Retention Time Prediction for TMT-Labeled Peptides in Proteomic LC-MS Experiments

Toward an Ultimate Solution for Peptide Retention Time Prediction: The Effect of Column Temperature on Separation Selectivity

We studied the effect of the column temperature on the selectivity of reversed-phase peptide separation in bottom-up proteomics. The number of peptide identifications from 2 h liquid chromatography with tandem mass spectrometry (LC-MS/MS) acquisitions reaches a plateau at 45-55 °C, driven simultaneously by improved separation efficiency, a gradual decrease in peptide retention, and possible on-column degradation of peptides at elevated temperatures. Performing 2D LC-MS/MS acquisitions at 25, 35, 45, and 55 °C resulted in the identification of ∼100,000 and ∼120,000 unique peptides for nonmodified and tandem mass tags (TMT)-labeled samples, respectively. These peptide collections were used to investigate the temperature-driven retention features. The latter is governed by the specific temperature response of individual residues, peptide hydrophobicity and length, and amphipathic helicity. On average, peptide retention decreased by 0.56 and 0.5% acetonitrile for each 10 °C increase for label-free and TMT-labeled peptides, respectively. This generally linear response of retention shifts allowed the extrapolation of predictive models beyond the studied temperature range. Thus, (trap) column cooling from room temperature to 0 °C will allow the retention of an additional 3% of detectable tryptic peptides. Meanwhile, the application of 90 °C would result in the loss of ∼20% of tryptic peptides that were amenable to MS/MS-based identification.

Cytoplasmic Shotgun Proteomic Points to Key Proteins and Pathways in Temozolomide-Resistant Glioblastoma Multiforme

Temozolomide (TMZ) is the first line of chemotherapy to treat primary brain tumors of the type glioblastoma multiforme (GBM). TMZ resistance (TMZR) is one of the main barriers to successful treatment and is a principal factor in relapse, resulting in a poor median survival of 15 months. The present paper focuses on proteomic analyses of cytosolic fractions from TMZ-resistant (TMZR) LN-18 cells. The experimental workflow includes an easy, cost-effective, and reproducible method to isolate subcellular fraction of cytosolic (CYTO) proteins, mitochondria, and plasma membrane proteins for proteomic studies. For this study, enriched cytoplasmic fractions were analyzed in replicates by nanoflow liquid chromatography tandem high-resolution mass spectrometry (nLC-MS/MS), and proteins identified were quantified using a label-free approach (LFQ). Statistical analysis of control (CTRL) and temozolomide-resistant (TMZR) proteomes revealed proteins that appear to be differentially controlled in the cytoplasm. The functions of these proteins are discussed as well as their roles in other cancers and TMZ resistance in GBM. Key proteins are also described through biological processes related to gene ontology (GO), molecular functions, and cellular components. For protein-protein interactions (PPI), network and pathway involvement analyses have been performed, highlighting the roles of key proteins in the TMZ resistance phenotypes. This study provides a detailed insight into methods of subcellular fractionation for proteomic analysis of TMZ-resistant GBM cells and the potential to apply this approach to future large-scale studies. Several key proteins, protein-protein interactions (PPI), and pathways have been identified, underlying the TMZ resistance phenotype and highlighting the proteins' biological functions.

Compendium of Chromatographic Behavior of Post-translationally and Chemically Modified Peptides in Bottom-Up Proteomic Experiments

We have systematically evaluated the chromatographic behavior of post-translationally/chemically modified peptides using data spanning over 70 of the most relevant modifications. These retention properties were measured for standard bottom-up proteomic settings (fully porous C18 separation media, 0.1% formic acid as ion-pairing modifier) using collections of modified/nonmodified peptide pairs. These pairs were generated by spontaneous degradation, chemical or enzymatic treatment, analysis of synthetic peptides, or the cotranslational incorporation of noncanonical proline analogues. In addition, these measurements were validated using external data acquired for synthetic peptides and enzymatically induced citrullination. Working in units of hydrophobicity index (HI, % ACN) and evaluating the average retention shifts (ΔHI) represent the simplest approach to describe the effect of modifications from a didactic point of view. Plotting HI values for modified (y-axis) vs nonmodified (x-axis) counterparts generates unique slope and intercept values for each modification defined by the chemistry of the modifying moiety: its hydrophobicity, size, pKa of ionizable groups, and position of the altered residue. These composition-dependent correlations can be used for coarse incorporation of PTMs into models for prediction of peptide retention. More accurate predictions would require the development of specific sequence-dependent algorithms to predict ΔHI values.

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications

In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes.