Sequence-Specific Model for Predicting Peptide Collision Cross Section Values in Proteomic Ion Mobility Spectrometry

Abstract

The contribution of peptide amino acid sequence to collision cross section values (CCS) has been investigated using a dataset of ∼134 000 peptides of four different charge states (1+ to 4+). The migration data were acquired using a two-dimensional liquid chromatography (LC)/trapped ion mobility spectrometry/quadrupole/time-of-flight mass spectrometry (MS) analysis of HeLa cell digests created using seven different proteases and was converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters (ISP), we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into eight groups (four charges for both tryptic/nontryptic peptides). Position-dependent ISPs were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of ∼0.981 for the entire population of peptides. We find that ion mobility is strongly affected by the peptide’s ability to solvate the positively charged sites. Internal positioning of polar residues and proline leads to decreased CCS values as they improve charge solvation; conversely, this ability decreases with increasing peptide charge due to electrostatic repulsion. Furthermore, higher helical propensity and peptide hydrophobicity result in a preferential formation of extended structures with higher than predicted CCS values. Finally, acidic/basic residues exhibit position-dependent ISP behavior consistent with electrostatic interaction with the peptide macrodipole, which affects the peptide helicity. The MS raw data files have been deposited with the ProteomeXchange Consortium via the jPOST partner repository () with the dataset identifiers PXD021440/JPST000959, PXD022800/JPST001017, and PXD026087/ JPST001176.

Toward an Integrated Machine Learning Model of a Proteomics Experiment

“…If used as a separation device, the ion mobility or arrival times can be calibrated into collisional cross sections (CCSs). Machine learning has already been used to predict CCS values for different classes of analytes ,, and integrated in software for identifying unknown compounds . As both experimental resolution of ion mobility devices and accuracy of machine-learned models increase, the value of CCS prediction also increases.…”

Field-Switching Repeller Flowing Atmospheric-Pressure Afterglow Drift Tube Ion Mobility Spectrometry

“…Drift tube ion mobility spectrometry (DTIMS) is an analytical separation technique based on the ions’ individual terminal velocity as they pass through a region with uniform electric field and drift gas performed mainly at atmospheric or subatmospheric pressure . DTIMS has been widely used for detection of chemical warfare agents, , environmental contaminants, narcotics, , aerosols, , proteomics investigations, − and gas-phase structural elucidations. , …”

Temperature Dependence of Retention Behavior of Phosphorylated Peptides in Ion-Pair Reversed-Phase Liquid Chromatography

“…Liquid chromatography/tandem mass spectrometry (LC/MS/MS) is currently one of the most powerful techniques to monitor alterations in intracellular phosphorylation events on a proteome-wide scale [3,4]. Traditionally, only MS/MS spectra are used for peptide identification in LC/MS/MS-based shotgun proteomics, but peptide retention times in reversed-phase LC (RPLC) and other information are also important and help to increase confidence in peptide identification [5,6]. The prediction accuracy of peptide characteristics including retention times have improved dramatically in recent years with the availability of large data sets obtained by LC/MS/MS [7][8][9][10].…”