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Prediction of Drift Time in Ion Mobility-Mass Spectrometry Based on Peptide Molecular Weight

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Abstract:

A computational model is introduced for predicting peptide drift time in ion mobility-mass spectrometry (IMMS). Each peptide was represented using a numeric descriptor: molecular weight. A simple linear regression predictor was constructed for peptides drift time prediction. Three datasets with different charge state assignments were used for the model training and testing. The dataset one contains 212 singly charged peptides, dataset two has 306 doubly charged peptides, and dataset three contains 77 triply charged peptides. Our proposed method achieved a prediction accuracy of 86.3%, 72.6%, and 59.7% for the dataset one, two and three, respectively. Peptide drift time prediction in IMMS will improve the confidence of peptide identifications by limiting the peptide search space during MS/MS database searching and therefore, reducing false discovery rate (FDR) of protein identification.





Keywords: Peptide drift time; charge state; ion mobility-mass spectrometry; linear regression model; molecular weight; protein identification

Document Type: Research Article

DOI: https://doi.org/10.2174/092986610791760360

Publication date: 2010-09-01

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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