Atomistic Details of Peptide Reversed-Phase Liquid Chromatography from Molecular Dynamics Simulations
Bibliographic record
Abstract
Peptide separations by reversed-phase liquid chromatography (RPLC) are an integral part of bottom-up proteomics. These separations typically employ C18 columns with water/acetonitrile gradient elution in the presence of formic acid. Despite the widespread use of such workflows, the exact nature of peptide interactions with the stationary and mobile phases is poorly understood. Here, we employ microsecond molecular dynamics (MD) simulations to uncover details of peptide RPLC. We examined two tryptic peptides, a hydrophobic and a hydrophilic species, in a slit pore lined with C18 chains that were grafted onto SiO 2 support. Our simulations explored peptide trapping, followed by desorption and elution. Trapping in an aqueous mobile phase was initiated by C18 contacts with Lys butyl moieties. This was followed by extensive anchoring of nonpolar side chains (Leu/Ile/Val) in the C18 layer. Exposure to water/acetonitrile triggered peptide desorption in a stepwise fashion; charged sites close to the termini were the first to lift off, followed by the other residues. During water/acetonitrile elution, both peptides preferentially resided close to the pore center. The hydrophilic peptide exhibited no contacts with the stationary phase under these conditions. In contrast, the hydrophobic species underwent multiple transient Leu/Ile/Val binding interactions with C18 chains. These nonpolar interactions represent the foundation of differential peptide retention, in agreement with the experimental elution behavior of the two peptides. Extensive peptide/formate ion pairing was observed in water/acetonitrile, particularly at N-terminal sites. Overall, this work uncovers an unprecedented level of RPLC molecular details, paving the way for MD simulations as a future tool for improving retention prediction algorithms and for the design of novel column materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".