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Li M, He Q, Yang C, Ma J, He F, Chen T, Zhu Y. The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction. BMC Genomics 2021; 22:544. [PMID: 34789143 PMCID: PMC8596923 DOI: 10.1186/s12864-021-07827-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 06/21/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND With the rapid increase in the amount of Protein-Protein Interaction (PPI) data, the establishment of an event-centered PPI ontology that contains temporal and spatial vocabularies is urgently needed to clarify PPI biological annotations. In this paper, we propose a precisely designed schema - PPIO (PPI Ontology) for representing the biological context of PPIs. RESULTS Inspired by the event model and the distinct characteristics of PPI events, PPIO consists of six core aspects of the information required for reporting a PPI event, including the interactor (who), the biological process (when), the subcellular location (where), the interaction type (how), the biological function (what) and the detection method (which). PPIO is implemented through the integration of appropriate terms from the corresponding vocabularies/ontologies, e.g., Gene Ontology, Protein Ontology, PSI-MI/MOD, etc. To assess PPIO, an approach based on PPIO in developed to extract PPI biological annotations from an open standard corpus "BioCreAtIvE-PPI". The experiment results demonstrate PPIO's high performance, a precision of 0.69, a recall of 0.72 and an F-score of 0.70. CONCLUSIONS PPIO is a well-constructed essential ontology in the interpretation of PPI biological context. The results of the experiments conducted on the BioCreAtIvE corpus demonstrate that PPIO is able to facilitate PPI annotation extraction from biomedical literature effectively and enrich essential annotation for PPIs.
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Affiliation(s)
- Mansheng Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, 102206, Beijing, China
| | - Qiang He
- School of Software and Electrical Engineering, Swinburne University of Technology, 3122, Melbourne, Victoria, Australia
| | - Chunyuan Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, 102206, Beijing, China
| | - Jie Ma
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, 102206, Beijing, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, 102206, Beijing, China
| | - Tao Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, 102206, Beijing, China.
| | - Yunping Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, 102206, Beijing, China.
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2
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Elayan H, Eckford AW, Adve RS. Information Rates of Controlled Protein Interactions Using Terahertz Communication. IEEE Trans Nanobioscience 2020; 20:9-19. [PMID: 32886612 DOI: 10.1109/tnb.2020.3021825] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this work, we present a paradigm bridging electromagnetic (EM) and molecular communication through a stimuli-responsive intra-body model. It has been established that protein molecules, which play a key role in governing cell behavior, can be selectively stimulated using Terahertz (THz) band frequencies. By triggering protein vibrational modes using THz waves, we induce changes in protein conformation, resulting in the activation of a controlled cascade of biochemical and biomechanical events. To analyze such an interaction, we formulate a communication system composed of a nanoantenna transmitter and a protein receiver. We adopt a Markov chain model to account for protein stochasticity with transition rates governed by the nanoantenna force. Both two-state and multi-state protein models are presented to depict different biological configurations. Closed form expressions for the mutual information of each scenario is derived and maximized to find the capacity between the input nanoantenna force and the protein state. The results we obtain indicate that controlled protein signaling provides a communication platform for information transmission between the nanoantenna and the protein with a clear physical significance. The analysis reported in this work should further research into the EM-based control of protein networks.
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3
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Torchala M, Gerguri T, Chaleil RAG, Gordon P, Russell F, Keshani M, Bates PA. Enhanced sampling of protein conformational states for dynamic cross-docking within the protein-protein docking server SwarmDock. Proteins 2020; 88:962-972. [PMID: 31697436 PMCID: PMC7496321 DOI: 10.1002/prot.25851] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 10/02/2019] [Accepted: 11/03/2019] [Indexed: 12/12/2022]
Abstract
The formation of specific protein-protein interactions is often a key to a protein's function. During complex formation, each protein component will undergo a change in the conformational state, for some these changes are relatively small and reside primarily at the sidechain level; however, others may display notable backbone adjustments. One of the classic problems in the protein-docking field is to be able to a priori predict the extent of such conformational changes. In this work, we investigated three protocols to find the most suitable input structure conformations for cross-docking, including a robust sampling approach in normal mode space. Counterintuitively, knowledge of the theoretically best combination of normal modes for unbound-bound transitions does not always lead to the best results. We used a novel spatial partitioning library, Aether Engine (see Supplementary Materials), to efficiently search the conformational states of 56 receptor/ligand pairs, including a recent CAPRI target, in a systematic manner and selected diverse conformations as input to our automated docking server, SwarmDock, a server that allows moderate conformational adjustments during the docking process. In essence, here we present a dynamic cross-docking protocol, which when benchmarked against the simpler approach of just docking the unbound components shows a 10% uplift in the quality of the top docking pose.
