Mass spectrometry-based methods are among the most commonly used techniques to characterize proteins in biological samples. With rapid technological developments allowing increasing throughput, thousands of proteins can now be monitored in a matter of hours. However, these advances brought a whole new set of analytical challenges. At the moment, it is no longer possible to rely on human experts to process the data. Instead, accurate computational tools are required.

In line with these observations, my research work has involved development of computational methods to facilitate the analysis of mass spectrometry-based experiments. In particular, the projects included in this thesis revolve around the chromatography step of such experiments, where peptides are separated according to their hydrophobicity.

The first part of the thesis describes an algorithm to predict retention time from peptide sequences. The method provides more accurate predictions compared to previous approaches, while being easily transferable to other chromatography setups. In addition, it gives equally good predictions for peptides carrying arbitrary posttranslational modifications as for unmodified peptides.

The second part of the thesis includes two applications of retention time predictions in the context of mass spectrometry-based proteomics experiments. First, we show how theoretical calculations of masses and retention times can be used to infer proteins in shotgun proteomics experiments. Secondly, we illustrate the use of retention time predictions to calculate optimized gradient functions for reversed-phase liquid chromatography.