Abstract We describe peptide mapping through Split Antibiotic Resistance Complementation (SpARC-map), a method to identify the probable interface between two interacting proteins. Our method is based on in vivo affinity selection inside a bacterial host, and uses high throughput DNA sequencing to infer the probable protein-protein interaction (PPI) interfaces. SpARC-map uses only routine microbiology techniques, with no reliance on specialized instrumentation, dedicated reagents, or reconstituting protein complexes in vitro. SpARC-map can be tuned to detect PPIs over a broad range of affinities, multiplexed to probe multiple PPIs in parallel, and its nonspecific background can be precisely measured, enabling the sensitive detection of weak PPIs. Using SpARC-map, we recover known PPI interfaces in the p21-PCNA, p53-MDM2, and MYC-MAX complexes. We also use SpARC-map to probe the purinosome, the weakly bound complex of six purine biosynthetic enzymes, where no PPI interfaces are known. There, we identify interfaces that satisfy structural requirements for substrate channeling, as well as protein surfaces that participate in multiple distinct interactions, which we validate using site-specific photocrosslinking in live human cells. Finally, we show that SpARC-map results can impose stringent constraints on machine learning based structure prediction. Significance Statement Protein-protein interactions (PPIs) are vital to biological function, and the identity of the PPI interface can lend valuable insights on the structure/function of protein complexes. Identifying PPI interfaces using standard structural biology approaches, such as x-ray protein crystallography or cryo-electron microscopy, is technically challenging and require reconstituting protein complexes in vitro. As an alternative, SpARC-map is a sensitive and potentially high throughput method for identifying probable PPI interfaces, requiring only standard laboratory methods and straightforward bioinformatic analyses. Its accessibility gives SpARC-map the potential to be a tool of first resort for identifying PPIs and probable PPI interfaces, thereby facilitating scientific discovery across molecular and cellular biology.