Research map / Probabilistic and scientific modeling / Molecules and proteins
A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine sequence attention with the cryo-EM module (ablation) | ModelAngelo · Test set | sequence recall | Helped |
| Combine spatial IPA with the cryo-EM module (ablation) | ModelAngelo · Test set | sequence recall | Helped |
| Use both sequence attention and spatial IPA rather than only the Cryo-EM module (ablation) | ModelAngelo · Test set | sequence recall | Helped |
| Prune low-confidence or incorrectly built model regions | ModelAngelo · Test set | sequence recall | No clear effect |
| Use ReLU instead of LeakyReLU (ablation) | Graph initialization network · Cryo-EM map segmentation | performance | No clear effect |
| Use ModelAngelo rather than Deeptracer | Automated model building · 8DTM | sequence recall: 44.9 -> 89.5 | Helped |
Closest papers by meaning
- Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data (NeurIPS 2025 · similarity 0.91)
- cryoSPHERE: Single-Particle HEterogeneous REconstruction from cryo EM (ICLR 2025 · similarity 0.90)
- CryoFM: A Flow-based Foundation Model for Cryo-EM Densities (ICLR 2025 · similarity 0.90)
- Mixture of neural fields for heterogeneous reconstruction in cryo-EM (NeurIPS 2024 · similarity 0.88)
- CryoDomain: Sequence-free Protein Domain Identification from Low-resolution Cryo-EM Density Maps (AAAI 2025 · similarity 0.88)
- CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference (NeurIPS 2024 · similarity 0.87)
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