AP3: An Advanced Proteotypic Peptide Predictor for Targeted Proteomics by Incorporating Peptide Digestibility

Abstract

The selection of proteotypic peptides, that is, detectable unique representatives of proteins of interest, is a key step in targeted proteomics. To date, much effort has been made to understand the mechanisms underlying peptide detection in liquid chromatography–tandem mass spectrometry (LC-MS/MS) based shotgun proteomics and to predict proteotypic peptides in the absence of experimental LC-MS/MS data. However, the prediction accuracy of existing tools is still unsatisfactory. We find that one crucial reason is their neglect of the significant influence of protein proteolytic digestion on peptide detectability in shotgun proteomics. Here, we present an Advanced Proteotypic Peptide Predictor (AP3), which explicitly takes peptide digestibility into account for the prediction of proteotypic peptides. Specifically, peptide digestibility is first predicted for each peptide and then incorporated as a feature into the peptide detectability prediction model. Our results demonstrated that peptide digestibility is the most important feature for the accurate prediction of proteotypic peptides in our model. Compared with the existing available algorithms, AP3 showed 10.3–34.7% higher prediction accuracy. On a targeted proteomics data set, AP3 accurately predicted the proteotypic peptides for proteins of interest, showing great potential for assisting the design of targeted proteomics experiments.

The Age of Data‐Driven Proteomics: How Machine Learning Enables Novel Workflows

“…It should be noted that most of these models also inherently predict the peptide's detectability by MS. While older digestibility/detectability predictors used decision tree ensembles, [43,44] current state-of-the-art predictors employ DL. [45,46] After enzymatic digestion, LC is often used as a first step to separate peptides based on their physicochemical properties.…”

Biofunctionality of Enzymatically Derived Peptides from Codfish (Gadus morhua) Frame: Bulk In Vitro Properties, Quantitative Proteomics, and Bioinformatic Prediction

“…Flyability in ESI-MS proteomics describes the ability/probability of peptides to be ionized in the gas phase and subsequently detected. Several studies have investigated the underlying principles governing the parameter, and computational models have been produced to estimate the highly complex and sequence dependent parameter for prediction of high responding tryptic peptides as biomarkers in protein quantification [32][33][34][35]. Although peptide length in general is negatively correlated with flyability thereby introducing an intensity bias towards shorter peptides, this is not reflected in our MS data, when compared to PCL DH .…”

Biofunctionality of Enzymatically Derived Peptides from Codfish (Gadus morhua) Frame: Bulk in vitro Properties, Quantitative Proteomics, and Bioinformatic Prediction

“…Flyability in ESI-MS proteomics describes the ability/probability of peptides to be ionized in the gas phase and subsequently detected. Several studies have investigated the underlying principles governing the parameter, and computational models have been produced to estimate the highly complex and sequence dependent parameter for prediction of high responding tryptic peptides as biomarkers in protein quantification [32][33][34][35]. Although peptide length in general is negatively correlated with flyability thereby introducing an intensity bias towards shorter peptides, this is not reflected in our MS data, when compared to PCLDH.…”