Fig. 1

Pipeline overview. a. Co-fractionation profiles from known interactors, ribosomal proteins P61247 (black) and P62899 (grey). b. Co-fractionation profiles from non- interacting protein pair, Q6IN85 (black) and E9PGT1 (grey). c. Pipeline workflow. Raw data consists of co-fractionation profiles grouped by replicate and condition. In pre-processing, Gaussian mixture models are fit to each co-fractionation profile to obtain peak height, width, and center. If there are multiple replicates, the Alignment module adjusts profiles such that Gaussian peaks for the same protein occur in the same fraction across replicates. Changes in protein amounts between conditions, i.e. fold changes, are computed in the FoldChange module. Inter- actions between pairs of proteins are predicted by first calculating distance measures between each pair of proteins and feeding these into a Naive Bayes supervised learning classifier. Known (non-)interactions from a reference database, e.g. CORUM, are used for training. Finally, the list of predicted pairwise interactions is processed by an optimized ClusterONE algorithm [16] to predict protein complexes