timsTOF Pro datasets for CCS prediction

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 collection was acquired using a two-dimensional LC/trapped ion mobility spectrometry/quadrupole/time-of-flight MS analysis of HeLa cell digests created using 7 different proteases and converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters, 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 8 groups (four charges for both tryptic/non-tryptic peptides). Position dependent intrinsic size parameters were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of ~0.98 for the entire population of peptides.