Enhancing Cryo-EM Density Map Segmentation in Phenix for Improved Atomic Model Building
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915986128502784 |
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| author | Zhang, Chenwei |
| author_facet | Zhang, Chenwei |
| contents | We introduce PhenixCraft, a fully automated pipeline for building atomic models from cryo-EM density maps. By integrating AlphaFold predictions, we enhance the map-segmentation step in Phenix during model building, addressing challenges posed by noise and artifacts that traditionally hinder this step. Our results demonstrate PhenixCraft's superior performance in TM-scores and sequence accuracy, significantly improving upon the limitations and inefficiencies of traditional model building using Phenix. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05259 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Cryo-EM Density Map Segmentation in Phenix for Improved Atomic Model Building Zhang, Chenwei Biomolecules Materials Science Artificial Intelligence Quantitative Methods We introduce PhenixCraft, a fully automated pipeline for building atomic models from cryo-EM density maps. By integrating AlphaFold predictions, we enhance the map-segmentation step in Phenix during model building, addressing challenges posed by noise and artifacts that traditionally hinder this step. Our results demonstrate PhenixCraft's superior performance in TM-scores and sequence accuracy, significantly improving upon the limitations and inefficiencies of traditional model building using Phenix. |
| title | Enhancing Cryo-EM Density Map Segmentation in Phenix for Improved Atomic Model Building |
| topic | Biomolecules Materials Science Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2605.05259 |