Abstract
Hydrophilic strong anion exchange chromatography (hSAX) is becoming a popular method for the prefractionation of proteomic samples. However, the use and further development of this approach is affected by the limited understanding of its retention mechanism and the absence of elution time prediction. Using a set of 59 297 confidentially identified peptides, we performed an explorative analysis and built a predictive deep learning model. As expected, charged residues are the major contributors to the retention time through electrostatic interactions. Aspartic acid and glutamic acid have a strong retaining effect and lysine and arginine have a strong repulsion effect. In addition, we also find the involvement of aromatic amino acids. This suggests a substantial contribution of cation−π interactions to the retention mechanism. The deep learning approach was validated using 5-fold cross-validation (CV) yielding a mean prediction accuracy of 70% during CV and 68% on a hold-out validation set. The results of this study emphasize that not only electrostatic interactions but rather diverse types of interactions must be integrated to build a reliable hSAX retention time predictor.
Highlights
Mass spectrometry (MS)-based proteomics is the driving technology for the characterization and quantification of complex protein samples.[1−3] With the current advancements in instrumentation and software solutions, the number of peptides and proteins that can be identified in a minimal amount of time have increased dramatically.[4]
Retention times have been predicted for other chromatographic methods including high-pH reverse phase liquid chromatography (RP-LC),[16,17] hydrophilic interaction liquid chromatography (HILIC),[18] and strong cation exchange chromatography (SCX).[19]
The result section is divided into four parts: (1) A general overview is given of the data and how the retention time during prefractionation is influenced by charged amino acids
Summary
Mass spectrometry (MS)-based proteomics is the driving technology for the characterization and quantification of complex protein samples.[1−3] With the current advancements in instrumentation and software solutions, the number of peptides and proteins that can be identified in a minimal amount of time have increased dramatically.[4] deep proteome coverage of higher eukaryotes, mammalian cell lines, or tissue is currently only feasible with extensive fractionation.[5,6] The wide dynamic range of all the expressed proteins in a cell remains a major challenge, leaving the least abundant proteins (and peptides) undiscovered In these cases, online (1D) reverse phase liquid chromatography (RP-LC) does not yield the necessary separation of the proteome. The predictive model and the preprocessing are available in the Python package DePART (https://github.com/Rappsilber-Laboratory/ DePART)
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