Toward a Mechanistic Understanding of Ionic Self-Complementary Peptide Self-Assembly: Role of Water Molecules and Ions
Bibliographic record
Abstract
Ionic self-complementary peptides are considered an important class of self-assembling peptides. In particular, RADARADARADARADA (RADA4) is well-known to form a relatively regular nanofiber structure that has been primarily studied in terms of its physicochemical properties, as related to its biomedical applications. However, the molecular level interactions that are involved in promoting the self-assembly of this peptide into nanofibers have not been fully elucidated. Herein, a thermodynamic analysis of the influences of peptide chemistry upon self-assembly is discussed for RADA4, RADA4-K5, and RADA4-S5. The regular nanofiber structure of the assembled peptides makes it a good candidate for isothermal titration calorimetry (ITC) studies for determining the propensity for self-assembly, the critical assembly concentration (CAC), and the role hydration and ion content play in the assembly of these peptides. First, solutions containing only RADA4-K5 did not self-assemble; illustrating even slight alterations in the asymmetric terminal amino acid chemistry affects assembly. The CAC of the remaining self-assembling peptides was between ~0.1 and ~0.15 mM. Interestingly, we found that self-assembly was entropically driven with hydrophobic forces being the main driving force for RADA4 and hydrogen bonding for RADA4-S5. The role of water molecules and counterions in self-assembly was also highlighted: assembly of RADA4 led to desolvation of interfacial surfaces, whereas the net number of water molecules in the assembled complex increased upon RADA4-S5 self-assembly. Moreover, it was found that counterions did not seem to contribute significantly to self-assembly: a result in contrast to current concepts regarding the role of electrostatic interactions in self-assembly of RADA4-like peptides. A molecular level understanding of peptide self-assembly will allow for further engineering of peptides for a vast array of biomedical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".