Umbrella Sampling MD Simulations for Retention Prediction in Peptide Reversed-phase Liquid Chromatography
Bibliographic record
Abstract
Reversed-phase liquid chromatography (RPLC) is an essential tool for separating complex mixtures such as proteolytic digests in bottom-up proteomics. There is growing interest in methods that can predict the RPLC retention behavior of peptides and other analytes. Already, existing algorithms provide excellent performance based on empirical rules or large sets of RPLC training data. Here we explored a new type of retention prediction strategy that relies on first-principles modeling of peptide interactions with a C18 stationary phase. We recently demonstrated that molecular dynamics (MD) simulations can provide atomistic insights into the behavior of peptides under RPLC conditions ( Anal . Chem . 2023, 95, 3892). However, the current work found that it is problematic to use conventional MD data for retention prediction, evident from a poor correlation between experimental retention times and MD-generated “fraction bound” values. We thus turned to umbrella sampling MD, a complementary technique that has previously been applied to probe noncovalent contacts in other types of systems. By restraining the peptide dynamic motions at various positions inside a C18-lined pore, we determined the free energy of the system as a function of peptide-stationary phase distance. Δ G binding values determined in this way under various mobile phase conditions were linearly correlated with experimental retention times of tryptic test peptides. This work opens retention prediction avenues for novel types of stationary and mobile phases, and for peptides (or other analytes) having arbitrary chemical properties, without the need for RPLC reference data. Umbrella sampling can be used as a stand-alone tool, or it may serve to enhance existing retention prediction algorithms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.002 | 0.000 |
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".