Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics

Abstract

Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS ²PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting.

Graphical abstract

Highlights

• •

  First proteogenomics with PSM rescoring using machine learning–predicted spectra

• •

  Demonstrated on both ribosome profiling and nanopore RNA-Seq–derived databases

• •

  Rescoring leads to elevated stringency and increased identification rates

• •

  Rescoring compensates for the search space size issues in proteogenomics

In Brief

Proteogenomics suffers from statistical issues as the sequencing information inflates the database size. To compensate for this, rescoring with the machine learning–based spectrum predictors MS ²PIP and Prosit was implemented in a proteogenomics approach. This was demonstrated for both ribosome profiling and nanopore RNA-Seq derived databases. Postprocessing with Percolator showed that these techniques result in recovered and often even elevated stringency levels and identification rates. In this way, it allows to validate novel proteoforms through proteogenomics with unsurpassed confidence levels.