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MPI-LIT: a literature-curated dataset of microbial binary protein--protein interactions

Seesandra V Rajagopala1, Johannes Goll, N D Deve Gowda

  • 1J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org

Bioinformatics (Oxford, England)
|September 13, 2008
PubMed
Abstract

Insights

A new dataset, MPI-LIT, offers a gold standard for microbial protein-protein interactions, including crucial experimental evidence. This resource significantly expands the available data on prokaryotic interactions, aiding future research.

Area of Science:

  • Microbiology
  • Biochemistry
  • Bioinformatics

Background:

  • Prokaryotic protein-protein interactions are essential biological processes.
  • Existing databases lack comprehensive data on microbial interactions and their supporting evidence.
  • Manual curation is needed to build high-quality interaction datasets.

Purpose of the Study:

  • To create a high-quality, manually curated dataset of microbial binary protein-protein interactions.
  • To provide a 'gold standard' resource (MPI-LIT) with detailed experimental evidence.
  • To assess the coverage and quality of existing microbial interaction databases.

Main Methods:

  • Manual curation of 813 selected abstracts and full texts from over 36,000 initial abstracts.
  • Compilation of 1237 experimental descriptions for a non-redundant set of 746 interactions.
  • Comparison of the MPI-LIT dataset with existing databases (IntAct, DIP, BIND, MINT) for sensitivity and coverage.

Main Results:

  • The MPI-LIT dataset contains 746 non-redundant interactions, with 88% not found in public databases.
  • Achieved up to 66% sensitivity for interactions and 75% for experimental methods compared to other datasets.
  • MPI-LIT exhibits the lowest fraction of interaction experiments per abstract and highest coverage of strains and articles.

Conclusions:

  • MPI-LIT serves as a valuable resource for studying prokaryotic protein-protein interactions.
  • The dataset demonstrates the underrepresentation of microbial interactions in current public databases.
  • Methods for evaluating functional interactions, like those in STRING, effectively discriminate between curated and high-throughput data.

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