Testing the interaction of HEPN domain of Sacsin with RNA through experimental and computational efforts
Bibliographic record
Abstract
Autosomal recessive spastic ataxia of Charlevoix-Saguenay, or ARSACS, is a neurodegenerative disorder whose symptoms appear in childhood and is caused by point mutations in the SACS gene (13q11), which encodes the Sacsin protein. Sacsin includes a HEPN domain, which shares structural similarity with the HEPN domains of other known RNA-binding proteins. Notably, individuals with a single missense mutation in this domain exhibit symptoms comparable to those of other ARSACS patients, indicating that altered RNA-binding abilities may play a role in the disease’s pathogenesis. We are investigating the RNA-binding properties of the HEPN domain. With the help of bioinformatic tools RNAA and RNAB, were selected based on their predicted higher (A) and lower (B) affinity for the HEPN domain, respectively. Their interaction with HEPN was analyzed via intrinsic fluorescence, circular dichroism (CD), and size-exclusion chromatography (SEC). Here we present the results of these experiments as well as GST-pulldown assays that confirmed the ability of HEPN to selectively bind to RNAs. We also show the modelling efforts to investigate the interaction of HEPN (monomer and dimer) with short RNA sequences. We used Alphafold3 and Nufold to predict the structure of isolated RNA sequences and of HEPN-RNA complexes (Alphafold3 only). Notwithstanding the difficulty in predicting RNA 3D structures with currently available computational tools, we found a suggestion of a specific interaction at the monomer-monomer interface of the HEPN dimer. We also performed Molecular Dynamics simulations of the HEPN, HEPN dimer and of complexes with strongly and weakly interacting RNA sequences. We plan to design a pipeline of machine learning and traditional tools to explore a wider space of RNA sequences to retrieve stronger and more specific RNA binders of HEPN.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".