WikiSysop: Created page with "== Citation == Ma, X. and Si, D. 2025. Beyond current boundaries: Integrating deep learning and AlphaFold for enhanced protein structure prediction from low-resolution cryo-EM maps. Computational Biology and Chemistry. 119, (2025), 108494. == Abstract == Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and grap..."

2026-01-16T15:37:19Z

Created page with "== Citation == Ma, X. and Si, D. 2025. Beyond current boundaries: Integrating deep learning and AlphaFold for enhanced protein structure prediction from low-resolution cryo-EM maps. Computational Biology and Chemistry. 119, (2025), 108494. == Abstract == Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and grap..."

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== Citation ==

Ma, X. and Si, D. 2025. Beyond current boundaries: Integrating deep learning and AlphaFold for enhanced protein structure prediction from low-resolution cryo-EM maps. Computational Biology and Chemistry. 119, (2025), 108494.

== Abstract ==

Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the development of sophisticated map-to-model tools like DeepTracer and ModelAngelo, their efficacy notably diminishes with low-resolution maps beyond 4 Å. To address this critical gap, this study introduces DeepTracer-LowResEnhance, an innovative computational framework that uniquely integrates structural predictions from AlphaFold with a deep-learning-based map refinement strategy specifically tailored to enhance low-resolution maps. Unlike existing techniques, our approach leverages the strengths of AlphaFold’s sequence-based predictions combined with advanced neural network-driven refinement processes to significantly improve map interpretability and modeling accuracy. DeepTracer-LowResEnhance demonstrates substantial and consistent improvements on an extensive dataset comprising 37 diverse protein cryo-EM maps, covering resolutions from 2.5 to 8.4 Å and including 22 challenging cases below 4 Å resolution. DeepTracer-LowResEnhance achieves an average TM-score improvement of 3.53x compared to baseline DeepTracer predictions. Notably, our enhanced methodology showed performance gains across 95.5% of the tested low-resolution datasets. A comparative analysis alongside traditional sharpening methods such as Phenix’s auto-sharpening illustrates DeepTracer-LowResEnhance’s superior capability in rendering more detailed and precise atomic models, thereby pushing the boundaries of current computational structural biology methodologies.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S1476927125001549

== Related software ==

== Related methods ==

== Comments ==

2025Ma DeepTracer - Revision history