Water-mediated ribonucleotide–amino acid pairs and higher-order structures at the RNA–protein interface: analysis of the crystal structure database and a topological classification
Bibliographic record
Abstract
Abstract Water is essential for the formation, stability and function of RNA–protein complexes. To delineate the structural role of water molecules in shaping the interactions between RNA and proteins, we comprehensively analyzed a dataset of 329 crystal structures of these complexes to identify water-mediated hydrogen-bonded contacts at RNA–protein interface. Our survey identified a total of 4963 water bridges. We then employed a graph theory-based approach to present a robust classification scheme, encompassing triplets, quartets and quintet bridging topologies, each further delineated into sub-topologies. The frequency of water bridges within each topology decreases with the increasing degree of water node, with simple triplet water bridges outnumbering the higher-order topologies. Overall, this analysis demonstrates the variety of water-mediated interactions and highlights the importance of water as not only the medium but also the organizing principle underlying biomolecular interactions. Further, our study emphasizes the functional significance of water-mediated interactions in RNA–protein complexes, and paving the way for exploring how these interactions operate in complex biological environments. Altogether, this understanding not only enhances insights into biomolecular dynamics but also informs the rational design of RNA–protein complexes, providing a framework for potential applications in biotechnology and therapeutics. All the scripts, and data are available at https://github.com/PSCPU/waterbridges.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".