1
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Shu J, Li J, Wang S, Lin J, Wen L, Ye H, Zhou P. Systematic analysis and comparison of peptide specificity and selectivity between their cognate receptors and noncognate decoys. J Mol Recognit 2023; 36:e3006. [PMID: 36579779 DOI: 10.1002/jmr.3006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/07/2022] [Accepted: 12/27/2022] [Indexed: 12/30/2022]
Abstract
Protein-peptide interactions (PpIs) play an important role in cell signaling networks and have been exploited as new and attractive therapeutic targets. The affinity and specificity are two unity-of-opposite aspects of PpIs (and other biomolecular interactions); the former indicates the absolute binding strength between the peptide ligand and its cognate protein receptor in a PpI, while the latter represents the relative recognition selectivity of the peptide ligand for its cognate protein receptor in a PpI over those noncognate decoys that could be potentially encountered by the peptide in cell. Although the PpI binding affinity has been widely investigated over the past decades, the peptide recognition specificity (and selectivity) still remains largely unexplored to date. In this study, we classified PpI specificity into three types: (i) class-I specificity: peptide selectivity for its cognate wild-type protein receptor over the noncognate mutant decoys of this receptor, (ii) class-II specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are homologous with this receptor, and (iii) class-III specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are the cognate receptors of other peptides. We performed affinity and selectivity analysis for the three types of PpI specificity and revealed that the PpIs generally exhibit a moderate or modest specificity; peptide selectivity increases in the order: class-I < class-II < class-III. All the three types of PpI specificity were observed to have no statistically significant correlation with peptide length and hydrophobicity, but the class-I and class-II specificities can be influenced considerably by peptide secondary structures; the high specificity is preferentially associated with ordered structure types as compared to undefined structure types. In addition, the mutation distribution (for class-I specificity), sequence conservation (for class-II specificity), and structural similarity (for class-III specificity) seem also to address effects on peptide selectivity.
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Affiliation(s)
- Jianping Shu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Juelin Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Shaozhou Wang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Jing Lin
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Li Wen
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Haiyang Ye
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Peng Zhou
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
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2
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Lin J, Wang S, Wen L, Ye H, Shang S, Li J, Shu J, Zhou P. Targeting peptide-mediated interactions in omics. Proteomics 2023; 23:e2200175. [PMID: 36461811 DOI: 10.1002/pmic.202200175] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/28/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022]
Abstract
Peptide-mediated interactions (PMIs) play a crucial role in cell signaling network, which are responsible for about half of cellular protein-protein associations in the human interactome and have recently been recognized as a new kind of promising druggable target for drug development and disease therapy. In this article, we give a systematic review regarding the proteome-wide discovery of PMIs and targeting druggable PMIs (dPMIs) with chemical drugs, self-inhibitory peptides (SIPs) and protein agents, particularly focusing on their implications and applications for therapeutic purpose in omics. We also introduce computational peptidology strategies used to model, analyze, and design PMI-targeted molecular entities and further extend the concepts of protein context, direct/indirect readout, and enthalpy/entropy effect involved in PMIs. Current issues and future perspective on this topic are discussed. There is still a long way to go before establishment of efficient therapeutic strategies to target PMIs on the omics scale.
