This research article from David Baker team details a novel approach for systematic and accurate prediction of protein-protein interactions (PPIs) in the human proteome. The core innovation lies in the development of omicMSAs, which are deeply enriched multiple sequence alignments derived from petabytes of untapped eukaryotic genomic data to enhance coevolutionary signals, and RoseTTAFold2-PPI (RF2-PPI), a rapid deep learning network. This new network was trained on an extensive dataset, including novel domain-domain interactions (DDIs) distilled from over 200 million predicted protein structures, making it much more efficient and accurate for high-throughput screening than previous methods like AlphaFold2. Using this pipeline, the researchers predicted over 17,849 high-confidence human PPIs, including thousands previously unknown interactions, offering significant new insights into biological mechanisms and disease.

References:

• Zhang J, Humphreys I R, Pei J, et al. Predicting protein-protein interactions in the human proteome[J]. Science, 2025: eadt1630.