Going Backward: A Flexible Geometric Approach to Reverse Transformation from Coarse Grained to Atomistic Models
Bibliographic record
Abstract
The conversion of coarse-grained to atomistic models is an important step in obtaining insight about atomistic scale processes from coarse-grained simulations. For this process, called backmapping or reverse transformation, several tools are available, but these commonly require libraries of molecule fragments or they are linked to a specific software package. In addition, the methods are usually restricted to specific molecules and to a specific force field. Here, we present an alternative method, consisting of geometric projection and subsequent force-field based relaxation. This method is designed to be simple and flexible, and offers a generic solution for resolution transformation. For simple systems, the conversion only requires a list of particle correspondences on the two levels of resolution. For special cases, such as nondefault protonation states of amino acids and virtual sites, a target particle list can be specified. The mapping uses simple building blocks, which list the particles on the different levels of resolution. For conversion to higher resolution, the initial model is relaxed with several short cycles of energy minimization and position-restrained MD. The reconstruction of an atomistic backbone from a coarse-grained model is done using a new dedicated algorithm. The method is generic and can be used to map between any two particle based representations, provided that a mapping can be written. The focus of this work is on the coarse-grained MARTINI force field, for which mapping definitions are written to allow conversion to and from the higher-resolution force fields GROMOS, CHARMM, and AMBER, and to and from a simplified three-bead lipid model. Together, these offer the possibility to simulate mesoscopic membrane structures, to be transformed to MARTINI and subsequently to an atomistic model for investigation of detailed interactions. The method was tested on a set of systems ranging from a simple, single-component bilayer to a large protein-membrane-solvent complex. The results demonstrate the efficiency and the efficacy of the new approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".