A comprehensive review of methods to study lncRNA–protein interactions in solution
Bibliographic record
Abstract
The long non-coding RNAs (lncRNAs) other than rRNA and tRNA were earlier assumed to be 'junk genomic material'. However, recent advancements in genomics methods have highlighted their roles not only in housekeeping but also in the progression of diseases like cancer as well as viral infections. lncRNAs owing to their length, have both short-range and long-range interactions resulting in complex folded structures that recruit various biomolecules enabling lncRNAs to undertake their various biological functions. Using cell lysate pull-down assays increasing number of lnRNAs-interacting proteins are being identified. These interactions can be further exploited to develop targeted novel therapeutic strategies to inhibit lncRNA-protein interactions. This review attempts to succinctly techniques that can identify and characterize the lnRNAs-protein interactions (i.e. affinity, stoichiometry, and thermodynamics). Furthermore, using other sophisticated biophysical techniques, one can also perform size estimations, and determine low-resolution structures. Since these methods study the biomolecules in solution, large-scale structural observations can be performed in real-time. This review attempts to briefly introduce the readers to biochemical and biophysical techniques, such that they can utilize these methods to obtain a holistic characterization of the biomolecules of interest. Additionally, it should be noted that the use of these methods is not limited to the characterization of the interacting molecules but can also be used to determine the efficacy of the therapeutic molecules to disrupt these interactions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".