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Retention time prediction using the model of liquid chromatography of biomacromolecules at critical conditions in LC‐MS phosphopeptide analysis
Authors:Tatiana Yu Perlova  Anton A Goloborodko  Yelena Margolin  Marina L Pridatchenko  Irina A Tarasova  Alexander V Gorshkov  Eugene Moskovets  Alexander R Ivanov  Mikhail V Gorshkov
Institution:1. Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences, Moscow, Russia;2. Harvard School of Public Health, HSPH Proteomics Resource, Department of Genetics and Complex Diseases, Boston, MA, USA;3. N. N. Semenov's Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia;4. MassTech Inc., Columbia, MD, USA;5. These authors have contributed equally to this work.
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.
Keywords:Bioinformatics  BioLCCC  LC–  MS  Phosphopeptides  Retention time prediction
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