A novel algorithm for validating peptide identification from a shotgun proteomics search engine

MSPoisDM: A Novel Peptide Identification Algorithm Optimized for Tandem Mass Spectra

Tandem mass spectrometry (MS/MS) plays an extremely important role in proteomics research. Thousands of spectra can be generated in modern experiments, how to interpret the LC-MS/MS is a challenging problem in tandem mass spectra analysis. Our peptide identification algorithm, MSPoisDM, is integrated the intensity information which produced by target-decoy statistics, although intensity information often undervalued. Furthermore, in order to combine the intensity information for better, we propose a novel concept scoring model which based on Poisson distribution. Compared with commonly used commercial software Mascot and Sequest at 1% FDR, the results show MSPoisDM is robust and versatile for various datasets which obtained from different instruments. We expect our algorithm MSPoisDM will be broadly applied in the proteomics studies.

A cost-sensitive online learning method for peptide identification

BACKGROUND

Post-database search is a key procedure in peptide identification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with a distribution of unbalanced PSMs. A more efficient learning strategy is required for improving the accuracy of peptide identification on challenging datasets. While complex learning models have larger power of classification, they may cause overfitting problems and introduce computational complexity on large-scale datasets. Kernel methods map data from the sample space to high dimensional spaces where data relationships can be simplified for modeling.

RESULTS

In order to tackle the computational challenge of using the kernel-based learning model for practical peptide identification problems, we present an online learning algorithm, OLCS-Ranker, which iteratively feeds only one training sample into the learning model at each round, and, as a result, the memory requirement for computation is significantly reduced. Meanwhile, we propose a cost-sensitive learning model for OLCS-Ranker by using a larger loss of decoy PSMs than that of target PSMs in the loss function.

CONCLUSIONS

The new model can reduce its false discovery rate on datasets with a distribution of unbalanced PSMs. Experimental studies show that OLCS-Ranker outperforms other methods in terms of accuracy and stability, especially on datasets with a distribution of unbalanced PSMs. Furthermore, OLCS-Ranker is 15-85 times faster than CRanker.

Targeted Identification of Protein Interactions in Eukaryotic mRNA Translation

To identify protein-protein interactions and phosphorylated amino acid sites in eukaryotic mRNA translation, replicate TAP-MudPIT and control experiments are performed targeting Saccharomyces cerevisiae genes previously implicated in eukaryotic mRNA translation by their genetic and/or functional roles in translation initiation, elongation, termination, or interactions with ribosomal complexes. Replicate tandem affinity purifications of each targeted yeast TAP-tagged mRNA translation protein coupled with multidimensional liquid chromatography and tandem mass spectrometry analysis are used to identify and quantify copurifying proteins. To improve sensitivity and minimize spurious, nonspecific interactions, a novel cross-validation approach is employed to identify the most statistically significant protein-protein interactions. Using experimental and computational strategies discussed herein, the previously described protein composition of the canonical eukaryotic mRNA translation initiation, elongation, and termination complexes is calculated. In addition, statistically significant unpublished protein interactions and phosphorylation sites for S. cerevisiae's mRNA translation proteins and complexes are identified.

l2 Multiple Kernel Fuzzy SVM-Based Data Fusion for Improving Peptide Identification

SEQUEST is a database-searching engine, which calculates the correlation score between observed spectrum and theoretical spectrum deduced from protein sequences stored in a flat text file, even though it is not a relational and object-oriental repository. Nevertheless, the SEQUEST score functions fail to discriminate between true and false PSMs accurately. Some approaches, such as PeptideProphet and Percolator, have been proposed to address the task of distinguishing true and false PSMs. However, most of these methods employ time-consuming learning algorithms to validate peptide assignments [1] . In this paper, we propose a fast algorithm for validating peptide identification by incorporating heterogeneous information from SEQUEST scores and peptide digested knowledge. To automate the peptide identification process and incorporate additional information, we employ l2 multiple kernel learning (MKL) to implement the current peptide identification task. Results on experimental datasets indicate that compared with state-of-the-art methods, i.e., PeptideProphet and Percolator, our data fusing strategy has comparable performance but reduces the running time significantly.

Independent highly sensitive characterization of asparagine deamidation and aspartic acid isomerization by sheathless CZE-ESI-MS/MS

Amino acids residues are commonly submitted to various physicochemical modifications occurring at physiological pH and temperature. Post-translational modifications (PTMs) require comprehensive characterization because of their major influence on protein structure and involvement in numerous in vivo process or signaling. Mass spectrometry (MS) has gradually become an analytical tool of choice to characterize PTMs; however, some modifications are still challenging because of sample faint modification levels or difficulty to separate an intact peptide from modified counterparts before their transfer to the ionization source. Here, we report the implementation of capillary zone electrophoresis coupled to electrospray ionization tandem mass spectrometry (CZE-ESI-MS/MS) by the intermediate of a sheathless interfacing for independent and highly sensitive characterization of asparagine deamidation (deaN) and aspartic acid isomerization (isoD). CZE selectivity regarding deaN and isoD was studied extensively using different sets of synthetic peptides based on actual tryptic peptides. Results demonstrated CZE ability to separate the unmodified peptide from modified homologous exhibiting deaN, isoD or both independently with a resolution systematically superior to 1.29. Developed CZE-ESI-MS/MS method was applied for the characterization of monoclonal antibodies and complex protein mixture. Conserved CZE selectivity could be demonstrated even for complex samples, and foremost results obtained showed that CZE selectivity is similar regardless of the composition of the peptide. Separation of modified peptides prior to the MS analysis allowed to characterize and estimate modification levels of the sample independently for deaN and isoD even for peptides affected by both modifications and, as a consequence, enables to distinguish the formation of l-aspartic acid or d-aspartic acid generated from deaN. Separation based on peptide modification allowed, as supported by the ESI efficiency provided by CZE-ESI-MS/MS properties, and enabled to characterize and estimate studied PTMs with an unprecedented sensitivity and proved the relevance of implementing an electrophoretic driven separation for MS-based peptide analysis.

An adaptive classification model for peptide identification

Background

Peptide sequence assignment is the central task in protein identification with MS/MS-based strategies. Although a number of post-database search algorithms for filtering target peptide spectrum matches (PSMs) have been developed, the discrepancy among the output PSMs is usually significant, remaining a few disputable PSMs. Current studies show that a number of target PSMs which are close to decoy PSMs can hardly be separated from those decoys by only using the discrimination function.

Results

In this paper, we assign each target PSM a weight showing its possibility of being correct. We employ a SVM-based learning model to search the optimal weight for each target PSM and develop a new score system, CRanker, to rank all target PSMs. Due to the large PSM datasets generated in routine database searches, we use the Cholesky factorization technique for storing a kernel matrix to reduce the memory requirement.

Conclusions

Compared with PeptideProphet and Percolator, CRanker has identified more PSMs under similar false discover rates over different datasets. CRanker has shown consistent performance on different test sets, validated the reasonability the proposed model.