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Affiliation(s)
- Mieczyslaw Torchala
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonUK
- Hadean Supercomputing LtdLondonUK
| | - Tereza Gerguri
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonUK
| | | | | | | | | | - Paul A. Bates
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonUK
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4
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Thanasomboon R, Kalapanulak S, Netrphan S, Saithong T. Exploring dynamic protein-protein interactions in cassava through the integrative interactome network. Sci Rep 2020; 10:6510. [PMID: 32300157 PMCID: PMC7162878 DOI: 10.1038/s41598-020-63536-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 04/01/2020] [Indexed: 01/01/2023] Open
Abstract
Protein-protein interactions (PPIs) play an essential role in cellular regulatory processes. Despite, in-depth studies to uncover the mystery of PPI-mediated regulations are still lacking. Here, an integrative interactome network (MePPI-Ux) was obtained by incorporating expression data into the improved genome-scale interactome network of cassava (MePPI-U). The MePPI-U, constructed by both interolog- and domain-based approaches, contained 3,638,916 interactions and 24,590 proteins (59% of proteins in the cassava AM560 genome version 6). After incorporating expression data as information of state, the MePPI-U rewired to represent condition-dependent PPIs (MePPI-Ux), enabling us to envisage dynamic PPIs (DPINs) that occur at specific conditions. The MePPI-Ux was exploited to demonstrate timely PPIs of cassava under various conditions, namely drought stress, brown streak virus (CBSV) infection, and starch biosynthesis in leaf/root tissues. MePPI-Uxdrought and MePPI-UxCBSV suggested involved PPIs in response to stress. MePPI-UxSB,leaf and MePPI-UxSB,root suggested the involvement of interactions among transcription factor proteins in modulating how leaf or root starch is synthesized. These findings deepened our knowledge of the regulatory roles of PPIs in cassava and would undeniably assist targeted breeding efforts to improve starch quality and quantity.
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Affiliation(s)
- Ratana Thanasomboon
- Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand.,Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand
| | - Saowalak Kalapanulak
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.,Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand
| | - Supatcharee Netrphan
- National Center for Genetic Engineering and Biotechnology, Pathum Thani, 12120, Thailand
| | - Treenut Saithong
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand. .,Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
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5
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Kurokawa N, Kishimoto T, Tanaka K, Kondo J, Takahashi N, Miura Y. New approach to evaluating the effects of a drug on protein complexes with quantitative proteomics, using the SILAC method and bioinformatic approach. Biosci Biotechnol Biochem 2019; 83:2034-2048. [PMID: 31282289 DOI: 10.1080/09168451.2019.1637244] [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] [Indexed: 01/30/2023]
Abstract
Protein-protein interactions (PPIs) lead the formation of protein complexes that perform biochemical reactions that maintain the living state of the living cell. Although therapeutic drugs should influence the formation of protein complexes in addition to PPI network, the methodology analyzing such influences remain to be developed. Here, we demonstrate that a new approach combining HPLC (high performance liquid chromatography) for separating protein complexes, and the SILAC (stable isotope labeling using amino acids in cell culture) method for relative protein quantification, enable us to identify the protein complexes influenced by a drug. We applied this approach to the analysis of thalidomide action on HepG2 cells, assessed the identified proteins by clustering data analyses, and assigned 135 novel protein complexes affected by the drug. We propose that this approach is applicable to elucidating the mechanisms of actions of other therapeutic drugs on the PPI network, and the formation of protein complexes.
