite outgrowth and neuronal differentiation, as well as synaptic signalling and plasticity. It follows that further studies should address BRI2 particularly given its relevance to neuropathological conditions.
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Affiliation(s)
- Filipa Martins
- Neuroscience and Signalling Laboratory, Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, Aveiro, Portugal
| | - Ana M Marafona
- Neuroscience and Signalling Laboratory, Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, Aveiro, Portugal
| | - Cátia D Pereira
- Neuroscience and Signalling Laboratory, Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, Aveiro, Portugal
| | - Thorsten Müller
- Leibniz-Institut für Analytische Wissenschaften -ISAS- e. V., Dortmund, Germany
- Cell Signaling, Department of Molecular Biochemistry, Faculty of Chemistry and Biochemistry, Ruhr University Bochum, Universitätsstr. 150, 44780, Bochum, Germany
- Institute of Psychiatric Phenomics and Genomics, Clinical Center of the University of Munich, Nussbaumstr. 7, 80336, Munich, Germany
| | - Christina Loosse
- Leibniz-Institut für Analytische Wissenschaften -ISAS- e. V., Dortmund, Germany
| | - Katharina Kolbe
- Leibniz-Institut für Analytische Wissenschaften -ISAS- e. V., Dortmund, Germany
- Cell Signaling, Department of Molecular Biochemistry, Faculty of Chemistry and Biochemistry, Ruhr University Bochum, Universitätsstr. 150, 44780, Bochum, Germany
- Institute of Psychiatric Phenomics and Genomics, Clinical Center of the University of Munich, Nussbaumstr. 7, 80336, Munich, Germany
| | - Odete A B da Cruz E Silva
- Neuroscience and Signalling Laboratory, Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, Aveiro, Portugal
| | - Sandra Rebelo
- Neuroscience and Signalling Laboratory, Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, Aveiro, Portugal.
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10
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Tian B, Zhao C, Gu F, He Z. A two-step framework for inferring direct protein-protein interaction network from AP-MS data. BMC SYSTEMS BIOLOGY 2017; 11:82. [PMID: 28950876 PMCID: PMC5615237 DOI: 10.1186/s12918-017-0452-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Background Affinity purification-mass spectrometry (AP-MS) has been widely used for generating bait-prey data sets so as to identify underlying protein-protein interactions and protein complexes. However, the AP-MS data sets in terms of bait-prey pairs are highly noisy, where candidate pairs contain many false positives. Recently, numerous computational methods have been developed to identify genuine interactions from AP-MS data sets. However, most of these methods aim at removing false positives that contain contaminants, ignoring the distinction between direct interactions and indirect interactions. Results In this paper, we present an initialization-and-refinement framework for inferring direct PPI networks from AP-MS data, in which an initial network is first generated with existing scoring methods and then a refined network is constructed by the application of indirect association removal methods. Experimental results on several real AP-MS data sets show that our method is capable of identifying more direct interactions than traditional scoring methods. Conclusions The proposed framework is sufficiently general to incorporate any feasible methods in each step so as to have potential for handling different types of AP-MS data in the future applications. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0452-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bo Tian
- School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China
| | - Can Zhao
- School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China
| | - Feiyang Gu
- School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China
| | - Zengyou He
- School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China. .,Key Laboratory for Ubiquitous Network and Service Software of Liaoning, Tuqiang Road 321, Dalian, 116600, China.
