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Predicting Protein–Protein Interactions from the Molecular to the Proteome Level

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Keskin, Ozlem, Tuncbag, Nurcan, Gursoy, Attila·2016·Chemical Reviews

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Abstract

Identification of protein-protein interactions (PPIs) is at the center of molecular biology considering the unquestionable role of proteins in cells. Combinatorial interactions result in a repertoire of multiple functions; hence, knowledge of PPI and binding regions naturally serve to functional proteomics and drug discovery. Given experimental limitations to find all interactions in a proteome, computational prediction/modeling of protein interactions is a prerequisite to proceed on the way to complete interactions at the proteome level. This review aims to provide a background on PPIs and their types. Computational methods for PPI predictions can use a variety of biological data including sequence-, evolution-, expression-, and structure-based data. Physical and statistical modeling are commonly used to integrate these data and infer PPI predictions. We review and list the state-of-the-art methods, servers, databases, and tools for protein-protein interaction prediction.

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Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
The Protein Data Bank
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436
Inference of Macromolecular Assemblies from Crystalline State
Krissinel, E., & Henrick, K. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.05.022 · Cited by 10,007
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae
Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature · doi:10.1038/35001009 · Cited by 4,706
STRING v9.1: protein-protein interaction networks, with increased coverage and integration
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1094 · Cited by 4,398
A comprehensive two-hybrid analysis to explore the yeast protein interactome
Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., & Sakaki, Y. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.061034498 · Cited by 3,566
Human Protein Reference Database--2009 update
Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, R. B., Venugopal, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,252

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Bishop C. M., 2007, Pattern Recognition and Machine Learning
10.1016/j.jmb.2007.05.022 · DOI

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Items 301–369 of 369

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Big data analytics in bioinformatics
Bjørnholt), G. A. (. (2020) · Research Publications (Maastricht University) · doi:10.26481/dis.20200828ag · Cited by 1
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Pérez-Regidor, L., Guzman‐Caldentey, J., Rodríguez, C. F., Billod, J., Nogales, J., & Martín‐Santamaría, S. (2017) · Royal Society of Chemistry eBooks · doi:10.1039/9781788010139-00221
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Taye, B. (2017) · doi:10.32657/10356/72814
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Koon, Y. L. (2018) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/73360
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Hayashi, T. (2018) · Springer eBooks · doi:10.1007/978-981-10-8459-1_13
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Aker, M., Ohanona, S., Fisher, S., Katsman, E., Dvorkin, S., Kopelowitz, E., Goldstein, M., Barnett‐Itzhaki, Z., & Amitay, M. (2018) · Protein Engineering Design and Selection · doi:10.1093/protein/gzy030
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Lin, X., & Zhou, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26969-2_31
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[Overview of urethral strictures: etiology, epidemiology, physiopathology and classification. General principles of urethral repair].
Abarca, C. L., Hernández, V., & Goizueta, F. J. D. (2014) · PubMed
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Borysik, A. J. (2017) · Angewandte Chemie · doi:10.1002/ange.201704604
References
Srihari, S., Yong, C. H., & Wong, L. (2017) · doi:10.1145/3064650.3064661
Preface
Srihari, S., Yong, C. H., & Wong, L. (2017) · doi:10.1145/3064650.3064651
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Authors: Keskin, O., Tuncbag, N., & Gursoy, A.
Affiliations: Graduate School of Informatics, Department of Health Informatics, Middle East Technical University, 06800 Ankara, Turkey
Venue: Chemical Reviews
Publisher: American Chemical Society (ACS)
Published: 2016-04-13
Volume: 116
Issue: 8
Pages: 4884-4909
ISSN: 0009-2665 (ISSN-L)
1520-6890
DOI: 10.1021/acs.chemrev.5b00683
OpenAlex: W2341018731
Cited by: 369
References: 211
Citations by year: 2026: 20 · 2025: 32 · 2024: 30 · 2023: 42 · 2022: 39 · 2021: 49 · 2020: 32 · 2019: 37 · 2018: 41 · 2017: 35 · 2016: 10
FWCI: 3.71
Type: journal article
Language: en
Open access: green
hdl.handle.net
Licence: cc-by-nc-nd
Locations: Chemical Reviews (publishedVersion)
PubMed (publishedVersion)
OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
Funders: Directorate-General for Research and Innovation (114C026)
Türkiye Bilimsel ve Teknolojik Arastirma Kurumu (113E164, 114C026, 114M196)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords: protein-protein interaction, sequence-based prediction, structure-based prediction, statistical modeling, interaction databases
MeSH terms: Amino Acid Sequence, Binding Sites, Humans, Protein Binding, Software, Gene Expression, Sequence Homology, Amino Acid, Proteome, Protein Interaction Mapping, Databases, Protein, Proteomics, Protein Interaction Domains and Motifs, Molecular Dynamics Simulation, Data Mining, Molecular Docking Simulation

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Predicting Protein–Protein Interactions from the Molecular to the Proteome Level

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