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Affiliation(s)
- Natsuki Kurokawa
- Graduate School of Agriculture, Tokyo University of Agriculture & Technology , Fuchu-shi, Tokyo , Japan.,Innovative Research Division, Mitsubishi Tanabe Pharma Corporation , Chuo-ku , Japan
| | - Taro Kishimoto
- Innovative Research Division, Mitsubishi Tanabe Pharma Corporation , Chuo-ku , Japan
| | - Kohei Tanaka
- Innovative Research Division, Mitsubishi Tanabe Pharma Corporation , Chuo-ku , Japan
| | - Jun Kondo
- Innovative Research Division, Mitsubishi Tanabe Pharma Corporation , Chuo-ku , Japan
| | - Nobuhiro Takahashi
- Graduate School of Agriculture, Tokyo University of Agriculture & Technology , Fuchu-shi, Tokyo , Japan
| | - Yutaka Miura
- Graduate School of Agriculture, Tokyo University of Agriculture & Technology , Fuchu-shi, Tokyo , Japan
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6
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Wetmore BA, Merrick BA. Invited Review: Toxicoproteomics: Proteomics Applied to Toxicology and Pathology. Toxicol Pathol 2016; 32:619-42. [PMID: 15580702 DOI: 10.1080/01926230490518244] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Global measurement of proteins and their many attributes in tissues and biofluids defines the field of proteomics. Toxicoproteomics, as part of the larger field of toxicogenomics, seeks to identify critical proteins and pathways in biological systems that are affected by and respond to adverse chemical and environmental exposures using global protein expression technologies. Toxicoproteomics integrates 3 disciplinary areas: traditional toxicology and pathology, differential protein and gene expression analysis, and systems biology. Key topics to be reviewed are the evolution of proteomics, proteomic technology platforms and their capabilities with exemplary studies from biology and medicine, a review of over 50 recent studies applying proteomic analysis to toxicological research, and the recent development of databases designed to integrate -Omics technologies with toxicology and pathology. Proteomics is examined for its potential in discovery of new biomarkers and toxicity signatures, in mapping serum, plasma, and other biofluid proteomes, and in parallel proteomic and transcriptomic studies. The new field of toxicoproteomics is uniquely positioned toward an expanded understanding of protein expression during toxicity and environmental disease for the advancement of public health.
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Affiliation(s)
- Barbara A Wetmore
- National Center for Toxicogenomics, National Institute of Environmental Health Sciences, Research Triangle Park, North Caroline 27709, USA
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7
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Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors. Molecules 2015; 20:11569-603. [PMID: 26111183 PMCID: PMC6272567 DOI: 10.3390/molecules200611569] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 06/02/2015] [Accepted: 06/15/2015] [Indexed: 02/06/2023] Open
Abstract
Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.
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8
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Haga SW, Wu HF. Overview of software options for processing, analysis and interpretation of mass spectrometric proteomic data. JOURNAL OF MASS SPECTROMETRY : JMS 2014; 49:959-969. [PMID: 25303385 DOI: 10.1002/jms.3414] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2014] [Revised: 05/23/2014] [Accepted: 06/13/2014] [Indexed: 06/04/2023]
Abstract
Recently, the interests in proteomics have been intensively increased, and the proteomic methods have been widely applied to many problems in cell biology. If the age of 1990s is considered to be a decade of genomics, we can claim that the following years of the new century is a decade of proteomics. The rapid evolution of proteomics has continued through these years, with a series of innovations in separation techniques and the core technologies of two-dimensional gel electrophoresis and MS. Both technologies are fueled by automation and high throughput computation for profiling of proteins from biological systems. As Patterson ever mentioned, 'data analysis is the Achilles heel of proteomics and our ability to generate data now outstrips our ability to analyze it'. The development of automatic and high throughput technologies for rapid identification of proteins is essential for large-scale proteome projects and automatic protein identification and characterization is essential for high throughput proteomics. This review provides a snap shot of the tools and applications that are available for mass spectrometric high throughput biocomputation. The review starts with a brief introduction of proteomics and MS. Computational tools that can be employed at various stages of analysis are presented, including that for data processing, identification, quantification, and the understanding of the biological functions of individual proteins and their dynamic interactions. The challenges of computation software development and its future trends in MS-based proteomics have also been speculated.
