<i>In Silico</i> Determined Properties of Designed Superoxide Dismutase-1 Mutants Predict ALS-like Phenotypes <i>In Vitro</i> and <i>In Vivo</i>
Bibliographic record
Abstract
Abstract The underlying physical causes of SOD1-related ALS are still not well-understood. We address this problem here by computationally designing two de novo mutants, A89R and K128N, which were predicted theoretically to be either significantly destabilizing or stabilizing respectively. We subjected these in silico designed mutants to a series of experimental tests, including in vitro measures of thermodynamic stability, cell-based aggregation and toxicity assays, and an in vivo developmental model of zebrafish motor neuron axonopathy. The experimental tests validated the theoretical predictions: A89R is an unstable, highly-deleterious mutant, and K128N is a stable, non-toxic mutant. Moreover, K128N is predicted computationally to form an unusually stable heterodimer with the familial ALS mutant A4V. Consistent with this prediction, co-injection of K128N and A4V into zebrafish shows profound rescue of motor neuron pathology. The demonstrated success of these first principles calculations to predict the physical properties of SOD1 mutants holds promise for rationally designed therapies to counter the progression of ALS. Significance Mutations in the protein superoxide dismutase cause ALS, and many of these mutants have decreased folding stability. We sought to pursue this thread using a synthetic biology approach, where we designed two de novo mutations, one stabilizing and one destabilizing, as predicted using computational molecular dynamics simulations. We then tested these mutants using in vitro , cell-based, and in vivo zebrafish models. We found that the unstable mutant was toxic, and induced a severe ALS phenotype in zebrafish; the predicted stable mutant, on the other hand, behaved even better than WT. In fact, it was able to rescue the ALS phenotype caused by mutant SOD1. We propose a mechanism for this rescue, which may provide an avenue for therapeutic intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".