Combination of Molecular Dynamics Simulations and Machine Learning Reveals Structural Characteristics of Stereochemistry-Specific Interdigitation of Synthetic Monomycoloyl Glycerol Analogs
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Synthetic monomycoloyl glycerol (MMG) analogs possess robust immunostimulatory activity and are investigated as adjuvants for subunit vaccines in preclinical and clinical studies. These synthetic lipids consist of a glycerol moiety attached to a corynomycolic acid. Previous experimental studies have shown that the stereochemistry of the lipid acid moiety affects whether the MMG analogs self-assemble into interdigitated or noninterdigitated structures below the main phase transition temperature ( T m ). In this study, we elucidated possible thermodynamic mechanisms governing the phase behavior of MMG analogs by exploring their conformations, interactions, and dynamics using a combination of machine learning (ML) and molecular dynamics (MD) simulations. We compared two analogs, MMG-1 and MMG-6, which differ only by the stereochemistry of the lipid acid moiety; the former has a configuration different from the natural MMG, and the latter displays a native-like stereochemistry. Three different membrane states were simulated: (1) a noninterdigitated single bilayer, (2) a noninterdigitated double bilayer, and (3) a fully interdigitated double bilayer. Our results indicate that the propensity for interdigitation of the MMG analogs in a bilayer is linked to the degree to which their hydrocarbon chains are ordered and oriented. This study demonstrates how combining MD simulations with ML can enhance the molecular understanding of lipid-based pharmaceutical formulations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".