Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry

Background: There is an increasing usage of ion mobility-mass spectrometry (IMMS) in proteomics. IMMS combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS). It separates and detects peptide ions on a millisecond time-scale. IMS separates peptide ions based on drift time that is determined by the collision cross-section of each peptide ion in a given experiment condition. A peptide ion's collision cross-section is related to the ion size and shape resulted from the peptide amino acid sequence and their modifications. This inherent relation between the drift time of peptide ion and peptide sequence indicates that the drift time of peptide ions can be used to infer peptide sequence and therefore, for peptide identification. Results: This paper describes an artificial neural networks (ANNs) regression model for the prediction of peptide ion drift time in IMMS. Each peptide in this work was represented using three descriptors (i.e., molecular weight, sequ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Bing Wang, Steve Valentine, Manolo Plasencia, Sriram Raghuraman, Xiang Zhang
• Keywords: BMCBI 2010, Drift, Peptide Ions, Triply-charged Peptide Ions