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

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.

Figure 1

Box plots of peptide molecular weight (A), sequence length (B) and drift time distribution (C) in the three datasets. The central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points that are not outliers, the cross points are outliers if they are larger than Q3+1.5*(Q3-Q1) or smaller than Q1-1.5*(Q3-Q1), where Q1 and Q3 are the 25th and 75th percentiles, respectively.

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.

Figure 3

Relationship between the observed and predicted drift times for the three peptide datasets. Subfigures A, B and C are the regression results of our proposed ANN model for singly-, doubly-, and triply-charged peptide ions, respectively. The linear function in each subfigure is achieved by fitting the predicted results to observed drift times, and the line is the corresponding fitted curve. The correlation coefficients between observed and predicted peptide ion drift time are also shows in each subfigure.

Figure 4

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

Figure 5

A typical 3-layers neural network architecture.