Predicting the protein interaction landscape of a free-living bacterium with pooled-AlphaFold3

Read the full article See related articles

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Accurate prediction of protein complex structures by AlphaFold3 and similar programs has been successfully used to predict the presence of protein-protein interactions (PPIs), but this technique has never been applied to an entire genome due to onerous computational requirements. Here we present pooled-PPI prediction, a technique that reduces the inference time of genome-scale screens ∼2-fold and the number of jobs ∼100-fold, and use it to predict the structure of all 113,050 pairwise PPIs in Mycoplasma genitalium using only 2,027 AlphaFold3 jobs. This unbiased dataset revealed a previously unappreciated but widespread size bias in AlphaFold interface scores, was highly predictive of known interactions, correctly identified protein-protein interfaces in macromolecular complexes, and uncovered new biology in M. genitalium. This work establishes pooled-PPI prediction as a highly scalable method for uncovering protein-protein interactions and a powerful addition to the functional genomics toolkit.

Article activity feed