Algorithms for Characterizing Peptides and Glycopeptides with Mass Spectrometry
The emergence of tandem mass spectrometry (MS/MS) technology has significantly accelerated protein identification and quantification in proteomics. It enables high-throughput analysis of proteins and their quantities in a complex protein mixture. A mass spectrometer can easily and rapidly generate large volumes of mass spectral data for a biological sample. This bulk of data makes manual interpretation impossible and has also brought numerous challenges in automated data analysis. Algorithmic solutions have been proposed and provide indispensable analytical support in current proteomic experiments. However, new algorithms are still needed to either improve result accuracy or provide additional data analysis capabilities for both protein identification and quantification. Accurate identification of proteins in a sample is the preliminary requirement of a proteomic study. In many cases, a mass spectrum cannot provide complete information to identify the peptide without ambiguity because of the inefficiency of the peptide fragmentation technique and the prevalent existence of noise. We propose ADEPTS to this problem using the complementary information provided in different types of mass spectra. Meanwhile, the occurrence of posttranslational modifications (PTMs) on proteins is another major issue that prevents the interpretation of a large portion of spectra. Using current software tools, users have to specify possible PTMs in advance. However, the number of possible PTMs has to be limited since specifying more PTMs to the software leads to a longer running time and lower result accuracy. Thus, we develop DeNovoPTM and PeaksPTM to provide efficient and accurate solutions. Glycosylation is one of the most frequently observed PTMs in proteomics. It plays important roles in many disease processes and thus has attracted growing research interest. However, lack of algorithms that can identify intact glycopeptides has become the major obstacle that hinders glycoprotein studies. We propose a novel algorithm, GlycoMaster DB, to fulfil this urgent requirement. Additional research is presented on protein quantification, which studies the changes of protein quantity by comparing two or more mass spectral datasets. A crucial problem in the quantification is to correct the retention time distortions between different datasets. Heuristic solutions from previous research have been used in practice but none of them has yet claimed a clear optimization goal. To address this issue, we propose a combinatorial model and practical algorithms for this problem.