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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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The fraction of predicted peptides vs. prediction accuracy variation threshold on the testing data.
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Figure 4: The fraction of predicted peptides vs. prediction accuracy variation threshold on the testing data.

Mentions: Figure 4 shows the overall prediction performance of our proposed ANN regression model on the testing data. Our regression model has a similar performance for the drift time prediction of the singly- and the doubly-charged peptide ions. The prediction accuracy is poor for the triply-charged peptides. This is consistent with the results of our training dataset (Figure 2). In general, the prediction accuracy of our model on the testing dataset is slightly less than its prediction on the training dataset. For example, the prediction accuracy on the testing dataset is 0.909, 0.872 and 0.727 for C1, C2 and C3, respectively, while the prediction accuracy on the training dataset is 0.944, 0.936 and 0.742 using a prediction variation value that is less than 15%. This is understandable because the testing dataset did not contribute to the construction of the ANN regression model. On the other hand, the reduction of prediction accuracy is relatively small as also shown by the comparison of Figures 2 and 4, which demonstrates our proposed model has a reasonable degree of generalization performance.


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 predicted peptides vs. prediction accuracy variation threshold on the testing data.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: The fraction of predicted peptides vs. prediction accuracy variation threshold on the testing data.
Mentions: Figure 4 shows the overall prediction performance of our proposed ANN regression model on the testing data. Our regression model has a similar performance for the drift time prediction of the singly- and the doubly-charged peptide ions. The prediction accuracy is poor for the triply-charged peptides. This is consistent with the results of our training dataset (Figure 2). In general, the prediction accuracy of our model on the testing dataset is slightly less than its prediction on the training dataset. For example, the prediction accuracy on the testing dataset is 0.909, 0.872 and 0.727 for C1, C2 and C3, respectively, while the prediction accuracy on the training dataset is 0.944, 0.936 and 0.742 using a prediction variation value that is less than 15%. This is understandable because the testing dataset did not contribute to the construction of the ANN regression model. On the other hand, the reduction of prediction accuracy is relatively small as also shown by the comparison of Figures 2 and 4, which demonstrates our proposed model has a reasonable degree of generalization performance.

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