Computational Lipidomics with <i>insane</i>: A Versatile Tool for Generating Custom Membranes for Molecular Simulations
Bibliographic record
Abstract
For simulations of membranes and membrane proteins, the generation of the lipid bilayer is a critical step in the setup of the system. Membranes comprising multiple components pose a particular challenge, because the relative abundances need to be controlled and the equilibration of the system may take several microseconds. Here we present a comprehensive method for building membrane containing systems, characterized by simplicity and versatility. The program uses preset, coarse-grain lipid templates to build the membrane, and also allows on-the-fly generation of simple lipid types by specifying the headgroup, linker, and lipid tails on the command line. The resulting models can be equilibrated, after which a relaxed atomistic model can be obtained by reverse transformation. For multicomponent membranes, this provides an efficient means for generating equilibrated atomistic models. The method is called insane, an acronym for INSert membrANE. The program has been made available, together with the complementary method for reverse transformation, at http://cgmartini.nl/ . This work highlights the key features of insane and presents a survey of properties for a large range of lipids as a start of a computational lipidomics project.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".