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Updated: Dec 29, 2025

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
Published on: March 3, 2015
Systematic comparison of the protein-protein interaction databases from a user's perspective
Akhilesh Kumar Bajpai1, Sravanthi Davuluri1, Kriti Tiwary2
1Structural Biology Lab, Centre for Biomedical Research, School of Bio Sciences & Technology (SBST), Vellore Institute of Technology (VIT) University, Vellore 632014, Tamil Nadu, India; Shodhaka Life Sciences Pvt. Ltd., Electronic City, Phase I, Bengaluru (Bangalore) 560100, Karnataka, India.
Biologists often choose protein-protein interaction (PPI) databases subjectively. This study compares 16 human PPI databases, revealing STRING and UniHI cover 84% of verified interactions, while hPRINT, STRING, and IID capture 94% of total interactions.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Biologists and bioinformaticians face challenges selecting protein-protein interaction (PPI) databases due to a lack of systematic comparisons.
- This can lead to subjective choices among numerous available PPI resources.
Purpose of the Study:
- To conduct a comprehensive comparison of human protein-protein interaction (PPI) databases and tools.
- To quantitatively assess the coverage and features of selected PPI databases.
Main Methods:
- Compiled 375 PPI resources, short-listed 125, and compared 16 key human PPI databases.
- Evaluated coverage of experimentally verified and predicted PPIs using gene queries and downloaded back-end data.
- Assessed high-quality interactions using a curated gold-standard PPI set.
Main Results:
- Combined STRING and UniHI covered 84% of experimentally verified PPIs.
- hPRINT, STRING, and IID together retrieved 94% of total PPIs.
- STRING uniquely contributed 71% of experimentally verified PPIs; GPS-Prot, STRING, APID, and HIPPIE each covered ~70% of gold-standard interactions.
Conclusions:
- No single PPI database offers complete coverage; combined usage is recommended.
- Database utility does not always correlate with usage frequency, highlighting the need for ongoing comparative studies.
- This research provides valuable insights for selecting appropriate PPI resources.
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