Innovative interactive flexible docking method for multi-scale reconstruction elucidates dystrophin molecular assembly
Bibliographic record
Abstract
At present, our molecular knowledge of dystrophin, the protein encoded by the DMD gene and mutated in myopathy patients, remains limited. To get around the absence of its atomic structure, we have developed an innovative interactive docking method based on the BioSpring software in combination with Small-angle X-ray Scattering (SAXS) data. BioSpring allows interactive handling of biological macromolecules thanks to an augmented Elastic Network Model (aENM) that combines the spring network with non-bonded terms between atoms or pseudo-atoms. This approach can be used for building molecular assemblies even on a desktop or a laptop computer thanks to code optimizations including parallel computing and GPU programming. By combining atomistic and coarse-grained models, the approach significantly simplifies the set-up of multi-scale scenarios. BioSpring is remarkably efficient for the preparation of numeric simulations or for the design of biomolecular models integrating qualitative experimental data restraints. The combination of this program and SAXS allowed us to propose the first high-resolution models of the filamentous central domain of dystrophin, covering repeats 11 to 17. Low-resolution interactive docking experiments driven by a potential grid enabled us to propose how dystrophin may associate with F-actin and nNOS. This information provides an insight into medically relevant discoveries to come.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".