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Affiliation(s)
- Jing Lin
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Shaozhou Wang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Li Wen
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Haiyang Ye
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Shuyong Shang
- Institute of Ecological Environment Protection, Chengdu Normal University, Chengdu, China
| | - Juelin Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Jianping Shu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Peng Zhou
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China
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3
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Johnson KL, Qi Z, Yan Z, Wen X, Nguyen TC, Zaleta-Rivera K, Chen CJ, Fan X, Sriram K, Wan X, Chen ZB, Zhong S. Revealing protein-protein interactions at the transcriptome scale by sequencing. Mol Cell 2021; 81:4091-4103.e9. [PMID: 34348091 PMCID: PMC8500946 DOI: 10.1016/j.molcel.2021.07.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/12/2021] [Accepted: 07/08/2021] [Indexed: 02/07/2023]
Abstract
We describe PROPER-seq (protein-protein interaction sequencing) to map protein-protein interactions (PPIs) en masse. PROPER-seq first converts transcriptomes of input cells into RNA-barcoded protein libraries, in which all interacting protein pairs are captured through nucleotide barcode ligation, recorded as chimeric DNA sequences, and decoded at once by sequencing and mapping. We applied PROPER-seq to human embryonic kidney cells, T lymphocytes, and endothelial cells and identified 210,518 human PPIs (collected in the PROPER v.1.0 database). Among these, 1,365 and 2,480 PPIs are supported by published co-immunoprecipitation (coIP) and affinity purification-mass spectrometry (AP-MS) data, 17,638 PPIs are predicted by the prePPI algorithm without previous experimental validation, and 100 PPIs overlap human synthetic lethal gene pairs. In addition, four previously uncharacterized interaction partners with poly(ADP-ribose) polymerase 1 (PARP1) (a critical protein in DNA repair) known as XPO1, MATR3, IPO5, and LEO1 are validated in vivo. PROPER-seq presents a time-effective technology to map PPIs at the transcriptome scale, and PROPER v.1.0 provides a rich resource for studying PPIs.
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Affiliation(s)
- Kara L Johnson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhijie Qi
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhangming Yan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xingzhao Wen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tri C Nguyen
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kathia Zaleta-Rivera
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Chien-Ju Chen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xiaochen Fan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kiran Sriram
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Xueyi Wan
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhen Bouman Chen
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Sheng Zhong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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4
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Hollander M, Do T, Will T, Helms V. Detecting Rewiring Events in Protein-Protein Interaction Networks Based on Transcriptomic Data. FRONTIERS IN BIOINFORMATICS 2021; 1:724297. [PMID: 36303788 PMCID: PMC9581068 DOI: 10.3389/fbinf.2021.724297] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 08/23/2021] [Indexed: 12/25/2022] Open
Abstract
Proteins rarely carry out their cellular functions in isolation. Instead, eukaryotic proteins engage in about six interactions with other proteins on average. The aggregated protein interactome of an organism forms a “hairy ball”-type protein-protein interaction (PPI) network. Yet, in a typical human cell, only about half of all proteins are expressed at a particular time. Hence, it has become common practice to prune the full PPI network to the subset of expressed proteins. If RNAseq data is available, one can further resolve the specific protein isoforms present in a cell or tissue. Here, we review various approaches, software tools and webservices that enable users to construct context-specific or tissue-specific PPI networks and how these are rewired between two cellular conditions. We illustrate their different functionalities on the example of the interactions involving the human TNR6 protein. In an outlook, we describe how PPI networks may be integrated with epigenetic data or with data on the activity of splicing factors.
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5
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Lasso G, Mayer SV, Winkelmann ER, Chu T, Elliot O, Patino-Galindo JA, Park K, Rabadan R, Honig B, Shapira SD. A Structure-Informed Atlas of Human-Virus Interactions. Cell 2019; 178:1526-1541.e16. [PMID: 31474372 PMCID: PMC6736651 DOI: 10.1016/j.cell.2019.08.005] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 05/17/2019] [Accepted: 08/02/2019] [Indexed: 12/19/2022]
Abstract
While knowledge of protein-protein interactions (PPIs) is critical for understanding virus-host relationships, limitations on the scalability of high-throughput methods have hampered their identification beyond a number of well-studied viruses. Here, we implement an in silico computational framework (pathogen host interactome prediction using structure similarity [P-HIPSTer]) that employs structural information to predict ∼282,000 pan viral-human PPIs with an experimental validation rate of ∼76%. In addition to rediscovering known biology, P-HIPSTer has yielded a series of new findings: the discovery of shared and unique machinery employed across human-infecting viruses, a likely role for ZIKV-ESR1 interactions in modulating viral replication, the identification of PPIs that discriminate between human papilloma viruses (HPVs) with high and low oncogenic potential, and a structure-enabled history of evolutionary selective pressure imposed on the human proteome. Further, P-HIPSTer enables discovery of previously unappreciated cellular circuits that act on human-infecting viruses and provides insight into experimentally intractable viruses.
