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Motivation Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section (CCS), a highly reproducible physicochemical property dependent on the ion's mass, charge and conformation in the gas phase. Thus, known peptide ion mobilities can be used to tailor acquisition methods or to refine database search results. The large space of potential peptide sequences, driven also by posttranslational modifications of amino acids, motivates an in silico predictor for peptide CCS. Recent studies explored the general performance of varying machine-learning techniques, however, the workflow engineering part was of secondary importance. For the sake of applicability, such a tool should be generic, data driven, and offer the possibility to be easily adapted to individual workflows for experimental design and data processing. Results We created ionmob , a Python-based framework for data preparation, training, and prediction of collisional cross-section values of peptides. It is easily customizable and includes a set of pretrained, ready-to-use models and preprocessing routines for training and inference. Using a set of ≈21 000 unique phosphorylated peptides and ≈17 000 MHC ligand sequences and charge state pairs, we expand upon the space of peptides that can be integrated into CCS prediction. Lastly, we investigate the applicability of in silico predicted CCS to increase confidence in identified peptides by applying methods of re-scoring and demonstrate that predicted CCS values complement existing predictors for that task. Availability and implementation The Python package is available at github: https://github.com/theGreatHerrLebert/ionmob. [ABSTRACT FROM AUTHOR]

As high-resolution mass spectrometry (HRMS) becomes increasingly available, the need of software tools capable of handling more complex data is surging. The complexity of the HRMS data stems partly from the presence of isotopes that give rise to more peaks to interpret compared to lower resolution instruments. However, a new generation of fine isotope calculators is on the rise. They calculate the smallest possible sets of isotopologues. However, none of these calculators lets the user specify the joint probability of the revealed envelope in advance. Instead, the user must provide a lower limit on the probability of isotopologues of interest, that is, provide minimal peak height. The choice of such threshold is far from obvious. In particular, it is impossible to a priori balance the trade-off between the algorithm speed and the portion of the revealed theoretical spectrum. We show that this leads to considerable inefficiencies. Here, we present IsoSpec: an algorithm for fast computation of isotopologues of chemical substances that can alternate between joint probability and peak height threshold. We prove that IsoSpec is optimal in terms of time complexity. Its implementation is freely available under a 2-clause BSD license, with bindings for C++, C, R, and Python.