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11
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Kutzera J, Smilde AK, Wilderjans TF, Hoefsloot HCJ. Towards a Hierarchical Strategy to Explore Multi-Scale IP/MS Data for Protein Complexes. PLoS One 2015; 10:e0139704. [PMID: 26448546 PMCID: PMC4598013 DOI: 10.1371/journal.pone.0139704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 09/16/2015] [Indexed: 11/24/2022] Open
Abstract
Protein interaction in cells can be described at different levels. At a low interaction level, proteins function together in small, stable complexes and at a higher level, in sets of interacting complexes. All interaction levels are crucial for the living organism, and one of the challenges in proteomics is to measure the proteins at their different interaction levels. One common method for such measurements is immunoprecipitation followed by mass spectrometry (IP/MS), which has the potential to probe the different protein interaction forms. However, IP/MS data are complex because proteins, in their diverse interaction forms, manifest themselves in different ways in the data. Numerous bioinformatic tools for finding protein complexes in IP/MS data are currently available, but most tools do not provide information about the interaction level of the discovered complexes, and no tool is geared specifically to unraveling and visualizing these different levels. We present a new bioinformatic tool to explore IP/MS datasets for protein complexes at different interaction levels and show its performance on several real–life datasets. Our tool creates clusters that represent protein complexes, but unlike previous methods, it arranges them in a tree–shaped structure, reporting why specific proteins are predicted to build a complex and where it can be divided into smaller complexes. In every data analysis method, parameters have to be chosen. Our method can suggest values for its parameters and comes with adapted visualization tools that display the effect of the parameters on the result. The tools provide fast graphical feedback and allow the user to interact with the data by changing the parameters and examining the result. The tools also allow for exploring the different organizational levels of the protein complexes in a given dataset. Our method is available as GNU-R source code and includes examples at www.bdagroup.nl.
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Affiliation(s)
- Joachim Kutzera
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Netherlands Institute for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands
- * E-mail:
| | - Age K. Smilde
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Netherlands Institute for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands
| | - Tom F. Wilderjans
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
- Faculty of Social and Behavioural Sciences, Leiden University, Leiden, The Netherlands
| | - Huub C. J. Hoefsloot
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Netherlands Institute for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands
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12
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Zhang XF, Ou-Yang L, Hu X, Dai DQ. Identifying binary protein-protein interactions from affinity purification mass spectrometry data. BMC Genomics 2015; 16:745. [PMID: 26438428 PMCID: PMC4595009 DOI: 10.1186/s12864-015-1944-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Accepted: 09/22/2015] [Indexed: 02/04/2023] Open
Abstract
Background The identification of protein-protein interactions contributes greatly to the understanding of functional organization within cells. With the development of affinity purification-mass spectrometry (AP-MS) techniques, several computational scoring methods have been proposed to detect protein interactions from AP-MS data. However, most of the current methods focus on the detection of co-complex interactions and do not discriminate between direct physical interactions and indirect interactions. Consequently, less is known about the precise physical wiring diagram within cells. Results In this paper, we develop a Binary Interaction Network Model (BINM) to computationally identify direct physical interactions from co-complex interactions which can be inferred from purification data using previous scoring methods. This model provides a mathematical framework for capturing topological relationships between direct physical interactions and observed co-complex interactions. It reassigns a confidence score to each observed interaction to indicate its propensity to be a direct physical interaction. Then observed interactions with high confidence scores are predicted as direct physical interactions. We run our model on two yeast co-complex interaction networks which are constructed by two different scoring methods on a same combined AP-MS data. The direct physical interactions identified by various methods are comprehensively benchmarked against different reference sets that provide both direct and indirect evidence for physical contacts. Experiment results show that our model has a competitive performance over the state-of-the-art methods. Conclusions According to the results obtained in this study, BINM is a powerful scoring method that can solely use network topology to predict direct physical interactions from AP-MS data. This study provides us an alternative approach to explore the information inherent in AP-MS data. The software can be downloaded from https://github.com/Zhangxf-ccnu/BINM. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1944-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiao-Fei Zhang
- School of Mathematics and Statistics, Central China Normal University, Luoyu Road, Wuhan, 430079, China.
| | - Le Ou-Yang
- Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China.
| | - Xiaohua Hu
- School of Computer, Central China Normal University, 774 Luoyu Road, Wuhan, 430079, China. .,College of Information Science and Technology, Drexel University, Chestnut Street, Philadelphia, 19104, USA.
| | - Dao-Qing Dai
- Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China.