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Affiliation(s)
- Gorka Lasso
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA
| | - Sandra V Mayer
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA
| | - Evandro R Winkelmann
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA
| | - Tim Chu
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA
| | - Oliver Elliot
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA
| | | | - Kernyu Park
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
| | - Raul Rabadan
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University Medical Center, New York, NY, USA; Howard Hughes Medical Institute, Columbia University Medical Center, New York, NY, USA.
| | - Sagi D Shapira
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA.
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6
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Li Z, Miao Q, Yan F, Meng Y, Zhou P. Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design. Curr Drug Metab 2019; 20:170-176. [DOI: 10.2174/1389200219666181012151944] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 11/07/2017] [Accepted: 08/20/2018] [Indexed: 01/03/2023]
Abstract
Background:Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.Methods:We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.Results:Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.Conclusion:There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.
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Affiliation(s)
- Zhongyan Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Qingqing Miao
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Fugang Yan
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Yang Meng
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Peng Zhou
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
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7
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Zhou P, Miao Q, Yan F, Li Z, Jiang Q, Wen L, Meng Y. Is protein context responsible for peptide-mediated interactions? Mol Omics 2019; 15:280-295. [DOI: 10.1039/c9mo00041k] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Many cell signaling pathways are orchestrated by the weak, transient, and reversible peptide-mediated interactions (PMIs). Here, the role of protein context in contributing to the stability and specificity of PMIs is investigated systematically.
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Affiliation(s)
- Peng Zhou
- Center for Informational Biology
- University of Electronic Science and Technology of China (UESTC)
- Chengdu 611731
- China
- School of Life Science and Technology
| | - Qingqing Miao
- Center for Informational Biology
- University of Electronic Science and Technology of China (UESTC)
- Chengdu 611731
- China
- School of Life Science and Technology
| | - Fugang Yan
- Center for Informational Biology
- University of Electronic Science and Technology of China (UESTC)
- Chengdu 611731
- China
- School of Life Science and Technology
| | - Zhongyan Li
- Center for Informational Biology
- University of Electronic Science and Technology of China (UESTC)
- Chengdu 611731
- China
- School of Life Science and Technology
| | - Qianhu Jiang
- School of Life Science and Technology
- University of Electronic Science and Technology of China (UESTC)
- Chengdu 610054
- China
| | - Li Wen
- School of Life Science and Technology
- University of Electronic Science and Technology of China (UESTC)
- Chengdu 610054
- China
| | - Yang Meng
- School of Life Science and Technology
- University of Electronic Science and Technology of China (UESTC)
- Chengdu 610054
- China
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8
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The present and the future of motif-mediated protein-protein interactions. Curr Opin Struct Biol 2018; 50:162-170. [PMID: 29730529 DOI: 10.1016/j.sbi.2018.04.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 02/07/2018] [Accepted: 04/11/2018] [Indexed: 01/14/2023]
Abstract
Protein-protein interactions (PPIs) are essential to governing virtually all cellular processes. Of particular importance are the versatile motif-mediated interactions (MMIs), which are thus far underrepresented in available interaction data. This is largely due to technical difficulties inherent in the properties of MMIs, but due to the increasing recognition of the vital roles of MMIs in biology, several systematic approaches have recently been developed to detect novel MMIs. Consequently, rapidly growing numbers of motifs are being identified and pursued further for therapeutic applications. In this review, we discuss the current understanding on the diverse functions and disease-relevance of MMIs, the key methodologies for detection of MMIs, and the potential of MMIs for drug development.
