Protein structure accuracy estimation using geometry‐complete perceptron networks Article Swipe
Alex Morehead
,
Jian Liu
,
Jianlin Cheng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/pro.4932
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/pro.4932
Estimating the accuracy of protein structural models is a critical task in protein bioinformatics. The need for robust methods in the estimation of protein model accuracy (EMA) is prevalent in the field of protein structure prediction, where computationally‐predicted structures need to be screened rapidly for the reliability of the positions predicted for each of their amino acid residues and their overall quality. Current methods proposed for EMA are either coupled tightly to existing protein structure prediction methods or evaluate protein structures without sufficiently leveraging the rich, geometric information available in such structures to guide accuracy estimation. In this work, we propose a geometric message passing neural network referred to as the geometry‐complete perceptron network for protein structure EMA (GCPNet‐EMA), where we demonstrate through rigorous computational benchmarks that GCPNet‐EMA's accuracy estimations are 47% faster and more than 10% (6%) more correlated with ground‐truth measures of per‐residue (per‐target) structural accuracy compared to baseline state‐of‐the‐art methods for tertiary (multimer) structure EMA including AlphaFold 2. The source code and data for GCPNet‐EMA are available on GitHub, and a public web server implementation is freely available.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/pro.4932
- OA Status
- green
- Cited By
- 8
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392004278
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392004278Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/pro.4932Digital Object Identifier
- Title
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Protein structure accuracy estimation using geometry‐complete perceptron networksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-21Full publication date if available
- Authors
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Alex Morehead, Jian Liu, Jianlin ChengList of authors in order
- Landing page
-
https://doi.org/10.1002/pro.4932Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://pmc.ncbi.nlm.nih.gov/articles/PMC10880424/pdf/PRO-33-e4932.pdfDirect OA link when available
- Concepts
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Protein structure, Protein structure prediction, Computer science, Estimation, Geometry, Algorithm, Artificial intelligence, Pattern recognition (psychology), Physics, Mathematics, Nuclear magnetic resonance, Engineering, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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