Publications

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

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

In: Proteins, vol. 94, no. 1, pp. 381–402, 2026, ISSN: 1097-0134.

Abstract | Links | BibTeX | Tags: Student Paper

@article{Kretsch2025,

title = {Blind Prediction of Complex Water and Ion Ensembles Around                    <scp>RNA</scp>                    in                    <scp>CASP16</scp>},

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

doi = {10.1002/prot.70079},

issn = {1097-0134},

year  = {2026},

date = {2026-01-00},

urldate = {2026-01-00},

journal = {Proteins},

volume = {94},

number = {1},

pages = {381--402},

publisher = {Wiley},

abstract = {<jats:title>ABSTRACT</jats:title>

                  <jats:p>

                    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

                    <jats:italic>Tetrahymena</jats:italic>

                    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.

                  </jats:p>},

keywords = {Student Paper},

pubstate = {published},

tppubtype = {article}

}

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Hoek, Pieter H. W.; Rosa, Angelo; Everaers, Ralf

Configurational entropy of random trees Journal Article

In: Phys. Rev. E, vol. 112, iss. 6, pp. 065405, 2025.

Links | BibTeX | Tags: Student Paper

Posani, Elisa; Janoš, Pavel; Haack, Daniel; Toor, Navtej; Bonomi, Massimiliano; Magistrato, Alessandra; Bussi, Giovanni

Ensemble refinement of mismodeled cryo-EM RNA structures using all-atom simulations Journal Article

In: Nat Commun, vol. 16, no. 1, 2025, ISSN: 2041-1723.

Links | BibTeX | Tags: Student Paper

Marcato, Davide; Giacometti, Achille; Maritan, Amos; Rosa, Angelo

Entropy of self-avoiding branching polymers: Mean-field theory and Monte Carlo simulations Journal Article

In: The Journal of Chemical Physics, vol. 163, no. 14, pp. 141101, 2025, ISBN: 0021-9606.

Abstract | Links | BibTeX | Tags: Student Paper

Zhang, Alex Chen Yi; Rosa, Angelo; Sanguinetti, Guido

bioSBM: A Random Graph Model to Integrate Epigenomic Data in Chromatin Structure Prediction Journal Article

In: PRX Life, vol. 3, iss. 4, pp. 043006, 2025.

Links | BibTeX | Tags: Student Paper

Mizrahi, Orel; Corley, Meredith; Feldman, Ori; Fröhlking, Thorben; Sun, Lei; Ziesel, Alison; Antczak, Maciej; Bernetti, Mattia; Elhajjajy, Shaimae I.; Huang, Wenze; Nguyen, Grady G.; Park, Samuel S.; Martell, Raul I. Perez; Trinity, Luke; Xu, Kui; Zok, Tomasz; Bussi, Giovanni; Jabbari, Hosna; Orenstein, Yaron; Aviran, Sharon; Meyer, Michelle M.; Yeo, Gene W.

Evaluation of novel computational methods to identify RNA-binding protein footprints from structural data Journal Article

In: RNA, vol. 31, no. 8, pp. 1103–1124, 2025, ISSN: 1469-9001.

Abstract | Links | BibTeX | Tags: Student Paper

@article{Mizrahi2025,

title = {Evaluation of novel computational methods to identify RNA-binding protein footprints from structural data},

author = {Orel Mizrahi and Meredith Corley and Ori Feldman and Thorben Fröhlking and Lei Sun and Alison Ziesel and Maciej Antczak and Mattia Bernetti and Shaimae I. Elhajjajy and Wenze Huang and Grady G. Nguyen and Samuel S. Park and Raul I. Perez Martell and Luke Trinity and Kui Xu and Tomasz Zok and Giovanni Bussi and Hosna Jabbari and Yaron Orenstein and Sharon Aviran and Michelle M. Meyer and Gene W. Yeo},

