Prediction of peptide ion collision cross sections from topological molecular structure and amino acid parameters

Quantitative structure-property relationships (QSPRs) have been developed to predict the ion mobility spectrometry (IMS) collision cross sections of singly protonated lysine-terminated peptides using information derived from topological molecular structure and various amino acid parameters. The primary amino acid sequence alone is sufficient to accurately predict the collision cross section. The models were built using multiple linear regression (MLR) and computational neural networks (CNNs). The best MLR model found contains six descriptors and predicts 94 of 113 peptides (83%) to within 2% of their experimentally determined values. The best CNN model using the same six descriptors predicts 105 of the 113 peptides (93%) to within 2% of their experimentally determined values. The best overall CNN model, using a different set of six descriptors, predicts 109 of the 113 peptides (96%) to within 2% of their experimentally determined values. In addition, this model can discriminate among peptides having identical amino acid composition, but differing in primary amino acid sequence. This represents a capability not found in previously described models. The descriptors used in the models presented may provide some insight into the nature of peptide ion folding in the gas phase.