Porous peptide frameworks generated by stacking non-self-complementary β-sheets
Bibliographic record
Abstract
"One of major approaches in the design of cavity space in the solids utilizes non-self-complementary molecules[1]. The irregular shape of the molecules and/or specific directionality of potential H-bonds prevent close packing of the molecules and yields various architectures hosting a second component, from inclusion compounds and co-crystals to complex non-crystalline patterns in biology. The strategy of non-self-complementary molecules has been extended in our studies to 2D supramolecular polymers based on short peptides[2]. The formation of the peptide layer with a desired overall geometry is controlled by strong, charge-assisted H-bonds (arrows in the Figure) in a β-sheet-like network as well as the segregation of hydrophobic amino acid residues into the interlayer space. The H-bonds add stability to the whole architecture while the hydrophobic groups keep the stacking layers at a distance that generates a cavity space available to a second component (encircled ""G"" in the Figure). A wide range of inclusions and co-crystals have been prepared in our group based on a series of dipeptides and higher peptide oligomers. For example, the incorporation of various organic solvents and bioactive molecules have been demonstrated for leucyl-alanine and similar dipeptides: alcohols, amides, phenols, pyridines, polyols, vitamins, scents and flavors. The crystal structure studies reveal a surprisingly persistent structural motif that can be used for engineering of crystalline materials with a specific property. We believe this type of peptide matrix may be utilized in the solid state organic synthesis [3] as reactive molecules of the second component can be oriented in a predictable way with respect to each other. "
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
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".