Prediction of Ion Drift Times for a Proteome-Wide Peptide Set Using Partial Least Squares Regression, Least-Squares Support Vector Machine and Gaussian Process

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

Background

Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.

Results

In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.

Conclusions

Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques.

QSPR modeling of bioconcentration factor of nonionic compounds using Gaussian processes and theoretical descriptors derived from electrostatic potentials on molecular surface

In the present study, geometrical structures were constructed and optimized for 122 nonionic organic compounds at the quantum-mechanical HF/6-31G level of theory. The electrostatic potentials and subsequent structural descriptors derived from them were obtained. Gaussian process, and for comparison purpose, multiple linear regression (MLR) and support vector machine (SVM), were then employed to build the quantitative structure-bioconcentration factor relationships. Systematical validations including internal leave-one-out cross-validation, the validation for external test set, as well as a more rigorous Monte Carlo cross-validation were made to confirm the reliability of the constructed models. It has been found that the quantities derived from electrostatic potential, V(min) and ∑V(s,ind)(-), together with the molecular volume (V(mc)), dipole moment (μ) and the energy level of highest occupied molecular orbital (E(HOMO)) can be well used to express the quantitative structure-property relationship of this sample set. Both linear and nonlinear models can give satisfactory results, and the GP, which be capable of handing with linear and nonlinear-hybrid relationship through a mixed covariance function, appears to have better fitting and predictive abilities than other two statistical methods. The coefficient of determination r(pred)(2) and root mean square error of prediction (RMSEP) for the external test set are 0.953 and 0.337, respectively.

Machine learning based prediction for peptide drift times in ion mobility spectrometry

MOTIVATION

Ion mobility spectrometry (IMS) has gained significant traction over the past few years for rapid, high-resolution separations of analytes based upon gas-phase ion structure, with significant potential impacts in the field of proteomic analysis. IMS coupled with mass spectrometry (MS) affords multiple improvements over traditional proteomics techniques, such as in the elucidation of secondary structure information, identification of post-translational modifications, as well as higher identification rates with reduced experiment times. The high throughput nature of this technique benefits from accurate calculation of cross sections, mobilities and associated drift times of peptides, thereby enhancing downstream data analysis. Here, we present a model that uses physicochemical properties of peptides to accurately predict a peptide's drift time directly from its amino acid sequence. This model is used in conjunction with two mathematical techniques, a partial least squares regression and a support vector regression setting.

RESULTS

When tested on an experimentally created high confidence database of 8675 peptide sequences with measured drift times, both techniques statistically significantly outperform the intrinsic size parameters-based calculations, the currently held practice in the field, on all charge states (+2, +3 and +4).

AVAILABILITY

The software executable, imPredict, is available for download from http:/omics.pnl.gov/software/imPredict.php

CONTACT

rds@pnl.gov

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.