Protein-Protein Interaction Detection: Methods and Analysis
Open accessAbstract
Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus,in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion,in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.
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Networks can also be formed based on genomic interactions captured by HiC data, and physical interactions can be measured across different layers, such as in ChIP-Seq.
“Networks can also be formed based on genomic interactions captured by HiC data [70, 71], and physical interactions can also be measured across different layers, such as in ChIP-Seq, which quantifies DNA binding by specific proteins.”
From Multi-omics approaches to disease (2017) · cites this paper as [70]
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- Authors
- Rao, V. S., Srinivas, K., Sujini, G. N., & Kumar, G. N. S.
- Affiliations
- Siddhartha Academy of Higher Education (IN)
- Mahatma Gandhi Institute of Technology (IN)
- Corresponding author
- Rao, V. Srinivasa
- Venue
- International Journal of Proteomics
- Publisher
- Wiley
- Published
- 2014-02-17
- Volume
- 2014
- Pages
- 1-12
- ISSN
- 2090-2166 (ISSN-L)
- 2090-2174
- PMC
- PMC3947875
- OpenAlex
- W1990315216
- Cited by
- 811
- References
- 93
- Citations by year
- 2026: 28 · 2025: 103 · 2024: 119 · 2023: 108 · 2022: 93 · 2021: 88 · 2020: 60 · 2019: 49 · 2018: 48 · 2017: 36 · 2016: 41 · 2015: 29 · 2014: 8
- FWCI
- 3.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International Journal of Proteomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- University Grants Commission
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein-protein interaction, yeast two-hybrid, tandem affinity purification, phylogenetic profiles, gene fusion, protein-protein interaction network, signaling pathways, protein complex identification, drug target prediction, protein function prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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