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Affiliation(s)
- Steve W Haga
- Department of Computer Science and Engineering, National Sun Yat Sen University, Kaohsiung, 804, Taiwan
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9
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Zahiri J, Bozorgmehr JH, Masoudi-Nejad A. Computational Prediction of Protein-Protein Interaction Networks: Algo-rithms and Resources. Curr Genomics 2014; 14:397-414. [PMID: 24396273 PMCID: PMC3861891 DOI: 10.2174/1389202911314060004] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Revised: 08/07/2013] [Accepted: 08/26/2013] [Indexed: 01/15/2023] Open
Abstract
Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.
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Affiliation(s)
- Javad Zahiri
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
| | - Joseph Hannon Bozorgmehr
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
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10
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Wobma HM, Blades ML, Grekova E, McGuire DL, Chen K, Chan WCW, Cramb DT. The development of direct multicolour fluorescence cross-correlation spectroscopy: towards a new tool for tracking complex biomolecular events in real-time. Phys Chem Chem Phys 2012; 14:3290-4. [PMID: 22249466 DOI: 10.1039/c2cp23278b] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Direct three-colour fluorescence cross-correlation spectroscopy can reveal interactions between three fluorescently labelled biomolecules, giving insight toward the complex events that constitute signal transduction pathways. Here we provide the optical and theoretical basis for this technology and demonstrate its ability to detect specific biological associations between nanoparticle-labelled DNA molecules.
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Affiliation(s)
- Holly M Wobma
- Department of Chemistry, University of Calgary, 2500 University Dr NW, Calgary, AB, Canada T2N 1N4
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11
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Ratsch E, Schultz J, Saric J, Lavin PC, Wittig U, Reyle U, Rojas I. Developing a protein-interactions ontology. Comp Funct Genomics 2010; 4:85-9. [PMID: 18629092 PMCID: PMC2447378 DOI: 10.1002/cfg.244] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2002] [Revised: 11/26/2002] [Indexed: 11/08/2022] Open
Affiliation(s)
- Esther Ratsch
- European Media Laboratory (EML), Schloss-Wolfsbrunnen-Weg 31c, Heidelberg 69118, Germany.
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12
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Pfeiffer T, Hoffmann R. Large-scale assessment of the effect of popularity on the reliability of research. PLoS One 2009; 4:e5996. [PMID: 19551148 PMCID: PMC2696594 DOI: 10.1371/journal.pone.0005996] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2009] [Accepted: 05/21/2009] [Indexed: 11/28/2022] Open
Abstract
Based on theoretical reasoning it has been suggested that the reliability of findings published in the scientific literature decreases with the popularity of a research field. Here we provide empirical support for this prediction. We evaluate published statements on protein interactions with data from high-throughput experiments. We find evidence for two distinctive effects. First, with increasing popularity of the interaction partners, individual statements in the literature become more erroneous. Second, the overall evidence on an interaction becomes increasingly distorted by multiple independent testing. We therefore argue that for increasing the reliability of research it is essential to assess the negative effects of popularity and develop approaches to diminish these effects.
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Affiliation(s)
- Thomas Pfeiffer
- Program for Evolutionary Dynamic, Harvard University, Cambridge, MA, USA.
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Zheng S, Sheng J, Wang C, Wang X, Yu Y, Li Y, Michie A, Dai J, Zhong Y, Hao P, Liu L, Li Y. MPSQ: a web tool for protein-state searching. Bioinformatics 2008; 24:2412-3. [PMID: 18697766 DOI: 10.1093/bioinformatics/btn427] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MPSQ (multi-protein-states query) is a web-based tool for the discovery of protein states (e.g. biological interactions, covalent modifications, cellular localizations). In particular, large sets of genes can be used to search for enriched state transition network maps (NMs) and features facilitating the interpretation of genomic-scale experiments such as microarrays. One NM collects all the catalogued states of a protein as well as the mutual transitions between the states. For the returned NM, graph visualization is provided for easy understanding and to guide further analysis.