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13
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Teng B, Zhao C, Liu X, He Z. Network inference from AP-MS data: computational challenges and solutions. Brief Bioinform 2014; 16:658-74. [DOI: 10.1093/bib/bbu038] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 09/30/2014] [Indexed: 02/04/2023] Open
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Esmaielbeiki R, Nebel JC. Scoring docking conformations using predicted protein interfaces. BMC Bioinformatics 2014; 15:171. [PMID: 24906633 PMCID: PMC4057934 DOI: 10.1186/1471-2105-15-171] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Accepted: 05/29/2014] [Indexed: 12/22/2022] Open
Abstract
Background Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). Results First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. Conclusion Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations.
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Affiliation(s)
- Reyhaneh Esmaielbeiki
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
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Kutzera J, Hoefsloot HCJ, Malovannaya A, Smit AB, Van Mechelen I, Smilde AK. Inferring protein-protein interaction complexes from immunoprecipitation data. BMC Res Notes 2013; 6:468. [PMID: 24237943 PMCID: PMC3874675 DOI: 10.1186/1756-0500-6-468] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Accepted: 10/31/2013] [Indexed: 11/26/2022] Open
Abstract
Background Protein–protein interactions in cells are widely explored using small–scale experiments. However, the search for protein complexes and their interactions in data from high throughput experiments such as immunoprecipitation is still a challenge. We present "4N", a novel method for detecting protein complexes in such data. Our method is a heuristic algorithm based on Near Neighbor Network (3N) clustering. It is written in R, it is faster than model-based methods, and has only a small number of tuning parameters. We explain the application of our new method to real immunoprecipitation results and two artificial datasets. We show that the method can infer protein complexes from protein immunoprecipitation datasets of different densities and sizes. Findings 4N was applied on the immunoprecipitation dataset that was presented by the authors of the original 3N in Cell 145:787–799, 2011. The test with our method shows that it can reproduce the original clustering results with fewer manually adapted parameters and, in addition, gives direct insight into the complex–complex interactions. We also tested 4N on the human "Tip49a/b" dataset. We conclude that 4N can handle the contaminants and can correctly infer complexes from this very dense dataset. Further tests were performed on two artificial datasets of different sizes. We proved that the method predicts the reference complexes in the two artificial datasets with high accuracy, even when the number of samples is reduced. Conclusions 4N has been implemented in R. We provide the sourcecode of 4N and a user-friendly toolbox including two example calculations. Biologists can use this 4N-toolbox even if they have a limited knowledge of R. There are only a few tuning parameters to set, and each of these parameters has a biological interpretation. The run times for medium scale datasets are in the order of minutes on a standard desktop PC. Large datasets can typically be analyzed within a few hours.
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Affiliation(s)
- Joachim Kutzera
- Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
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Kuzu G, Gursoy A, Nussinov R, Keskin O. Exploiting conformational ensembles in modeling protein-protein interactions on the proteome scale. J Proteome Res 2013; 12:2641-53. [PMID: 23590674 PMCID: PMC3685852 DOI: 10.1021/pr400006k] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than "blind" docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited an ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested "difficult" cases in a docking-benchmark data set, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from ~26 to 66%, and 57% of the interactions were successfully predicted in an "unbiased" scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome scale. We constructed the structural network of ERK interacting proteins as a case study.
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Affiliation(s)
- Guray Kuzu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
| | - Ruth Nussinov
- Basic Science Program, SAIC-Frederick, Inc. National Cancer Institute, Center for Cancer Research Nanobiology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702
- Sackler Inst. of Molecular Medicine Department of Human Genetics and Molecular Medicine Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ozlem Keskin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
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Kuzu G, Keskin O, Gursoy A, Nussinov R. Constructing structural networks of signaling pathways on the proteome scale. Curr Opin Struct Biol 2012; 22:367-77. [PMID: 22575757 DOI: 10.1016/j.sbi.2012.04.004] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2011] [Revised: 03/20/2012] [Accepted: 04/18/2012] [Indexed: 11/30/2022]
Abstract
Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces.
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Affiliation(s)
- Guray Kuzu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
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