Comparative Analysis of Protein Hydration from MD simulations with Additive and Polarizable Force Fields
Bibliographic record
Abstract
Abstract Recent development of the Drude polarizable (Drude) force field (FF), based on the extension of an induced dipole model, has reached a milestone in the past few years providing a complete set of polarizable parameters for proteins, water, ions, and many lipid types. This FF enables stable simulations up to microseconds, surpassing the capability of other polarizable FFs. The quality of the Drude FF, however, has remained largely untested for modeling the secondary structures of small peptides in explicit solvents compared with classical non‐polarizable FFs. It is critical to benchmark the complex and mutually dependent dynamics of hydrogen‐bond (H‐bond) networks formed by water–water, protein–water, and protein–protein interactions that are expected to have a major impact on the stability of protein structures and their conformational space. Here, a direct comparison is presented between the current Drude FF and the CHARMM‐36 non‐polarizable classical FF for 1) the solvation free energy of mimetics for all amino acid side‐chain equivalents, 2) limited conformational space, 3) protein–water and protein–protein interactions, and 4) the comparative lifetimes of H‐bonds. The impact of counterions on the stabilization of secondary structure in model peptides is additionally discussed and compared between these FFs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".