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Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry. 2010

Bing Wang, and Steve Valentine, and Manolo Plasencia, and Sriram Raghuraman, and Xiang Zhang
Department of Electronics and Information Engineering, Anhui University of Technology, Ma'anshan, 243002, China. wangbing@ustc.edu

BACKGROUND There is an increasing usage of ion mobility-mass spectrometry (IMMS) in proteomics. IMMS combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS). It separates and detects peptide ions on a millisecond time-scale. IMS separates peptide ions based on drift time that is determined by the collision cross-section of each peptide ion in a given experiment condition. A peptide ion's collision cross-section is related to the ion size and shape resulted from the peptide amino acid sequence and their modifications. This inherent relation between the drift time of peptide ion and peptide sequence indicates that the drift time of peptide ions can be used to infer peptide sequence and therefore, for peptide identification. RESULTS This paper describes an artificial neural networks (ANNs) regression model for the prediction of peptide ion drift time in IMMS. Each peptide in this work was represented using three descriptors (i.e., molecular weight, sequence length and a two-dimensional sequence index). An ANN predictor consisting of four input nodes, three hidden nodes and one output node was constructed for peptide ion drift time prediction. For the model training and testing, a 10-fold cross-validation strategy was employed for three datasets each containing different charge states. Dataset one contains 212 singly-charged peptide ions, dataset two has 306 doubly-charged peptide ions, and dataset three has 77 triply-charged peptide ions. Our proposed method achieved 94.4%, 93.6% and 74.2% prediction accuracy for singly-, doubly- and triply-charged peptide ions, respectively. CONCLUSIONS An ANN-based method has been developed for predicting the drift time of peptide ions in IMMS. The results achieved here demonstrate the effectiveness and efficiency of the prediction model. This work can enhance the confidence of protein identification by combining with current database search approaches for protein identification.

UI MeSH Term Description Entries
D007477 Ions An atom or group of atoms that have a positive or negative electric charge due to a gain (negative charge) or loss (positive charge) of one or more electrons. Atoms with a positive charge are known as CATIONS; those with a negative charge are ANIONS.
D010455 Peptides Members of the class of compounds composed of AMINO ACIDS joined together by peptide bonds between adjacent amino acids into linear, branched or cyclical structures. OLIGOPEPTIDES are composed of approximately 2-12 amino acids. Polypeptides are composed of approximately 13 or more amino acids. PROTEINS are considered to be larger versions of peptides that can form into complex structures such as ENZYMES and RECEPTORS. Peptide,Polypeptide,Polypeptides
D011506 Proteins Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein. Gene Products, Protein,Gene Proteins,Protein,Protein Gene Products,Proteins, Gene
D013058 Mass Spectrometry An analytical method used in determining the identity of a chemical based on its mass using mass analyzers/mass spectrometers. Mass Spectroscopy,Spectrometry, Mass,Spectroscopy, Mass,Spectrum Analysis, Mass,Analysis, Mass Spectrum,Mass Spectrum Analysis,Analyses, Mass Spectrum,Mass Spectrum Analyses,Spectrum Analyses, Mass
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D040901 Proteomics The systematic study of the complete complement of proteins (PROTEOME) of organisms. Peptidomics

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