Mechanism of Protein Aggregation Inhibition by Arginine: Blockage of Anionic Side Chains Favors Unproductive Encounter Complexes
Bibliographic record
Abstract
Aggregation refers to the assembly of proteins into nonphysiological higher order structures. While amyloid has been studied extensively, much less is known about amorphous aggregation, a process that interferes with protein expression and storage. Free arginine (Arg + ) is a widely used aggregation inhibitor, but its mechanism remains elusive. Focusing on myoglobin (Mb), we recently applied atomistic molecular dynamics (MD) simulations for gaining detailed insights into amorphous aggregation ( Ng J. Phys. Chem. B 2021, 125, 13099). Building on that approach, the current work for the first time demonstrates that MD simulations can directly elucidate aggregation inhibition mechanisms. Comparative simulations with and without Arg + reproduced the experimental finding that Arg + significantly decreased the Mb aggregation propensity. Our data reveal that, without Arg +, protein–protein encounter complexes readily form salt bridges and hydrophobic contacts, culminating in firmly linked dimeric aggregation nuclei. Arg + promotes the dissociation of encounter complexes. These “unproductive” encounter complexes are favored because Arg + binding to D – and E – lowers the tendency of these anionic residues to form interprotein salt bridges. Side chain blockage is mediated largely by the guanidinium group of Arg +, which binds carboxylates through H-bond-reinforced ionic contacts. Our MD data revealed Arg + self-association into a dynamic quasi-infinite network, but we found no evidence that this self-association is important for protein aggregation inhibition. Instead, aggregation inhibition by Arg + is similar to that mediated by free guanidinium ions. The computational strategy used here should be suitable for the rational design of aggregation inhibitors with enhanced potency.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".