Effects of flanking regions on DNA i-motif folding and stability
Bibliographic record
Abstract
i-Motifs (iMs) are four-stranded non-canonical nucleic acid secondary structures that are formed by cytosine-rich sequences. Putative iM-forming sequences are concentrated in human promoter and telomeric regions, suggesting possible biological roles. However, many iMs do not readily fold at neutral pH, sparking interest in factors that may stabilize them. We performed a systematic study on how the nucleotides flanking iMs affect their stabilities and folding kinetics. We found that the mere presence of flanking nucleotides led to dramatically slower folding and lower stability compared to isolated iMs. Conversely, complementary flanking nucleotides that comprise an inverted repeat and form a hairpin with the iM in the loop led to greater stability and faster folding than the iM on its own. A bioinformatic analysis of human promoter regions showed that the flanking regions are more likely than average to be complementary to each other, suggesting that this stabilization might be biologically relevant. We analyzed several naturally occurring iM sequences and found that complementary flanking regions substantially stabilized the structures (up to 64-fold faster folding and 17.2°C more stable). Our results show that the regions of DNA flanking iMs are an important and hitherto overlooked factor in iM folding and stability.
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 | 0.001 |
| 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 | 0.000 |
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".