Bibliographic record
Abstract
Les fibrilles amyloïcles sont associées à pius d'une vingtaine de maladies collec tivement nommées amyloïdoses.Ces structures sont morphologiquement similaires, malgré l'absence de propriété commttne au niveau de la séquence ou de la structure native des protéines qui les composent.La nature des fibrilles a considérablement nui à leur caractérisation.Toutefois, des études ont révélé que les peptides les composant s'organisent selon un motif de feuillets en croix.Aussi, un grand nombre d'observations suggère que la toxicité observée dans les amyloïdoses n'est pas liée aux fibrilles, mais aux oligomères solubles formés plus tôt dans la, fi brillogénèse.Les oligomères sont donc des cibles thérapeutiques potentielles et la compréhension de leurs mécanismes d'agrégation devient primordiale pour lutter contrer les amyloïdoses.Cependant, leur caractérisation expérimentale est com plexe d'où l'attrait de complémenter les efforts expérimentaux en utilisant des méthodes d'analyse in sitico.Par contre, les méthodes numériques classiques ne peuvent être utilisées efficacement pour étudier la formation des oligomères, car leur temps d'agrégation est considérable et hors de la portée de ces méthodes.Des méthodes numériques alternatives doivent donc être utilisées.Dans le cadre de ce mémoire, j'ai étudié de façon détaillée les mécanismes d'agrégation de peptides amyloïdes en utilisant la, technique d'activation-relaxation (ART) couplée à un potentiel d'énergie simplifié (OPEP).Cette combinaison a été utilisée avec succès par le passé.J'ai donc utilisé cette méthode pour étudier l'assemblage d'un tétramère du peptide KFfE, petit pepticle capable de former des fibrilles in vitro, et pour étudier la formation d'un tétramère d'un fragment constitué des résidus 11 à 25 du peptide /3-amyloïde, peptide associé à la maladie d'Alzheimer.iVIots-clés amyloïde, agrégation, oligomères, fibrilles, simulation, technique d' activation-relaxation ABSTRACT Insoluble amyloid blinis are found in several diseases such as Alzheimer's di sease.Aithougli the ftbnillae observed in these diseases are histologically similar, the normally soluble proteins implied in their formation do not have, a priori, any common properties.The insoluble polymeric nature of the fibril has limited the amount of high resolution data available.However, X-ray diffraction of the fiber revealed a cross-beta slieet motif within the fibriis.Mounting evidences suggest that the toxicity observed in these diseases is not related to the fibrillae themselves, but to soluble intermediate oligomers formed earlier in the process of fibrillogenesis.It is therefore of great interest to study and understand the mechanisrns of oligomers formation.However, the experimental and detailed charactenization of intermectiate oligomers is compïex.Considening this obstacle, in sitico methods provide an interesting alternative approach that eau complement efficiently experimental efforts.In particular, computer simulations should be able to provide reliable insights on the general properties associated with the aggregation mechanisms.Even for small peptidic chains with an implicit solvent description the aggregation process eau take a time beyond the reach of standard simulation techniques such as molecular dynamics (IVID), and alternative techniques must be used. Using the activation-relaxation technique (ART)and an approximate free energy model (OPEP), we study in details the mechanisms of aggregation of some amy loid and amyloid-like pepticles.ART-OPEP has been tested extensively on a beta liairpin as well as a dimer and trimer of A316_00, producing realistic folding or aggregation trajectories in agreement with experiments anci standard simulations.In this work, we have useci ART-OPEP to stud the aggregation in tetramer of the smail peptide KFFE, the smallest known to form in vitro fibniis that are similar to those observed in the varions diseases, and of n sub-fragment composed of residues 11 to 25 of the bigger -amyloid peptide (associated with Alzheimer's disease).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".