Chen, X.; Liu, J.; Park, N.; Cheng, J. A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models. Biomolecules2024, 14, 574.
Chen, X.; Liu, J.; Park, N.; Cheng, J. A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models. Biomolecules 2024, 14, 574.
Chen, X.; Liu, J.; Park, N.; Cheng, J. A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models. Biomolecules2024, 14, 574.
Chen, X.; Liu, J.; Park, N.; Cheng, J. A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models. Biomolecules 2024, 14, 574.
Abstract
The quality prediction of quaternary structure models of a protein complex, in the absence
of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for
ranking predicted protein complex structures and using them appropriately in biomedical research,
such as protein-protein interaction studies, protein design, and drug discovery. With the advent
of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and
ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing
attention. Many deep learning methods have been developed to tackle this problem; however,
there is a noticeable absence of a comprehensive overview of these methods to facilitate future
development. Addressing this gap, we present a review of deep learning EMA methods for protein
complex structures developed in the past several years, analyzing their methodologies and impacts.
We also provide a prospective summary of some potential new developments for further improving
the accuracy of the EMA methods.
Keywords
Protein quality assessment; estimation of model accuracy; deep learning; protein complex; protein quaternary structure
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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