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9
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Iqbal S, Hoque MT. PBRpredict-Suite: a suite of models to predict peptide-recognition domain residues from protein sequence. Bioinformatics 2018; 34:3289-3299. [DOI: 10.1093/bioinformatics/bty352] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 04/29/2018] [Indexed: 01/10/2023] Open
Affiliation(s)
- Sumaiya Iqbal
- Computer Science, University of New Orleans, New Orleans, LA, USA
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10
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Yu J, Wang S, Yu J, Liu C, Xu F, Wang S, Yi Y, Yin Y. Structure-based rational design of self-inhibitory peptides to disrupt the intermolecular interaction between the troponin subunits C and I in neuropathic pain. Bioorg Chem 2017; 73:10-15. [PMID: 28525735 DOI: 10.1016/j.bioorg.2017.05.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Revised: 04/08/2017] [Accepted: 05/01/2017] [Indexed: 11/26/2022]
Abstract
The troponin (Tn) is a ternary complex consisting of three subunits TnC, TnI and TnT; molecular disruption of the Tn complex has been recognized as an attractive strategy against neuropathic pain. Here, a self-inhibitory peptide is stripped from the switch region of TnI interaction interface with TnC, which is considered as a lead molecular entity and then used to generate potential peptide disruptors of TnC-TnI interaction based on a rational molecular design protocol. The region is a helical peptide segment capped by N- and C-terminal disorders. Molecular dynamics simulation and binding free energy analysis suggests that the switch peptide can interact with TnC in a structurally and energetically independent manner. Terminal truncation of the peptide results in a number of potent TnC binders with considerably simplified structure and moderately decreased activity relative to the native switch. We also employ fluorescence polarization assays to substantiate the computational findings; it is found that the rationally designed peptides exhibit moderate or high affinity to TnC with dissociation constants KD at micromolar level.
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Affiliation(s)
- Junmin Yu
- Department of Pain Management, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Shilei Wang
- Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Junjie Yu
- Chinese Traditional Patent Medicine Dispensary, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Chuansheng Liu
- Department of Pain Management, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Fenghe Xu
- Department of Pain Management, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Shijie Wang
- Department of Pain Management, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Yusheng Yi
- Department of Pain Management, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - Yanwei Yin
- Department of Pain Management, The Affiliated Hospital of Qingdao University, Qingdao 266003, China.
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11
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Garzón JI, Deng L, Murray D, Shapira S, Petrey D, Honig B. A computational interactome and functional annotation for the human proteome. eLife 2016; 5. [PMID: 27770567 PMCID: PMC5115866 DOI: 10.7554/elife.18715] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Accepted: 10/19/2016] [Indexed: 12/14/2022] Open
Abstract
We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein's function. We provide annotations for most human proteins, including many annotated as having unknown function.
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Affiliation(s)
- José Ignacio Garzón
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States
| | - Lei Deng
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States.,School of Software, Central South University, Changsha, China
| | - Diana Murray
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States
| | - Sagi Shapira
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States.,Department of Microbiology and Immunology, Columbia University, New York, United States
| | - Donald Petrey
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States.,Howard Hughes Medical Institute, Columbia University, New York, United States
| | - Barry Honig
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States.,Howard Hughes Medical Institute, Columbia University, New York, United States.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States.,Department of Medicine, Columbia University, New York, United States.,Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States
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12
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Jain S, Bader GD. Predicting physiologically relevant SH3 domain mediated protein-protein interactions in yeast. Bioinformatics 2016; 32:1865-72. [PMID: 26861823 PMCID: PMC4908317 DOI: 10.1093/bioinformatics/btw045] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 12/05/2015] [Accepted: 01/20/2016] [Indexed: 12/02/2022] Open
Abstract
