Conformational Selectivity of Peptides for Single-Walled Carbon Nanotubes
Bibliographic record
Abstract
Carbon nanotubes show promising prospects for applications ranging from molecular electronics to ultrasensitive biosensors. An important aspect to understanding carbon nanotube properties is their interactions with biomolecules such as peptides and proteins, as these interactions are important in our understanding of nanotube interactions with the environment, their use in cellular systems, as well as their interface with biological materials for medical and diagnostic applications. Here we report the sequence and conformational requirements of peptides for high-affinity binding to single-walled carbon nanotubes (SWNTs). A new motif, X(1)THX(2)X(3)PWTX(4), where X(1) is G or H, X(2) is H or D or null, X(3) is null or R, and X4 is null or K, was identified from two classes of phage-displayed peptide libraries. The high affinity binding of the motif to SWNTs required constrained conformations which were achieved through either the extension of the amino acid sequence (e.g., LLADTTHHRPWT) or the addition of a constrained disulfide bond (e.g., CGHPWTKC). This motif shows specific high-affinity to the currently studied SWNTs, compared to previously reported peptides. The conformations of the identified peptides in complex with SWNTs were also characterized with a variety of biophysical methodologies including CD, fluorescence, NMR spectroscopy, and molecular modeling.
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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) | 0.000 | 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 | 0.001 |
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".