Blind Prediction of Complex Water and Ion Ensembles Around <scp>RNA</scp> in <scp>CASP16</scp>

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 Å resolution, collected through target R1260 in the CASP16 competition. Twenty-six 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.

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Journal
Proteins Structure Function and Bioinformatics
Published
2025-11-08
DOI
https://doi.org/10.1002/prot.70079
Citations
1
Primary Topic
RNA and protein synthesis mechanisms
Type
article
Field-Weighted Citation Impact
0.81

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article

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

Janusz M. Bujnicki, David Huang, Giovanni Bussi, Tsukasa Nakamura et al.
1 citations
Proteins Structure Function and Bioinformatics
RNA and protein synthesis mechanisms
0.81
article

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

Janusz M. Bujnicki, David Huang, Giovanni Bussi, Tsukasa Nakamura, Sebastian Kmiecik, Yuki Kagaya, Anika Jain, Ambuj Srivastava, David A. Case, Sicheng Zhang, Thomas E. Cheatham, Michiaki Hamada, Olivia N. Fisher, K. Harini, Elisa Posani, Eugene F. Baulin, Ikuo Kurisaki, Gül H. Zerze, Chandran Nithin, Ayush Gupta, Qiqige Wuyun, Emilia Tugolukova, Joon Hong Park, M. Michael Gromiha, Yuanzhe Zhou, Wah Chiu, Rachael C. Kretsch, Jun Li, Daisuke Kihara, Jacob Verburgt, Michael Z. Palo, Genki Terashi, Shanshan Li, Rhiju Das, Gang Hu, Arne Elofsson, Smita P. Pilla, Wentao Ni, Kengo Sato, Simón Poblete, Olivier Languin–Cattoën, Fabrizio Pucci, Junichi Iwakiri, Masoud Amiri Farsani, Anindya Saha, Wei Zheng, Shi‐Jie Chen, Kaiming Zhang, Sowmya Ramaswamy Krishnan, Karim Malekzadeh, Pranav Punuru
article en
1 citations

Abstract

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 Å resolution, collected through target R1260 in the CASP16 competition. Twenty-six 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.

Proteins Structure Function and BioinformaticsVol. 94(1)
Rutgers, The State University of New Jersey (US), University of Science and Technology of China (CN), Université Libre de Bruxelles (BE), Howard Hughes Medical Institute (US), Scuola Internazionale Superiore di Studi Avanzati (IT), Waseda University (JP), Stockholm University (SE), Nankai University (CN), Purdue University West Lafayette (US), University of Utah (US), Indian Institute of Technology Madras (IN), Science for Life Laboratory (SE), University of Missouri Health System (US), SLAC National Accelerator Laboratory (US), San Sebastián University (CL), Environmental and Occupational Health Sciences Institute (US), Life Science Institute (JP), International Institute of Molecular and Cell Biology (PL), Institute for Advanced Study (DE), University of Houston (US), University of Warsaw (PL), National Institute of Advanced Industrial Science and Technology (JP), The University of Tokyo (JP), University of Missouri (US), The University of Texas at Austin (US), Michigan State University (US), Stanford University (US), Polish Academy of Sciences (PL)
National Science Foundation, Howard Hughes Medical Institute, Cancer Prevention and Research Institute of Texas, Japan Agency for Medical Research and Development, European Commission, National Natural Science Foundation of China, Fundacja na rzecz Nauki Polskiej, Linköpings Universitet, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet, Narodowym Centrum Nauki, Infrastruktura PL-Grid, Agencia Nacional de Investigación y Desarrollo, National Institutes of Health, Stanford Bio-X, Stanford Research Computing Center, Stanford University, European Regional Development Fund, Fundamental Research Funds for the Central Universities, National Supercomputer Centre, Linköpings Universitet, Tianjin Science and Technology Program, Academic Computer Centre Cyfronet, AGH University of Science and Technology, National Institute of General Medical Sciences, Division of Chemical, Bioengineering, Environmental, and Transport Systems, Division of Chemistry, Core Research for Evolutional Science and Technology, SLAC National Accelerator Laboratory
Openalex Percentile: Top 22%
RNA and protein synthesis mechanisms
0.81
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