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Introduction of ion mobility mass spectrometry (IMS/MS) into the proteomic workflow provides an orthogonal separation to the widely used LC-MS platforms. IMS also provides structural information that could facilitate peptide identification. However, the lack of tools capable of predictive power in a high-throughput fashion makes peptide global profiling quite challenging. To target this issue, a computational workflow was developed based on biophysical principles to predict the collision cross-section area (CCS) of peptides as measured from IMS/MS experiments. Hosted on a web server, it allows the user to input a primary sequence (query) and retrieve information on peptide structure, sequence, and corresponding CCS. The current version is designed to identify peptide sequences up to 23 residues in length, in its higher charge state, based on a match of the molecule m/z and CCS. The protocol was validated against a 128-sequences-dataset and CCS predicted within 2.8% average error., (Copyright © 2013 Wiley Periodicals, Inc.)

Recent technological advances have made multidimensional peptide separation techniques coupled with tandem mass spectrometry the method of choice for high- throughput identification of proteins. Due to these advances, the development of software tools for large-scale, fully automated, unambiguous peptide identification is highly necessary. In this work, we have used as a model the nuclear proteome from Jurkat cells and present a processing algorithm that allows accurate predictions of random matching distributions, based on the two SEQUEST scores Xcorr and ΔCn. Our method permits a very simple and precise calculation of the probabilities associated with individual peptide assignments, as well as of the false discovery rate among the peptides identified in any experiment A further mathematical analysis demonstrates that the score distributions are highly dependent on database size and precursor mass window and suggests that the probability associated with SEQUEST scores depends on the number of candidate peptide sequences available for the search. Our results highlight the importance of adjusting the filtering criteria to discriminate between correct and incorrect peptide sequences according to the circumstances of each particular experiment. [ABSTRACT FROM AUTHOR]