The Formation of RNA Pre-Polymers in the Presence of Different Prebiotic Mineral Surfaces Studied by Molecular Dynamics Simulations
Bibliographic record
Abstract
We used all-atom Molecular Dynamics (MD) computer simulations to study the formation of pre-polymers between the four nucleotides in RNA (AMP, UMP, CMP, GMP) in the presence of different substrates that could have been present in a prebiotic environment. Pre-polymers are C3'-C5' hydrogen-bonded nucleotides that have been suggested to be the precursors of phosphodiester-bonded RNA polymers. We simulated wet-dry cycles by successively removing water molecules from the simulations, from ~60 to 3 water molecules per nucleotide. The nine substrates in this study include three clay minerals, one mica, one phosphate mineral, one silica, and two metal oxides. The substrates differ in their surface charge and ability to form hydrogen bonds with the nucleotides. From the MD simulations, we quantify the interactions between different nucleotides, and between nucleotides and substrates. For comparison, we included graphite as an inert substrate, which is not charged and cannot form hydrogen bonds. We also simulated the dehydration of a nucleotide-only system, which mimics the drying of small droplets. The number of hydrogen bonds between nucleotides and nucleotides and substrates was found to increase significantly when water molecules were removed from the systems. The largest number of C3'-C5' hydrogen bonds between nucleotides occurred in the graphite and nucleotide-only systems. While the surface of the substrates led to an organization and periodic arrangement of the nucleotides, none of the substrates was found to be a catalyst for pre-polymer formation, neither at full hydration, nor when dehydrated. While confinement and dehydration seem to be the main drivers for hydrogen bond formation, substrate interactions reduced the interactions between nucleotides in all cases. Our findings suggest that small supersaturated water droplets that could have been produced by geysers or springs on the primitive Earth may play an important role in non-enzymatic RNA polymerization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".