Highlights of model quality assessment in CASP16

Fadini, A. ORCID: https://orcid.org/0000-0001-5246-9124, Adiyaman, R. ORCID: https://orcid.org/0000-0001-8124-5381, Alhaddad, S. N. ORCID: https://orcid.org/0009-0001-1937-0373, Behzadi, B. ORCID: https://orcid.org/0009-0008-8980-0894, Cheng, J. ORCID: https://orcid.org/0000-0003-0305-2853, Cui, X. ORCID: https://orcid.org/0009-0005-6015-2449, Edmunds, N. S. ORCID: https://orcid.org/0000-0002-7238-6008, Freddolino, L. ORCID: https://orcid.org/0000-0002-5821-4226, Genc, A. G. ORCID: https://orcid.org/0009-0001-7195-9353, Liang, F. ORCID: https://orcid.org/0009-0006-4834-5755, Liu, D. ORCID: https://orcid.org/0009-0003-8512-9005, Liu, J. ORCID: https://orcid.org/0000-0002-7570-8690, Liu, Q. ORCID: https://orcid.org/0009-0004-8207-2629, McGuffin, L. ORCID: https://orcid.org/0000-0003-4501-4767, Neupane, P. ORCID: https://orcid.org/0009-0008-5283-4951, Peng, C. ORCID: https://orcid.org/0009-0006-9735-0278, Shortle, D. R. ORCID: https://orcid.org/0009-0009-4903-5865, Sun, M. ORCID: https://orcid.org/0009-0002-7123-7788, Wang, H. ORCID: https://orcid.org/0009-0000-1896-9408, Wuyun, Q. ORCID: https://orcid.org/0000-0002-7228-903X, Zhang, G. ORCID: https://orcid.org/0000-0002-7815-5884, Zhao, X. ORCID: https://orcid.org/0009-0005-0455-9500, Zheng, W. ORCID: https://orcid.org/0000-0002-2984-9003 and Read, R. J. ORCID: https://orcid.org/0000-0001-8273-0047 (2025) Highlights of model quality assessment in CASP16. Proteins: Structure, Function, and Bioinformatics. ISSN 0887-3585 doi: 10.1002/prot.70035

Abstract/Summary

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.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/123993
Identification Number/DOI	10.1002/prot.70035
Refereed	Yes
Divisions	Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR) Life Sciences > School of Biological Sciences > Biomedical Sciences
Publisher	Wiley
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