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Affiliation(s)
- Siyuan Zheng
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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Abstract
After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be approximately 650,000. We find that the human interaction network is one order of magnitude bigger than the Drosophila melanogaster interactome and approximately 3 times bigger than in Caenorhabditis elegans.
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15
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Shen Y, Senzer NN, Nemunaitis JJ. Use of Proteomics Analysis for Molecular Precision Approaches in Cancer Therapy. Drug Target Insights 2008. [DOI: 10.4137/dti.s649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
| | - Neil N. Senzer
- LEAD Therapeutics, Inc., San Bruno, CA
- Mary Crowley Cancer Research Centers, Dallas, TX
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16
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Shoemaker BA, Panchenko AR. Deciphering protein-protein interactions. Part I. Experimental techniques and databases. PLoS Comput Biol 2007; 3:e42. [PMID: 17397251 PMCID: PMC1847991 DOI: 10.1371/journal.pcbi.0030042] [Citation(s) in RCA: 235] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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17
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Mathivanan S, Periaswamy B, Gandhi TKB, Kandasamy K, Suresh S, Mohmood R, Ramachandra YL, Pandey A. An evaluation of human protein-protein interaction data in the public domain. BMC Bioinformatics 2006; 7 Suppl 5:S19. [PMID: 17254303 PMCID: PMC1764475 DOI: 10.1186/1471-2105-7-s5-s19] [Citation(s) in RCA: 172] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Background Protein-protein interaction (PPI) databases have become a major resource for investigating biological networks and pathways in cells. A number of publicly available repositories for human PPIs are currently available. Each of these databases has their own unique features with a large variation in the type and depth of their annotations. Results We analyzed the major publicly available primary databases that contain literature curated PPI information for human proteins. This included BIND, DIP, HPRD, IntAct, MINT, MIPS, PDZBase and Reactome databases. The number of binary non-redundant human PPIs ranged from 101 in PDZBase and 346 in MIPS to 11,367 in MINT and 36,617 in HPRD. The number of genes annotated with at least one interactor was 9,427 in HPRD, 4,975 in MINT, 4,614 in IntAct, 3,887 in BIND and <1,000 in the remaining databases. The number of literature citations for the PPIs included in the databases was 43,634 in HPRD, 11,480 in MINT, 10,331 in IntAct, 8,020 in BIND and <2,100 in the remaining databases. Conclusion Given the importance of PPIs, we suggest that submission of PPIs to repositories be made mandatory by scientific journals at the time of manuscript submission as this will minimize annotation errors, promote standardization and help keep the information up to date. We hope that our analysis will help guide biomedical scientists in selecting the most appropriate database for their needs especially in light of the dramatic differences in their content.
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Affiliation(s)
- Suresh Mathivanan
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- McKusick-Nathans Institute of Genetic Medicine and the Departments of Biological Chemistry, Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21205, USA
- Department of Biotechnology, Kuvempu University, Shankaraghatta, Karnataka, India
| | - Balamurugan Periaswamy
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- McKusick-Nathans Institute of Genetic Medicine and the Departments of Biological Chemistry, Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21205, USA
- Department of Biotechnology, Kuvempu University, Shankaraghatta, Karnataka, India
| | - TKB Gandhi
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- McKusick-Nathans Institute of Genetic Medicine and the Departments of Biological Chemistry, Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Kumaran Kandasamy
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- Department of Biotechnology, Kuvempu University, Shankaraghatta, Karnataka, India
| | - Shubha Suresh
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- McKusick-Nathans Institute of Genetic Medicine and the Departments of Biological Chemistry, Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Riaz Mohmood
- Department of Biotechnology, Kuvempu University, Shankaraghatta, Karnataka, India
| | - YL Ramachandra
- Department of Biotechnology, Kuvempu University, Shankaraghatta, Karnataka, India
| | - Akhilesh Pandey
- McKusick-Nathans Institute of Genetic Medicine and the Departments of Biological Chemistry, Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21205, USA
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Chen Y, Xu D. Bioinformatics analysis for interactive proteomics. CURRENT PROTOCOLS IN PROTEIN SCIENCE 2005; Chapter 25:25.1.1-25.1.14. [PMID: 18429286 DOI: 10.1002/0471140864.ps2501s42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
High-throughput protein-protein interaction data are becoming a foundation for new biological discoveries. A major challenge is to manage, analyze, and model these data. In this unit several databases are described that are used to store, query, and visualize protein-protein interaction data. Comparison between experimental techniques reveals that each high-throughput technique has its limitations in detecting certain types of interactions; however, the techniques are generally complementary. In silico prediction methods for protein-protein interactions can expand the scope of experimental data and increase the confidence of certain interactions. Use of protein-protein interaction networks, preferably integrating them with other types of data, allows assignment of cellular functions to novel proteins and derivation of new biological pathways. As demonstrated in this unit, bioinformatics can be used to transform protein-protein interaction data from noisy information into knowledge of cellular mechanisms.
