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A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models.
Chen, Xiao; Liu, Jian; Park, Nolan; Cheng, Jianlin.
Affiliation
  • Chen X; Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.
  • Liu J; Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.
  • Park N; NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA.
  • Cheng J; Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.
Biomolecules ; 14(5)2024 May 13.
Article in En | MEDLINE | ID: mdl-38785981
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, data and feature construction. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Proteins / Protein Structure, Quaternary / Deep Learning Limits: Humans Language: En Journal: Biomolecules Year: 2024 Document type: Article Affiliation country: United States Country of publication: Switzerland

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Proteins / Protein Structure, Quaternary / Deep Learning Limits: Humans Language: En Journal: Biomolecules Year: 2024 Document type: Article Affiliation country: United States Country of publication: Switzerland