MassIVE MSV000095965

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Peptide Retention Time Prediction for Electrostatic Repulsion-Hydrophilic Interaction Chromatography

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Description

Electrostatic Repulsion-Hydrophilic Interaction Chromatography (ERLIC) is a legacy separation tools developed by Dr. Andrew Alpert and has been used for developing unique separation methods of hydrophilic compounds, including peptides. We employed a proteomics-derived ~170,000 peptide retention dataset to evaluate major ERLIC retention features using the framework of our Sequence-Specific Retention Calculator model. Separation conditions were adjusted to obtain a wider proteome coverage, particularly for non-modified peptides, resulting in a superior separation orthogonality for a 2D LC combination with reversed-phase C18 LC-MS in the second dimension. The SSRCalc ERLIC model coefficients elucidated known ERLIC retention mechanisms, reflecting a dependence on peptide orientation and the position of charged and hydrophilic residues across the peptide backbone. R2 values of 0.935 and 0.955 accuracy were demonstrated for the standard interpretable SSRCalc model and a GRU-based machine learning algorithm, respectively. The effects of various PTMs on peptide retention were evaluated in this study, covering spontaneous (oxidation, deamidation) and enzymatic (N-terminal acetylation, phosphorylation, glycosylation) modifications. [doi:10.25345/C5D50G867] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: retention prediction models ; ERLIC

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Principal Investigators:
(in alphabetical order) 		Oleg Krokhin, University of Manitoba, Canada
Submitting User: vspicer1
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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct unmodified peptide sequences reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Total number of peptide-spectrum matches (i.e. spectrum identifications) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
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