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Affiliation(s)
- Yu Chen
- Monsanto Company, St. Louis, Missouri
| | - Dong Xu
- University of Missouri-Columbia, Columbia, Missouri
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19
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Alfarano C, Andrade CE, Anthony K, Bahroos N, Bajec M, Bantoft K, Betel D, Bobechko B, Boutilier K, Burgess E, Buzadzija K, Cavero R, D'Abreo C, Donaldson I, Dorairajoo D, Dumontier MJ, Dumontier MR, Earles V, Farrall R, Feldman H, Garderman E, Gong Y, Gonzaga R, Grytsan V, Gryz E, Gu V, Haldorsen E, Halupa A, Haw R, Hrvojic A, Hurrell L, Isserlin R, Jack F, Juma F, Khan A, Kon T, Konopinsky S, Le V, Lee E, Ling S, Magidin M, Moniakis J, Montojo J, Moore S, Muskat B, Ng I, Paraiso JP, Parker B, Pintilie G, Pirone R, Salama JJ, Sgro S, Shan T, Shu Y, Siew J, Skinner D, Snyder K, Stasiuk R, Strumpf D, Tuekam B, Tao S, Wang Z, White M, Willis R, Wolting C, Wong S, Wrong A, Xin C, Yao R, Yates B, Zhang S, Zheng K, Pawson T, Ouellette BFF, Hogue CWV. The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res 2005; 33:D418-24. [PMID: 15608229 PMCID: PMC540005 DOI: 10.1093/nar/gki051] [Citation(s) in RCA: 447] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machine-readable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues.
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Affiliation(s)
- C Alfarano
- The Blueprint Initiative of Mount Sinai Hospital, 600 University Avenue, Toronto, ON, Canada M5G 1X5
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20
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Whittaker PA. The role of bioinformatics in target validation. DRUG DISCOVERY TODAY. TECHNOLOGIES 2004; 1:125-133. [PMID: 24981382 DOI: 10.1016/j.ddtec.2004.08.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Bioinformatics is being increasingly used to support target validation by providing functionally predictive information mined from databases and experimental datasets using a variety of computational tools. The predictive power of these complementary approaches is strongest when information from several techniques is combined, including experimental confirmation of predictions. The aim of this review is to highlight and discuss the key approaches available in this rapidly developing area to facilitate selection of the appropriate tools and databases.:
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Affiliation(s)
- Paul A Whittaker
- Novartis Institutes for Biomedical Research, Respiratory Disease Area, Horsham, West Sussex, UK RH125AB.
