Computational discovery of potential therapeutic agents against brain-eating amoeba (Naegleria fowleri)
Bibliographic record
Abstract
Naegleria fowleri is a human and animal pathogen well-known for its ability to digest neurons and astrocytes of the host's brain, causing a haemorrhagic and necrotizing inflammation called Primary Amoebic Meningoencephalitis. Although infections are rare, the mortality rate is over 97%, due to both the non-specificity of the symptoms and the absence of an effective treatment. In this work we employed bioinformatics tools to evaluate the possibility of treating the infection with tubulin-targeting compounds, which we regard as the most promising approach given the unclear view on the pathogenic factors in N. fowleri, the divergence of the amoeba's tubulins from the human counterparts, and how well-established microtubule-targeting therapies are in clinical practices. The amoeba's tubulin sequences were analyzed and compared to the human tubulins to conjecture the role of their differences in drugs resistance. The binding affinity of the compounds was computed for both species by performing docking simulations using Chemical Computing Group's MOE and CCSB's AutoDock4 and AutoDock Vina. The results were analyzed using a consensus method to increase their reliability. We found that the amoeba's mitotic tubulins show a significant number of changes that are expected to decrease their affinity for tubulin-targeting compounds. We identified the Colchicine binding site as the most suitable target, and propose that Colchicine analogs retain their ability to bind to the amoeba's tubulins in vivo. The selectivity of the compounds for the pathogen however remains an issue. The changes in the amino. acid sequences in the Colchicine site could create a template for designing novel derivatives with an improved selectivity for the parasite and a safer profile for the patient. We therefore believe that our results could be the starting point for a rational derivatization of the selected ligands, leading to the development of an effective treatment for Naegleria fowleri infection.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".