MOTIVATION Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein-protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins. RESULTS A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein-protein interactions in S. cerevisae using phage display data is presented. Like some previous similar methods, this method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. The novelty of this approach is the large number of evidence sources used and the method of combination of sequence based and protein pair based evidence sources. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy of 0.97. AVAILABILITY AND IMPLEMENTATION Domain-Motif Mediated Interaction Prediction (DoMo-Pred) command line tool and all relevant datasets are available under GNU LGPL license for download from http://www.baderlab.org/Software/DoMo-Pred The DoMo-Pred command line tool is implemented using Python 2.7 and C ++. CONTACT gary.bader@utoronto.ca SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shobhit Jain
- Department of Computer Science and The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Gary D Bader
- Department of Computer Science and The Donnelly Centre, University of Toronto, Toronto, ON, Canada
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13
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Dinkel H, Van Roey K, Michael S, Kumar M, Uyar B, Altenberg B, Milchevskaya V, Schneider M, Kühn H, Behrendt A, Dahl SL, Damerell V, Diebel S, Kalman S, Klein S, Knudsen AC, Mäder C, Merrill S, Staudt A, Thiel V, Welti L, Davey NE, Diella F, Gibson TJ. ELM 2016--data update and new functionality of the eukaryotic linear motif resource. Nucleic Acids Res 2016; 44:D294-300. [PMID: 26615199 PMCID: PMC4702912 DOI: 10.1093/nar/gkv1291] [Citation(s) in RCA: 224] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2015] [Revised: 11/04/2015] [Accepted: 11/05/2015] [Indexed: 01/18/2023] Open
Abstract
The Eukaryotic Linear Motif (ELM) resource (http://elm.eu.org) is a manually curated database of short linear motifs (SLiMs). In this update, we present the latest additions to this resource, along with more improvements to the web interface. ELM 2016 contains more than 240 different motif classes with over 2700 experimentally validated instances, manually curated from more than 2400 scientific publications. In addition, more data have been made available as individually searchable pages and are downloadable in various formats.
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Affiliation(s)
- Holger Dinkel
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Kim Van Roey
- Health Services Research Unit, Operational Direction Public Health and Surveillance, Scientific Institute of Public Health (WIV-ISP), 1050 Brussels, Belgium
| | - Sushama Michael
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Manjeet Kumar
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Bora Uyar
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Brigitte Altenberg
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Vladislava Milchevskaya
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | | | - Helen Kühn
- Ruprecht-Karls-Universität, Heidelberg, Germany
| | | | | | | | | | - Sara Kalman
- Ruprecht-Karls-Universität, Heidelberg, Germany
| | | | | | | | | | | | - Vera Thiel
- Ruprecht-Karls-Universität, Heidelberg, Germany
| | - Lukas Welti
- Ruprecht-Karls-Universität, Heidelberg, Germany
| | - Norman E Davey
- Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin 4, Ireland
| | - Francesca Diella
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Toby J Gibson
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
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Blikstad C, Ivarsson Y. High-throughput methods for identification of protein-protein interactions involving short linear motifs. Cell Commun Signal 2015; 13:38. [PMID: 26297553 PMCID: PMC4546347 DOI: 10.1186/s12964-015-0116-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 08/11/2015] [Indexed: 02/07/2023] Open
Abstract
Interactions between modular domains and short linear motifs (3–10 amino acids peptide stretches) are crucial for cell signaling. The motifs typically reside in the disordered regions of the proteome and the interactions are often transient, allowing for rapid changes in response to changing stimuli. The properties that make domain-motif interactions suitable for cell signaling also make them difficult to capture experimentally and they are therefore largely underrepresented in the known protein-protein interaction networks. Most of the knowledge on domain-motif interactions is derived from low-throughput studies, although there exist dedicated high-throughput methods for the identification of domain-motif interactions. The methods include arrays of peptides or proteins, display of peptides on phage or yeast, and yeast-two-hybrid experiments. We here provide a survey of scalable methods for domain-motif interaction profiling. These methods have frequently been applied to a limited number of ubiquitous domain families. It is now time to apply them to a broader set of peptide binding proteins, to provide a comprehensive picture of the linear motifs in the human proteome and to link them to their potential binding partners. Despite the plethora of methods, it is still a challenge for most approaches to identify interactions that rely on post-translational modification or context dependent or conditional interactions, suggesting directions for further method development.
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Affiliation(s)
- Cecilia Blikstad
- Department of Chemistry - BMC, Husargatan 3, 751 23, Uppsala, Sweden
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Husargatan 3, 751 23, Uppsala, Sweden.
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