doi = {10.1261/rna.080215.124},

issn = {1469-9001},

year  = {2025},

date = {2025-08-00},

journal = {RNA},

volume = {31},

number = {8},

pages = {1103--1124},

publisher = {Cold Spring Harbor Laboratory},

abstract = {RNA-binding proteins (RBP) play diverse roles in mRNA processing and function. However, from thousands of RBPs encoded in the human genome, a detailed molecular understanding of their interactions with RNA is available only for a small fraction. In most cases, our knowledge of the combination of RNA sequence and structure required for specific RBP binding is insufficient for accurately predicting binding sites transcriptome-wide. In this context, the rapidly expanding collection of transcriptomic data sets that map distinct, yet intertwined posttranscriptional marks, such as RNA structure and RBP binding, presents an opportunity for integrative analysis to better characterize RBP binding. A grand challenge faced by our community is that relatively little information on the secondary structure context within and near RBP-binding sites has been gleaned from integrating such data sets, partially due to lack of suitable computational methods. To engage scientists from diverse backgrounds in addressing this gap, the RNA Society organized the RBP Footprint Grand Challenge in 2021, an international community effort to develop new methods or leverage existing ones for predicting RBP-binding sites through analysis of a growing volume of sequence, structure, and binding data and to experimentally validate select predictions. Here, we report the initiative, analyses, and methods developed by the participants, validation results, and five new in vivo binding data sets generated for validation. We hope our work will inspire additional innovation in computational methods, further utilization of available data resources, and future endeavors to engage the community in collaborating toward closing other critical data-analysis gaps.},

keywords = {Student Paper},

pubstate = {published},

tppubtype = {article}

}

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Müller-Hermes, Christoph; Piomponi, Valerio; Hilber, Stefan; Asami, Sam; Kreutz, Christoph; Bussi, Giovanni; Sattler, Michael

Unique conformational dynamics and protein recognition of A-to-I hyper-edited dsRNA Journal Article

In: vol. 53, no. 12, 2025, ISSN: 1362-4962.

Abstract | Links | BibTeX | Tags: Student Paper

@article{Müller-Hermes2025,

title = {Unique conformational dynamics and protein recognition of A-to-I hyper-edited dsRNA},

author = {Christoph Müller-Hermes and Valerio Piomponi and Stefan Hilber and Sam Asami and Christoph Kreutz and Giovanni Bussi and Michael Sattler},

doi = {10.1093/nar/gkaf550},

issn = {1362-4962},

year  = {2025},

date = {2025-06-20},

volume = {53},

number = {12},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               Adenosine-to-inosine (A-to-I) editing is a highly abundant modification of double-stranded RNA (dsRNA) and plays an important role in posttranscriptional gene regulation. Editing of multiple inosines by the ADAR1 enzyme leads to A-to-I hyper-editing of non-coding dsRNA, such as 3′UTRs, transposable elements, or foreign pathogenic RNAs, and is implicated in immune response and human diseases including cancer. The structural consequences of hyper-editing and its role in protein binding are poorly understood. Here, we combine solution nuclear magnetic resonance spectroscopy (NMR), biophysical methods such as small-angle X-ray scattering, and molecular dynamics simulations to study the sequence-dependent effects on conformation and dynamics of A-to-I hyper-editing for a 20-mer dsRNA and recognition of such RNAs by Endonuclease V. By comparing non-edited, single-edited, and hyper-edited dsRNA, we identify unique conformational features and extensive dynamics associated with hyper-editing, resulting in significantly increased base-pair opening. Hyper-edited dsRNA is more extended and adopts a highly dynamic ensemble of canonical and non-canonical conformations, which lead to preferential binding by Endonuclease V. Our integrated experimental and computational analysis identifies unique structural and dynamic features that are likely linked to specific protein recognition and the unique biological consequences of hyper-editing.},

keywords = {Student Paper},

pubstate = {published},

tppubtype = {article}

}