ArticleBriefings in bioinformatics2024
Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Plant structural biology: Emerging technologies and future biological insights.The Plant journal : for cell and molecular biology · 2026Review
- Combining Cryo-EM with Computational Approaches To Revolutionize Structural Biology.The protein journal · 2025Review
- Modeling cryo-EM structures in alternative states with AlphaFold2-based models and density-guided simulations.Communications chemistry · 2025Article
- Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints.Communications biology · 2025Article
- Toward Modeling Protein Multimers by Combining AlphaFold 3 Predictions with Secondary Structures from Medium-Resolution Cryo-EM Maps.Computational structural bioinformatics : international workshop, CSBW 2024, Boston, MA, USA, November 16, 2024, proceedings. Computational Structural Bioinformatics Workshop (2024 : Boston, Mass.) · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the challenge, machine learning approaches have been proposed to automate this process. Currently, the majority of the experimental maps in the database lack atomic resolution features, making it challenging for machine learning-based methods to precisely determine protein structures from cryogenic electron microscopy density maps. On the other hand, protein structure prediction methods, such as AlphaFold2, leverage evolutionary information from protein sequences and have recently achieved groundbreaking accuracy. However, these methods often require manual refinement, which is labor intensive and time consuming. In this study, we present DeepTracer-Refine, an automated method that refines AlphaFold predicted structures by aligning them to DeepTracers modeled structure. Our method was evaluated on 39 multi-domain proteins and we improved the average residue coverage from 78.2 to 90.0% and average local Distance Difference Test score from 0.67 to 0.71. We also compared DeepTracer-Refine with Phenixs AlphaFold refinement and demonstrated that our method not only performs better when the initial AlphaFold model is less precise but also surpasses Phenix in run-time performance.
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Registered trials
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