Template-based RNA structure prediction advanced through a blind code competition

Automatically predicting RNA 3D structure from sequence remains an unsolved challenge in biology and biotechnology. Here, we describe a Kaggle code competition engaging over 1700 teams and 43 previously unreleased structures to tackle this challenge. The top three submitted algorithms achieved scores within statistical error of the winners of the recent CASP16 competition. Unexpectedly, the top Kaggle strategy involved a pipeline for discovering 3D templates, without the use of deep learning. We integrated this template-modeling pipeline and other Kaggle strategies to develop a single model RNAPro that retrospectively outperformed individual Kaggle models on the same test set. These results suggest a growing importance of template-based modeling in RNA structure prediction.

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Publication Details

Journal
Maryland Shared Open Access Repository (USMAI Consortium)
Published
2025-12-30
DOI
https://doi.org/10.13016/m2tfgm-gxyx
Primary Topic
RNA and protein synthesis mechanisms
Type
article
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article

Template-based RNA structure prediction advanced through a blind code competition

Anna Lewicka, Boris Rudolfs, Danny Kowerko, Alejo Paullier et al.
Maryland Shared Open Access Repository (USMAI Consortium)
RNA and protein synthesis mechanisms
article

Template-based RNA structure prediction advanced through a blind code competition

Anna Lewicka, Boris Rudolfs, Danny Kowerko, Alejo Paullier, Toshiyuki Oda, Cher Keng Heng, Deepak Koirala, Shi‐Jie Chen, Bin Zhao, Shun Kuraishi, Atsushi Yoshizawa, John Moult, Arunodhayan Sampathkumar, Raul Enrique Gomez, Tatsuya Hirano, Tsu-An Hsu, Christian Munley, Walter Reade, Theo Viel, Huey-Long Chen, Raehyun Kim, Yehyun Kim, Eric Westhof, Yibao Hong, Sicheng Zhang, Jian Zou, Zhaoming Su, Chaitanya K. Joshi, Shuxian Zou, Anna-Lena Steckelberg, Shekhar Jadhav, Hyunjin Kim, Alissa M. Hummer, Liu Wang, Hoa Nguyen, Andrew Favor, Shanshan Li, Shujun He, Andriy Kryshtafovych, Jeanine G. Gezelle, Kenji Ogawa, Joseph Piccirilli, Meng Chen, G. John Rao, Zi Yang, Zhichao Miao, Youhan Lee, David Baker, Haowei Li, Wah Chiu, Rhiju Das, Rachael C. Kretsch, Maggie Demkin, Navtej Toor, Yoshita Srivastava, Kaiming Zhang, Marco Marcia, Daniel Haack
article en

Abstract

Automatically predicting RNA 3D structure from sequence remains an unsolved challenge in biology and biotechnology. Here, we describe a Kaggle code competition engaging over 1700 teams and 43 previously unreleased structures to tackle this challenge. The top three submitted algorithms achieved scores within statistical error of the winners of the recent CASP16 competition. Unexpectedly, the top Kaggle strategy involved a pipeline for discovering 3D templates, without the use of deep learning. We integrated this template-modeling pipeline and other Kaggle strategies to develop a single model RNAPro that retrospectively outperformed individual Kaggle models on the same test set. These results suggest a growing importance of template-based modeling in RNA structure prediction.

Maryland Shared Open Access Repository (USMAI Consortium)
Openalex Percentile: Top 34%
RNA and protein synthesis mechanisms
DOIOpen Access PDFOpenAlex
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