Biology subjects

Pilla, S. P.

Publications and source records attributed to Pilla, S. P..

3 recordsLinked to original sources

bioRxiv2026

Hierarchical Breakdown of RNA Structure Prediction in CASP16: From Reliable Local Features to Speculative Multimer Assembly

CASP16 provided a community-wide benchmark for assessing RNA structure prediction, including the first large-scale blind assessment of RNA-RNA multimer prediction. CASP16 results showed that accurate three-dimensional modeling, especially for RNA-RNA multimers, remains a major challenge across the field. In this work, we use the submissions of our group (LCBio) as a diagnostic case study to examine the current limits of RNA structure prediction. In the official CASP16 best-of-submitted-models analysis, our workflow ranked first in the RNA-RNA multimer category and remained competitive for monomers. This makes the submitted model set useful for examining why high-ranking multimer predictions can still deviate substantially from experimental structures. We combine hierarchical analysis with representative case studies to connect this field-wide limitation to specific structural failure modes, showing that prediction accuracy decreases from relatively reliable canonical base-pairing and local helical organization to less reliable non-canonical interactions, stacking geometry, tertiary motifs, and assembly-level features. In RNA-RNA multimers, errors in monomer structure can combine with uncertainty in interface geometry and model selection, reducing the accuracy of the assembled complexes. These findings point to monomer structure accuracy, interface modeling, and model selection as key areas for improving RNA-RNA multimer prediction.

bioinformatics↗

bioRxiv2025

Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16

Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo-EM densities observed around the Tetrahymena ribozyme at 2.2-2.3 [A] resolution, collected through target R1260 in the CASP16 competition. 26 groups participated in this solvation cryo-ensemble prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo-EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo-EM densities using Pearson correlation, Spearman correlation, mutual information, and precision-recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo-EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo-EM maps. We propose that further community-wide blind challenges can drive and evaluate progress in modeling water, ions and other previously hidden components of biomolecular systems.

biophysics↗

bioRxiv2025

When Does Molecular Dynamics Improve RNA Models? Insights from CASP15 and Practical Guidelines

Molecular dynamics (MD) simulations are increasingly applied to refine biomolecular models, yet their practical value in RNA structure prediction remains unclear. Here, we systematically benchmarked the effect of MD on RNA models submitted to the CASP15 community experiment, using Amber with the RNA-specific {chi}OL3 force field. Across 61 models representing diverse targets, we find that short simulations (10-50 ns) can provide modest improvements for high-quality starting models, particularly by stabilizing stacking and non-canonical base pairs. In contrast, poorly predicted models rarely benefit and often deteriorate, regardless of their CASP difficulty class. Longer simulations (>50 ns) typically induced structural drift and reduced fidelity. Based on these findings, we provide practical guidelines for selecting suitable input models, defining optimal simulation lengths, and diagnosing early whether refinement is viable. Overall, MD works best for fine-tuning reliable RNA models and for quickly testing their stability, not as a universal corrective method.

bioinformatics↗