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Peptide Retention Time (peptide + retention_time)

Distribution by Scientific Domains
Chemistry100%

Selected Abstracts

Improving peptide identification using an empirical peptide retention time database

RAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 1 2009
Wei Sun
Peptide retention time (RT) is independent of tandem mass spectrometry (MS/MS) parameters and can be combined with MS/MS information to enhance peptide identification. In this paper, we utilized peptide empirical RT and MS/MS for peptide identification. This new approach resulted in the construction of an Empirical Peptide Retention Time Database (EPRTD) based on peptides showing a false-positive rate (FPR) ,1%, detected in several liquid chromatography (LC)/MS/MS analyses. In subsequent experiments, the RT of peptides with FPR >1% was compared with empirical data derived from the EPRTD. If the experimental RT was within a specified time range of the empirical value, the corresponding MS/MS spectra were accepted as positive. Application of the EPRTD approach to simple samples (known protein mixtures) and complex samples (human urinary proteome) revealed that this method could significantly enhance peptide identification without compromising the associated confidence levels. Further analysis indicated that the EPRTD approach could improve low-abundance peptides and with the expansion of the EPRTD the number of peptide identifications will be increased. This approach is suitable for large-scale clinical proteomics research, in which tens of LC/MS/MS analyses are run for different samples with similar components. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Retention time prediction using the model of liquid chromatography of biomacromolecules at critical conditions in LC-MS phosphopeptide analysis

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 19 2010
Tatiana Yu Perlova
Abstract LC combined with MS/MS analysis of complex mixtures of protein digests is a reliable and sensitive method for characterization of protein phosphorylation. Peptide retention times (RTs) measured during an LC-MS/MS run depend on both the peptide sequence and the location of modified amino acids. These RTs can be predicted using the LC of biomacromolecules at critical conditions model (BioLCCC). Comparing the observed RTs to those obtained from the BioLCCC model can provide additional validation of MS/MS-based peptide identifications to reduce the false discovery rate and to improve the reliability of phosphoproteome profiling. In this study, energies of interaction between phosphorylated residues and the surface of RP separation media for both "classic" alkyl C18 and polar-embedded C18 stationary phases were experimentally determined and included in the BioLCCC model extended for phosphopeptide analysis. The RTs for phosphorylated peptides and their nonphosphorylated analogs were predicted using the extended BioLCCC model and compared with their experimental RTs. The extended model was evaluated using literary data and a complex phosphoproteome data set distributed through the Association of Biomolecular Resource Facilities Proteome Informatics Research Group 2010 study. The reported results demonstrate the capability of the extended BioLCCC model to predict RTs which may lead to improved sensitivity and reliability of LC-MS/MS-based phosphoproteome profiling. [source]

Predictions of peptides' retention times in reversed-phase liquid chromatography as a new supportive tool to improve protein identification in proteomics

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 4 2009
Tomasz B, czek Dr.
Abstract One of the initial steps of proteomic analysis is peptide separation. However, little information from RP-HPLC, employed for peptides separation, is utilized in proteomics. Meanwhile, prediction of the retention time for a given peptide, combined with routine MS/MS data analysis, could help to improve the confidence of peptide identifications. Recently, a number of models has been proposed to characterize quantitatively the structure of a peptide and to predict its gradient RP-HPLC retention at given separation conditions. The chromatographic behavior of peptides has usually been related to their amino acid composition. However, different values of retention coefficients of the same amino acid in different peptides at different neighborhoods were commonly observed. Therefore, specific retention coefficients were derived by regression analysis or by artificial neural networks (ANNs) with the use of a set of peptides retention. In the review, various approaches for peptide elution time prediction in RP-HPLC are presented and critically discussed. The contribution of sequence dependent parameters (e.g., amphipathicity or peptide sequence) and peptide physicochemical descriptors (e.g., hydrophobicity or peptide length) that have been shown to affect the peptide retention time in LC are considered and analyzed. The predictive capability of the retention time prediction models based on quantitative structure,retention relationships (QSRRs) are discussed in details. Advantages and limitations of various retention prediction strategies are identified. It is concluded that proper processing of chromatographic data by statistical learning techniques can result in information of direct use for proteomics, which is otherwise wasted. [source]