Advanced coarse-grained model for fast simulation of nascent polypeptide chain dynamics within the ribosome
Bibliographic record
Abstract
ABSTRACT The nascent polypeptide exit tunnel (NPET) is a sub-compartment of the ribosome that constrains the dynamics of nascent polypeptide chains during protein translation. Simulating these dynamics has been limited due by the spatial scale of the ribosome and the temporal scale of elongation. Here, we present an automated pipeline to extract the geometry of the NPET and the ribosome surface at high resolution from any ribosome structure. We further convert this into a coarse-grained (CG) bead model that can be used in molecular simulations. This CG model more accurately captures NPET geometry than previous representations and allows for the simulation of co- and post-translational processes that are computationally prohibitive with all-atom approaches. In particular, we illustrate how the CG model may be used to simulate the elongation dynamics of the nascent polypeptide and its escape post-translation, as well as evaluate free energy landscapes and examine the influence of electrostatics on the nascent polypeptide escape. SIGNIFICANCE The translation of nascent polypeptide chains is mediated by the ribosome, with interactions between the protein and the nascent polypeptide exit tunnel (NPET) impacting the process. However, modeling and simulating protein elongation and its escape from the NPET remain challenging due to computational limitations in spatial and temporal resolution. Here, we develop a computational pipeline for generating coarse-grained (CG) models of the NPET and ribosome surface from any ribosome structure that allow for both effective and accurate computer simulations. We demonstrate how the model can be implemented for various simulations, including the elongation dynamics of the nascent polypeptide and escape of the chain post-translation, as well as in estimating free energy landscapes and examining the impact of the charged environment on the escape time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".