A deep-learning-informed prior and Bayesian model for differential AP-MS interactome analysis
Abstract
Affinity-purification mass spectrometry (AP-MS) maps a bait proteins 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.
Lifecycle
- biorxiv v1 2026-07-09 source ↗
Discussion
Moderated digest of third-party discussion on Bluesky — substantive endorsement and critique.
-
AI x Bio Discovery @aixbiobot.bsky.social · 787 followers neutral
A deep-learning-informed prior and Bayesian model for differential AP-MS interactome analysis [new] Integrates structural deep learning prior and multi-evid. Bayesian model to identify condition-specific interactome changes in AP-MS.
-
#NeuroDegeneration preprints @prepub-neurodegen.bsky.social · 230 followers neutral
A deep-learning-informed prior and Bayesian model for differential AP-MS interactome analysis #NeuroDegeneration 🧪🧠 https://www.biorxiv.org/content/10.64898/2026.07.06.736690v1
-
bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · 4920 followers neutral
A deep-learning-informed prior and Bayesian model for differential AP-MS interactome analysis https://www.biorxiv.org/content/10.64898/2026.07.06.736690v1
-
bioRxivpreprint @biorxivpreprint.bsky.social · 8895 followers neutral
A deep-learning-informed prior and Bayesian model for differential AP-MS interactome analysis https://www.biorxiv.org/content/10.64898/2026.07.06.736690v1