Prediction of high-responding peptides for targeted protein assays by mass spectrometry

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Abstract
Development of sensitive mass spectrometry–based assays for complex biofluids depends on the ability to identify signature peptides that produce the strongest signals. Fusaro et al. use protein physicochemical properties to predict high-responding peptides in data obtained from complex samples such as plasma. Protein biomarker discovery produces lengthy lists of candidates that must subsequently be verified in blood or other accessible biofluids. Use of targeted mass spectrometry (MS) to verify disease- or therapy-related changes in protein levels requires the selection of peptides that are quantifiable surrogates for proteins of interest. Peptides that produce the highest ion-current response (high-responding peptides) are likely to provide the best detection sensitivity. Identification of the most effective signature peptides, particularly in the absence of experimental data, remains a major resource constraint in developing targeted MS–based assays. Here we describe a computational method that uses protein physicochemical properties to select high-responding peptides and demonstrate its utility in identifying signature peptides in plasma, a complex proteome with a wide range of protein concentrations. Our method, which employs a Random Forest classifier, facilitates the development of targeted MS–based assays for biomarker verification or any application where protein levels need to be measured.