MS2PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation

Area of Science:

• Proteomics
• Computational Biology
• Mass Spectrometry

Background:

• Accurate prediction of tandem mass spectrometry (MS2) spectra is crucial for peptide identification and quantification.
• Existing prediction models may not fully capture the complexities of different fragmentation methods and post-translational modifications.

Purpose of the Study:

• To present an enhanced MS2 peak intensity prediction server (MS2PIP) with improved accuracy for common fragment ions.
• To integrate post-translational modification data for modified peptides.
• To provide prediction models for both Collision-Induced Dissociation (CID) and Higher-Energy Collisional Dissociation (HCD) fragmentation.

Main Methods:

• Development of an improved MS2 peak intensity prediction model based on the previous MS2PIP model.
• Integration of the Unimod database for handling post-translational modifications.
• Training separate prediction models for CID and HCD fragmentation using Orbitrap-LTQ and Q-Exactive Orbitrap data.

Main Results:

• The enhanced MS2PIP server accurately predicts MS2 spectra for charge 2+ and 3+ peptides.
• Prediction models for HCD fragmentation achieve accuracy comparable to CID.
• Separate training for CID and HCD models demonstrably improves prediction accuracy for each method.

Conclusions:

• The MS2PIP prediction server offers a valuable tool for researchers by providing accurate MS2 spectra predictions.
• The server supports the analysis of both unmodified and modified peptides across different fragmentation techniques.
• The improved prediction accuracy facilitates more reliable peptide identification and characterization in mass spectrometry-based proteomics.