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21
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Abstract
To understand complex signaling pathways and networks, it is necessary to develop a formal and structured representation of the available information in a format suitable for analysis by software tools. Due to the complexity and incompleteness of the current biological knowledge about cell signaling, such a device must be able to represent cellular pathways at differing levels of details, one level of information abstract enough to convey an essential signaling flow while hiding its details and another level of information detailed enough to explain the underlying mechanisms that account for the signaling flow described at a more abstract level. We have defined a formal ontology for cell-signaling events that allows us to describe these cellular pathways at various levels of abstraction. Using this formal representation, ROSPath (reactive oxygen species-mediated signaling pathway) database system has been implemented and made available on the web (rospath.ewha.ac.kr). ROSPath is a database system for reactive oxygen species (ROS)-mediated cell signaling pathways and signaling processes in molecular detail, which facilitates a comprehensive understanding of the regulatory mechanisms in signaling pathways. ROSPath includes growth factor-, stress-, and cytokine-induced signaling pathways containing about 500 unique proteins (mostly mammalian) and their related protein states, protein complexes, protein complex states, signaling interactions, signaling steps, and pathways. It is a web-based structured repository of information on the signaling pathways of interest and provides a means for managing data produced by large-scale and high-throughput techniques such as proteomics. Also, software tools are provided for querying, displaying, and analyzing pathways, thus furnishing an integrated web environment for visualizing and manipulating ROS-mediated cell-signaling events.
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Affiliation(s)
- Eunok Paek
- Department of Mechanical and Information Engineering, University of Seoul, Seoul 130-743, Republic of Korea.
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22
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Fernández A, Scott R, Berry RS. The nonconserved wrapping of conserved protein folds reveals a trend toward increasing connectivity in proteomic networks. Proc Natl Acad Sci U S A 2004; 101:2823-7. [PMID: 14978274 PMCID: PMC365704 DOI: 10.1073/pnas.0308295100] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Although protein folding domains are generally conserved for function across distant homologous sequences, one crucial structural feature is not conserved: the wrapping of backbone hydrogen bonds, that is, the extent to which they are intramolecularly desolvated and thereby protected from water attack. Extensive data on protein complex interfaces led us to postulate that insufficiently wrapped backbone hydrogen bonds in monomeric domains must be adhesive, and therefore determinants of interactivity, a result that has been experimentally confirmed. Here, we show that the wrapping of certain conserved folds becomes progressively poorer as species diverge in some lineages. This trend is thus concurrent with a progressive enhancement of the interactivity of individual domains sharing the conserved fold. Such increase in interactivity is predicted to impose an "evolutionary brake" on the overall speed of sequence divergence. This phenomenon follows when more and more residues become engaged in protein associations and thus become functionally indispensable. For complete proteomes for which statistically significant structural data are available, scale-free network statistics based solely on the distribution of folding domains, catalogued by their number of wrapping defects, best describe the proteomic connectivity. Thus, the intermolecular connectivity may be effectively used as a measure of species complexity. Our results might contribute to explaining how interactome complexity may be achieved without a dramatic increase in genome size.
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Affiliation(s)
- Ariel Fernández
- Indiana University School of Informatics, Center for Computational Biology and Bioinformatics, and Department of Biochemistry and Molecular Biology, Indiana University Medical School, 714 North Senate Avenue, Indianapolis, IN 46202, USA.
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23
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Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res 2004; 32:D449-51. [PMID: 14681454 PMCID: PMC308820 DOI: 10.1093/nar/gkh086] [Citation(s) in RCA: 1388] [Impact Index Per Article: 69.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The Database of Interacting Proteins (http://dip.doe-mbi.ucla.edu) aims to integrate the diverse body of experimental evidence on protein-protein interactions into a single, easily accessible online database. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks.
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Affiliation(s)
- Lukasz Salwinski
- Howard Hughes Medical Institute, UCLA-DOE Institute for Genomics and Proteomics, 90095-1570, USA
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24
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Tien AC, Lin MH, Su LJ, Hong YR, Cheng TS, Lee YCG, Lin WJ, Still IH, Huang CYF. Identification of the Substrates and Interaction Proteins of Aurora Kinases from a Protein-Protein Interaction Model. Mol Cell Proteomics 2004; 3:93-104. [PMID: 14602875 DOI: 10.1074/mcp.m300072-mcp200] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. The major need now is to explore this disparate data to find biologically relevant interactions and pathways. Thus, in the post-genomic era, there is clearly a need for the development of algorithms that can accurately predict novel protein-protein interaction networks in silico. The evolutionarily conserved Aurora family kinases have been chosen as a model for the development of a method to identify novel biological networks by a comparison of human and various model organisms. Our search methodology was designed to predict and prioritize molecular targets for Aurora family kinases, so that only the most promising are subjected to empirical testing. Four potential Aurora substrates and/or interacting proteins, TACC3, survivin, Hec1, and hsNuf2, were identified and empirically validated. Together, these results justify the timely implementation of in silico biology in routine wet-lab studies and have also allowed the application of a new approach to the elucidation of protein function in the post-genomic era.
