Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning

Abstract

The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteomics practice. Herein, we propose deep learning as an ideal tool to considerably improve this prediction. A new peptide RT prediction tool, DeepRT, was designed using a capsule network model, and the public data sets containing peptides separated by reverse-phase liquid chromatography were used to evaluate the DeepRT performance. Compared with other prevailing RT predictors, DeepRT attained overall improvement in the prediction of peptide RTs with an R of ∼0.994. Moreover, DeepRT was able to accommodate to the peptides that were separated by different types of LC, such as strong cation exchange (SCX) and hydrophilic interaction liquid chromatography (HILIC) and to reach the RT prediction with R values of ∼0.996 for SCX and ∼0.993 for HILIC, respectively. If a large peptide data set is available for one type of LC, DeepRT can be promoted to DeepRT(+) using transfer learning. Based on a large peptide data set gained from SWATH, DeepRT(+) further elevated the accuracy of RT prediction for peptides in a small data set and enabled a satisfactory prediction upon limited peptides approximating hundreds. Further, DeepRT automatically learns retention-related properties of amino acids under different separation mechanisms, which are well consistent with retention coefficients (Rc) of the amino acids. DeepRT was thus proven to be an improved RT predictor with high flexibility and efficiency. DeepRT is available at https://github.com/horsepurve/DeepRTplus .

Sequence-specific model for predicting peptide collision cross-section values in proteomic ion mobility spectrometry

“…[24][25][26][27] This paved the possibility for wider application of high data density machine learning techniques to address peptide retention time prediction problems. [27][28][29] Clemmer and co-workers led the way in the development of IMS technology for proteomic applications, 30 peptide IMS data collection, and modeling peptide ion mobility. 8,31 Valentine et al…”

Deep learning the collisional cross sections of the peptide universe from a million training samples

“…We reasoned that a combination of very large and consistent data sets acquired by PASEF with state of the art deep learning methods would address both challenges. Due to their inherent flexibility and their ability to scale to large data sets, deep learning methods have proven very successful in genomics 30,31 and more recently in proteomics for the prediction of retention times and fragmentation spectra [32][33][34][35] .…”

A hybrid spectral library combining DIA-MS data and a targeted virtual library substantially deepens the proteome coverage

“…2A-B). However, for iRT prediction and because of large differences in the liquid chromatography conditions adopted in our DIA experiment vs. the earlier experiments upon which the DeepRT was originally trained (Ma et al, 2018), the initial performance of DeepRT was lower than expected ( Supplementary Fig. 2C).…”

“…Before constructing a virtual spectral library, we tested the performance of several deep learning models to predict fragment ion intensities and retention time indices (iRT) for the 415 GPCR peptide precursors from the initial DIA spectral library. Distinct from the aforementioned wholeproteome virtual library approaches, we here used the deep neutral network-based models pDeep (Zhou et al, 2017) to predict fragment ion intensities and DeepRT (Ma et al, 2018) to predict iRT from GPCR peptide sequences (Fig. 1).…”