DeepTracer-LowResEnhance: Improving Protein Structure Prediction from Low-Resolution Cryo-EM Maps Using Deep Learning and AlphaFold Simulations
DeepTracer-LowResEnhance is a novel computational tool that significantly improves protein structure prediction from low-resolution cryo-EM maps by integrating deep learning-based map enhancement with AlphaFold structure prediction.
MSAGPT: Enhancing Protein Structure Prediction for Low-Homology Sequences Using Generative Pre-trained Multiple Sequence Alignments
MSAGPT is a novel approach that leverages the power of generative pre-training to create high-quality virtual Multiple Sequence Alignments (MSAs), significantly improving protein structure prediction accuracy, especially for proteins with limited homologous sequence information.
CPE-Pro: A Deep Learning Model Using Structure-Sequence to Determine the Origin of Protein Structures
CPE-Pro is a novel deep learning model that effectively distinguishes between experimentally determined and computationally predicted protein structures by leveraging a "structure-sequence" representation learned from protein structure data.
SGNet: Protein Folding in Symmetrical Complexes with Deep Learning
SGNet proposes a protein folding framework to model symmetrical protein assemblies, addressing challenges in structure determination.
SGNet: Protein Folding Framework with Deep Learning
Deep learning framework SGNet models symmetrical protein complexes effectively, addressing challenges in structure determination.
SGNet: Deep Learning for Symmetrical Protein Complex Folding
Proposing SGNet for modeling symmetrical protein structures, leveraging relative position maps and symmetry generators.
SGNet: Protein Folding with Deep Learning for Symmetrical Complexes
The author proposes SGNet, a protein folding framework leveraging deep learning to model symmetrical protein complexes. By addressing challenges like sequence length and label assignment ambiguity, SGNet demonstrates improved performance in predicting global symmetry types.
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