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Affiliation(s)
- An-Chi Tien
- Division of Molecular and Genomic Medicine, National Health Research Institutes, Taipei 115, Taiwan, Republic of China
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25
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Lundgren DH, Eng J, Wright ME, Han DK. PROTEOME-3D: an interactive bioinformatics tool for large-scale data exploration and knowledge discovery. Mol Cell Proteomics 2003; 2:1164-76. [PMID: 12960178 PMCID: PMC1487189 DOI: 10.1074/mcp.m300059-mcp200] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Comprehensive understanding of biological systems requires efficient and systematic assimilation of high-throughput datasets in the context of the existing knowledge base. A major limitation in the field of proteomics is the lack of an appropriate software platform that can synthesize a large number of experimental datasets in the context of the existing knowledge base. Here, we describe a software platform, termed PROTEOME-3D, that utilizes three essential features for systematic analysis of proteomics data: creation of a scalable, queryable, customized database for identified proteins from published literature; graphical tools for displaying proteome landscapes and trends from multiple large-scale experiments; and interactive data analysis that facilitates identification of crucial networks and pathways. Thus, PROTEOME-3D offers a standardized platform to analyze high-throughput experimental datasets for the identification of crucial players in co-regulated pathways and cellular processes.
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Affiliation(s)
- Deborah H. Lundgren
- From the Center for Vascular Biology, Department of Physiology, University of Connecticut School of Medicine, Farmington, CT 06030, and
| | - Jimmy Eng
- Institute for Systems Biology, Seattle, WA 98103
| | | | - David K. Han
- From the Center for Vascular Biology, Department of Physiology, University of Connecticut School of Medicine, Farmington, CT 06030, and
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26
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Abstract
Computational methods play an important role at all stages of the process of determining protein-protein interactions. They are used to predict potential interactions, to validate the results of high-throughput interaction screens and to analyze the protein networks inferred from interaction databases.
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Affiliation(s)
- Lukasz Salwinski
- Howard Hughes Medical Institute, UCLA-DOE Institute for Genomics and Proteomics, Departments of Chemistry & Biochemistry and Biological Chemistry, Molecular Biology Institute, Box 951570, UCLA, Los Angeles, CA 90095-1570, USA.
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27
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28
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Neet KE, Lee JC. Biophysical characterization of proteins in the post-genomic era of proteomics. Mol Cell Proteomics 2002; 1:415-20. [PMID: 12169682 DOI: 10.1074/mcp.r200003-mcp200] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Proteomics focuses on the high throughput study of the expression, structure, interactions, and, to some extent, function of large numbers of proteins. A true understanding of the functioning of a living cell also requires a quantitative description of the stoichiometry, kinetics, and energetics of each protein complex in a cellular pathway. Classical molecular biophysical studies contribute to understanding of these detailed properties of proteins on a smaller scale than does proteomics in that individual proteins are usually studied. This perspective article deals with the role of biophysical methods in the study of proteins in the proteomic era. Several important physical biochemical methods are discussed briefly and critiqued from the standpoint of information content and data acquisition. The focus is on conformational changes and macromolecular assembly, the utility of dynamic and static structural data, and the necessity to combine experimental approaches to obtain a full functional description. The conclusions are that biophysical information on proteins is a useful adjunct to "standard" proteomic methods, that data can be obtained by high throughput technology in some instances, but that hypothesis-driven experimentation may frequently be required.
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Affiliation(s)
- Kenneth E Neet
- Department of Biochemistry and Molecular Biology, Finch University of Health Sciences/Chicago Medical School, North Chicago, Illinois 60064, USA.
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