Highlights of Model Quality Assessment in <scp>CASP16</scp>

Model quality assessment (MQA) remains a critical component of structural bioinformatics for both structure predictors and experimentalists seeking to use predictions for downstream applications. In CASP16, the Evaluation of Model Accuracy (EMA) category featured both global and local quality estimation for multimeric assemblies (QMODE1 and QMODE2), as well as a novel QMODE3 challenge-requiring predictors to identify the best five models from thousands generated by MassiveFold. This paper presents detailed results from several leading CASP16 EMA methods, highlighting the strengths and limitations of the approaches.

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Publication Details

Journal
Proteins Structure Function and Bioinformatics
Published
2025-08-14
DOI
https://doi.org/10.1002/prot.70035
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
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article

Highlights of Model Quality Assessment in CASP16

Recep Adiyaman, Alisia Fadini, Jian Liu, Lydia Freddolino et al.
Proteins Structure Function and Bioinformatics
Scientific Computing and Data Management
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Highlights of Model Quality Assessment in CASP16

Recep Adiyaman, Alisia Fadini, Jian Liu, Lydia Freddolino, Ahmet G Genc, Qiqige Wuyun, Jianlin Jack Cheng, Guijun Zhang, H.P. Wang, Chunxiang Peng, Bijan Behzadi, Nicholas S. Edmunds, Liam James McGuffin, Randy J. Read, Quancheng Liu, Xinyue Cui, David Shortle, Wei Zheng, Fang Liang, Sun Meng, Xuanfeng Zhao, Dong Liu, Pawan Neupane, Shaima N Alhaddad
article en

Abstract

Model quality assessment (MQA) remains a critical component of structural bioinformatics for both structure predictors and experimentalists seeking to use predictions for downstream applications. In CASP16, the Evaluation of Model Accuracy (EMA) category featured both global and local quality estimation for multimeric assemblies (QMODE1 and QMODE2), as well as a novel QMODE3 challenge-requiring predictors to identify the best five models from thousands generated by MassiveFold. This paper presents detailed results from several leading CASP16 EMA methods, highlighting the strengths and limitations of the approaches.

Proteins Structure Function and BioinformaticsVol. 94(1)
Johns Hopkins University (US), Nankai University (CN), University of Cambridge (GB), University of Michigan (US), Johns Hopkins Medicine (US), Zhejiang University of Technology (CN), University of Reading (GB), University of Missouri (US), Michigan State University (US)
Openalex Percentile: Top 80%
Scientific Computing and Data Management
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