PrePPI: A Structure Informed Proteome-wide Database of Protein–Protein Interactions

Donald Petrey, Haiqing Zhao, Stephen J. Trudeau, Diana Murray, Barry Honig

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein–protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.

Original languageEnglish (US)
Article number168052
JournalJournal of Molecular Biology
Volume435
Issue number14
DOIs
StatePublished - Jul 15 2023
Externally publishedYes

Keywords

  • alphafold models
  • database
  • non-structural evidence
  • protein-protein interactions
  • structural modeling

ASJC Scopus subject areas

  • Biophysics
  • Structural Biology
  • Molecular Biology

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