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

Wang B, Valentine S, Plasencia M, Raghuraman S, Zhang X - BMC Bioinformatics (2010)

Bottom Line: For the model training and testing, a 10-fold cross-validation strategy was employed for three datasets each containing different charge states.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electronics and Information Engineering, Anhui University of Technology, Ma'anshan, 243002, China. wangbing@ustc.edu

ABSTRACT

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.

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Related in: MedlinePlus

The fraction of peptides vs. prediction accuracy variation threshold during the model construction process using the training dataset. The diagram shows the number of peptides which can be predicted in different accuracy variation levels.
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Figure 2: The fraction of peptides vs. prediction accuracy variation threshold during the model construction process using the training dataset. The diagram shows the number of peptides which can be predicted in different accuracy variation levels.

Mentions: After determining the optimal three-node hidden layer, we used 10-fold cross validation to study the performance of our proposed ANN regression model on the training dataset. Figure 2 displays the relation between fractions of peptides with a correct prediction of drift time for the threshold of ANN prediction variation. Our proposed ANN regression model performs best on the singly-charged dataset. With 10% of prediction variation threshold, the ANN regression model correctly predicted the drift time of 88.3% of the singly-charged peptide ions. The fraction of correct prediction increases to 94.4% when the prediction variation threshold is set at 15%. The performance of the proposed ANN regression model on the doubly-charged peptide ions is close to its performance for the singly-charged peptide ions.


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

Wang B, Valentine S, Plasencia M, Raghuraman S, Zhang X - BMC Bioinformatics (2010)

The fraction of peptides vs. prediction accuracy variation threshold during the model construction process using the training dataset. The diagram shows the number of peptides which can be predicted in different accuracy variation levels.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2874804&req=5

Figure 2: The fraction of peptides vs. prediction accuracy variation threshold during the model construction process using the training dataset. The diagram shows the number of peptides which can be predicted in different accuracy variation levels.
Mentions: After determining the optimal three-node hidden layer, we used 10-fold cross validation to study the performance of our proposed ANN regression model on the training dataset. Figure 2 displays the relation between fractions of peptides with a correct prediction of drift time for the threshold of ANN prediction variation. Our proposed ANN regression model performs best on the singly-charged dataset. With 10% of prediction variation threshold, the ANN regression model correctly predicted the drift time of 88.3% of the singly-charged peptide ions. The fraction of correct prediction increases to 94.4% when the prediction variation threshold is set at 15%. The performance of the proposed ANN regression model on the doubly-charged peptide ions is close to its performance for the singly-charged peptide ions.

Bottom Line: For the model training and testing, a 10-fold cross-validation strategy was employed for three datasets each containing different charge states.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electronics and Information Engineering, Anhui University of Technology, Ma'anshan, 243002, China. wangbing@ustc.edu

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus