The DOI matches its Crossref record; 210 of the 211 references it cites were found in a registry or an index.
Predicting Protein–Protein Interactions from the Molecular to the Proteome Level
Open accessAbstract
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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“suggesting that they may be functionally related69–71 or operate together72,73 to mediate phenotypic features associated with UDN-P9’s disease and hypomyelinating leukodystropy-20”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [70]
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Metadata
- 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
- 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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