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Original Paper | Open Access | Just Accepted

Evaluation Metrics and Quality Assessment Methods for Predicted Protein Complex Structures

Jiaqi Zhai1Wenda Wang2Yixiang Huang1Xinqi Gong3( )

1 Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China

2 Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China

3 Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China

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Abstract

Accurately evaluating predicted protein structures is crucial for improving protein structure prediction ability. With advancements in computational methods, particularly AlphaFold2, which has revolutionized the prediction of protein monomers, the focus has now shifted to protein quaternary structures. Accurately predicting protein complex structures is becoming increasingly important. Evaluating the quality of predicted protein complex structures has become essential, involving machine learning and other methods. This evaluation can be categorized into two aspects: assessing predicted structures when the native protein structure is known to identify the best option, and guiding the structure prediction process when the native structure is unknown. Despite advancements, assessing protein quaternary structures is still in its early stages and faces many unresolved issues. This review explores common evaluation metrics and various quality assessment methods. At the same time, we highlight existing challenges in predicting protein multimers and outline potential future directions for further development.

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Tsinghua Science and Technology

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Cite this article:
Zhai J, Wang W, Huang Y, et al. Evaluation Metrics and Quality Assessment Methods for Predicted Protein Complex Structures. Tsinghua Science and Technology, 2025, https://doi.org/10.26599/TST.2025.9010068

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Received: 15 September 2024
Revised: 03 November 2024
Accepted: 14 April 2025
Available online: 29 September 2025

© The author(s) 2025

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).