ReviewCurrent opinion in structural biology2025
Navigating protein-nucleic acid sequence-structure landscapes with deep learning.
Review in Current opinion in structural biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Black-box data: a new paradigm for biomedicine in the AI era.Chemical science · 2026Review
- PARSEbp: pairwise agreement-based RNA scoring with emphasis on base pairings.Bioinformatics advances · 2026Article
- PARSEbp: Pairwise Agreement-based RNA Scoring with Emphasis on Base Pairings.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A few years after AlphaFold revolutionised the field of protein structure prediction, the new frontiers and limitations in structural biology have become clearer. Predicting protein-nucleic acid interactions currently stands as one of the major unresolved challenges in the field. This knowledge gap stems from the scarcity and limited diversity of experimental data, as well as the unique geometric, physicochemical, and evolutionary properties of nucleic acids. Despite these challenges, innovative ideas and promising methodological developments have emerged for both predicting protein-nucleic acid complex structures and designing nucleic acids capable of binding to specific protein conformations. This review presents these recent advances and discusses promising avenues, including the integration of high-throughput profiling data, the development of more rigourous and richer evaluation benchmarks, and the discovery of biologically meaningful regulatory and structural signals using self-supervised learning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.