This review focuses on a central shift in interaction proteomics: the transition from depth-limited, low-throughput studies to scalable, high-throughput mapping of protein interaction networks.

Recent advances in mass spectrometry, sample preparation, and data acquisition are enabling interaction datasets at a scale that was not feasible only a few years ago. Short-gradient chromatography and faster acquisition strategies now allow rapid profiling of protein complexes across multiple conditions, opening the door to systematic, comparative interactomics.

A key theme is that no single method captures the full complexity of the interactome. Affinity purification and proximity labeling remain essential for targeted studies, while co-fractionation approaches provide a scalable route to map protein complexes under native conditions and across biological states.

Importantly, the field is moving beyond static interaction maps toward context-specific networks that reflect cellular state, perturbation, and disease. This shift is reinforced by the integration of machine learning and structural prediction, enabling the interpretation of increasingly large and complex datasets.

Together, these developments position interaction proteomics as a quantitative and systems-level framework to study how protein networks reorganize in response to biological perturbations.

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Need for speed: advances in the era of high throughput interaction proteomics →