In Saccharomyces cerevisiae, several large-scale efforts have systematically catalogued the protein-protein interactions in a single environment. However, little is known about how the protein interactome changes across genetic or environmental perturbations. Current technologies, which assay one PPI at a time, are too low throughput to make it practical to study these protein interactome dynamics. Here, we develop a massively parallel protein-protein interaction Sequencing platform (iSeq) using a novel double barcoding system in conjunction with the murine dihydrofolate reductase-based protein-fragment complementation assay (PCA). Random barcodes are inserted into yeast and are mated to existing PCA strains. Mating of barcoded haploid PCA pools and translocation of barcodes in vivo and en masse yields diploid PCA strains, each with a double barcode representing a specific PPI. In a pilot study, we generated a pool of 2500 strains that represent 100 PPIs, each tagged with 25 unique double barcodes. Growth of this cell pool and sequencing of double barcodes yields an accurate fitness measurement of each double barcode in the pool, which can be translated to an interaction score for each pairwise protein combination. We find that PPI interaction scores using iSeq are highly reproducible across both double barcodes and growth replicates. Under standard growth conditions, our system detects most previously-reported PPIs. Additionally, we identify many additional putative PPIs with weaker interaction scores. By growing this pool in four different conditions, we identify several dynamic PPIs that reproducibly change in strength across environments. Verification of new and dynamic PPIs is ongoing. Finally, we demonstrate that the iSeq platform is capable of generating and assaying millions of PPIs in parallel. Current efforts are focused on building larger iSeq yeast pools to perform genome-scale dynamic PPI studies.