PeptideProphet 5 is designed to model the search engine output scores and other attributes of each PSM to compute a machine-learning Bayesian model of the correct and incorrect distribution of PSMs, with or without the aid of a decoy database. The model is refined based on other attributes such as the mass delta, the number of missed proteolytic cleavages, the number of protease-specific termini, the precursor charge, delta from predicted retention time, and the number of variable mass modifications. PSMs that are more like the correct-PSM distribution are boosted, and PSMs that are more like the incorrect-PSM distribution are penalized. The models are learned from each dataset, which can vary widely. Each PSM is assigned a probability of being correct based on the learned model, and output tables and ROC plots are provided so that the user may select a probability threshold that meets their FDR or sensitivity/specificity requirements. The iProphet tool has two primary functions. First, it can combine multiple output pepXML files of PeptideProphet from multiple analyses of the input spectra, typically via different search engines. For example, if a dataset is searched with both Comet and SpectraST, each set of output results is first modeled separately by PeptideProphet (since the search engine scores are different) to produce PSM probabilities, and then combined into a single set of results by iProphet based on those probabilities. The second main functionality of iProphet is to further refine all the input PeptideProphet probabilities based on other corroborating evidence within the whole dataset. iProphet applies models for the number of search engines agreeing on the result, the number of replicate spectra of a precursor ion, the number of different charge states for the same peptidoform, the number of different peptidoforms for the same peptide sequence, and the number of PSMs for each protein sequence. In each case, multiple lines of corroborating evidence boost probabilities, and lack thereof reduces probabilities, as learned by the Bayesian machine-learning model for each dataset. If any of the above attributes provide no discriminating power between correct and incorrect, there is simply no boost/penalty to the probabilities. In nearly all cases, iProphet will markedly improve the overall discrimination between correct and incorrect in any dataset and yield more identifications above any selected tolerable FDR than without. Output modeling results and probabilities are added to the pepXML file.