Unnatural Amino Acid and Emerging Chemistry Approaches to Map RNA–Protein Interactions
Bibliographic record
Abstract
RNA serves as both a genetic messenger and a functional non-coding molecule, with its activity relying on interactions with diverse proteins. Characterizing RNA-protein interactions remains challenging, particularly for dynamic or low-abundance complexes. Traditional crosslinking methods, such as UV-254 nm irradiation and formaldehyde fixation, suffer from low efficiency, poor specificity, and broad reactivity, limiting their utility for high-resolution interactome mapping. Recent advances in genetic code expansion (GCE) and unnatural amino acid (UAA) incorporation now enable chemoselective, site-specific crosslinking chemistries that circumvent these limitations. This review highlights classes of crosslinkable UAAs, including benzophenone-, diazirine-, and aryl azide-based moieties, which have been used for photo-induced covalent capture of RNA-protein interactions. Next-generation modalities, such as acetophenone derivatives, halogenated UAAs, bifunctional UAAs, acyl silanes, diaryl nitrones, and photoactivatable systems further expand the toolkit for photocrosslinking and bioorthogonal labelling. Beyond UV-based approaches, latent bioreactive UAAs and proximity-induced chemistries exploit electrophile-nucleophile substitution and masked acylating agents to enable covalent capture under physiological conditions. Together, these strategies provide unprecedented control over reactivity, temporal resolution and site specificity, advancing RNA-targeted proteomics. Continued innovation in UAA-based chemistry promise to transform how RNA-protein complexes are interrogated and manipulated with molecular precision.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".