A deep-learning–informed prior and Bayesian model for differential AP-MS interactome analysis

Affinity-purification mass spectrometry (AP-MS) maps a bait protein’s partners, but every purification also captures abundant non-specific background that masks genuine interactors. Established tools such as SAINTexpress and CompPASS score one evidence type, treat correlated signals as independent, and ignore prior knowledge of likely interactions. BayesInteractomics, an open-source Julia framework, addresses both limitations by combining machine learning with Bayesian statistics. A neural network trained on protein structures predicts direct binding. A calibrated meta-learner turns this into an informed prior. The prior guides a Bayesian copula-mixture model integrating three AP-MS evidence streams: enrichment, co-abundance, and detection reproducibility. Each candidate receives an interaction probability at a controlled false-discovery rate, optionally updated by structural docking. On synthetic data it ranks first in every benchmark (median AUROC 0.747), and across independent studies it raises high-confidence-call reproducibility from 21% to 79%. It also identifies which interactions are gained or lost between two conditions, unlike established tools.