The Importance of Peptide Detectability for Protein Identification, Quantification, and Experiment Design in MS/MS Proteomics

Peptide detectabilay is defined as the probability that a peptide is identified in an LC-MS/MS experiment and has been useful in providing solutions to protein inference and label-free quantification Previously predictors for peptide detectability trained on standard or complex samples were proposed Although the models trained on complex samples may benefit from the large training data sets it is unclear to what extent they are affected by the unequal abundances of identified proteins To address this challenge and improve detectability prediction we present a new algorithm for the iterative learning of peptide detectability from complex mixtures We provide evidence that the new method approximates detectability with useful accuracy and, based on its design can be used to interpret the outcome of other learning strategies We studied the properties of peptides from the bacterium Deinococcus radiodurans and found that at standard quantities, its tryptic peptides can be roughly classified as either detectable or undetectable, with a relatively small fraction having medium detectability We extend the concept of detectability from peptides to proteins and apply the model to predict the behavior of a replicate LC-MS/MS experiment from a single analysis Finally, our study summarizes a theoretical framework for peptide/protein identification and label-free quantification