From static structures to dynamic landscapes: cryo-EM redefines RNA biology

Shekhar Jadhav; Spandan Saha; Qingbin Shang; Marco Marcia

Home/Archive/arXiv

arXiv · 2609.01132

From static structures to dynamic landscapes: cryo-EM redefines RNA biology

Shekhar Jadhav·Spandan Saha·Qingbin Shang·Marco Marcia

View original source ↗Read source document ↗Export metadata ↓

Abstract

RNA molecules perform diverse biological functions by dynamically exploring multiple conformational states rather than adopting a single static structure. Capturing these ensembles is a challenge in molecular biology. Recent advances in cryoEM are now transforming this landscape by enabling the visualization of RNA molecules across a broad spectrum of functionally relevant conformations at near atomic resolution. Here, we examine how cryoEM is reshaping RNA structural biology changing focus from the analysis of static structures to dynamic conformational landscapes. Through 8 representative case studies we illustrate how cryoEM has revealed previously inaccessible mechanisms of RNA motion, including folding processes, ligand-dependent switching, and cooperative assembly. We specifically discuss emerging experimental and computational approaches that address and overcome the challenges associated with studying dynamic RNAs, particularly in construct design, sample preparation, vitrification, and data analysis. These novel methods resolve conformational variability and enable the reconstruction of discrete and continuous RNA conformational landscapes from cryoEM data, highlighting how structural heterogeneity can be harnessed to extract functional insights. Looking forward, the integration of cryoEM with complementary biophysical techniques and time resolved methodologies promises to bridge structural and temporal resolution, to routinely derive experimental molecular movies of RNA in action. These advances will not only deepen our understanding of RNA biology but also provide new opportunities for RNA targeted therapeutics and the rational design of dynamic RNA-based nanodevices. By connecting structural snapshots into coherent dynamic models, cryoEM is establishing a framework for quantitative descriptions of RNA energy landscapes and their functional roles.

Explore related subjects

q-bio.BM

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Shekhar Jadhav, Spandan Saha, Qingbin Shang, Marco Marcia. 2026-09-01. From static structures to dynamic landscapes: cryo-EM redefines RNA biology. https://arxiv.org/abs/2609.01132

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

SaltyMeta: a curated benchmark and protein language model-informed web tool for salty peptide prediction

Excess sodium intake remains a major public health challenge, while salty and saltiness-enhancing peptides offer a potential route to preserve sensory saltiness in reduced-sodium foods. Machine-learning studies of salty peptides, however, are constrained by small datasets, heterogeneous evidence standards, uncertain negative labels, and sequence similarity leakage. Here we present SaltyMeta, a curated benchmark and web-accessible screening framework for salty or saltiness-enhancing short peptides. The benchmark contains 580 peptides, including 280 positive peptides and 300 negative peptides, all standardized to 2-15 residue one-letter amino-acid sequences. Quality control found no non-standard residues, exact duplicates, or positive-negative overlaps. A similarity-grouped split retained 456 peptides for training and 124 for held-out testing. We evaluated 548 interpretable peptide descriptors, frozen ESM2 embeddings at 8M, 35M, and 150M parameter scales, and descriptor-embedding fusion models under grouped cross-validation. The traditional ExtraTrees baseline selected by training-set grouped cross-validation achieved ROC-AUC=0.693 in cross-validation and ROC-AUC=0.704, PR-AUC=0.702, F1=0.626, and MCC=0.304 on the held-out test set. The best initial protein-language-model fusion was traditional descriptors plus ESM2-8M embeddings, with grouped CV ROC-AUC=0.696 and test ROC-AUC=0.700. Advanced optimization using PCA95 dimensionality reduction and ExtraTrees feature-importance filtering yielded a practical ESM2-8M PCA95 top-300 model with test ROC-AUC=0.715 and PR-AUC=0.703, although repeated-CV gains remained modest. SaltyMeta is a transparent prioritization tool, not a sensory validation substitute. We provide benchmark, models, scripts, GitHub, and Streamlit for reproducible screening of food-derived peptides.

q-bio.BM↗

arXiv2026

Synthesizing State-of-the-Art Structure Predictions from Soup of Co-folding Models

Co-folding models have advanced rapidly, yet no single model consistently performs best across all biomolecular complexes. This raises the question of whether independently trained co-folding models encode complementary information that can be transferred across co-folding models. We introduce SoupFold, which improves co-folding predictions by learning simple mappings between the representation spaces of co-folding models. At inference time, SoupFold transfers and incorporates representations from other co-folding models to update the representation used for structure prediction. Importantly, this does not re-train the co-folding models. We evaluate SoupFold on protein-protein and protein-ligand prediction tasks of FoldBench using AlphaFold3, Protenix, ESMFold2, and OpenDDE. By combining their representations, SoupFold achieves state-of-the-art performance on both protein-protein and protein-ligand structure prediction, showing that independently trained co-folding models encode complementary information that can be effectively transferred across models.

q-bio.BM↗

arXiv2026

Exploring Optimal Parameters for Ligand-Based Virtual Screening in Early Drug Discovery

Ligand-based virtual screening depends on choices that are often treated as implementation details, including the similarity threshold, fingerprint setting and atom-invariant scheme. We examined how these choices altered the composition of ranked searches against the Enamine library for four aminergic reference ligands: atomoxetine, bupropion, mirtazapine and venlafaxine. Candidate sets were evaluated by compound-weighted scaffold novelty, normalized Shannon scaffold diversity and three computational estimates of synthetic accessibility. We first identified ligand-specific operating points along cumulative Tanimoto-ranked searches. We then compared five extended-connectivity fingerprint settings at matched retrieval depths and compared ECFP4 with the feature-class analogue FCSFP4. Finally, 240 records, corresponding to 229 unique structures, were scored independently by three chemists who were blinded to the computational scores. No single Tanimoto cutoff described all four searches. ECFP8 provided the most stable pooled setting, although venlafaxine favored ECFP2. ECFP4 was the stronger primary fingerprint in pooled comparisons, whereas FCSFP4 contributed nonredundant chemical space. Agreement among individual chemists was moderate, and the mean rating was more reliable than a single rating. SCScore showed the highest association with the blinded consensus, but performance varied by ligand. These results support a staged screening design in which a pooled default is followed by ligand-specific calibration and expert review.

q-bio.BM↗