用AI预测提升冷冻电镜模型构建精度
Enhancing Cryo-EM Density Map Segmentation in Phenix for Improved Atomic Model Building
- 融合AlphaFold预测优化密度图分割
- 在TM分数和序列准确率上显著优于传统方法
- 适合需高精度冷冻电镜建模的研究者
我们提出PhenixCraft，一个从冷冻电镜密度图自动构建原子模型的全流程管道。通过集成AlphaFold预测，改进Phenix中模型构建时的密度图分割步骤，有效应对噪声和伪影带来的挑战。实验结果表明，PhenixCraft在TM分数和序列准确率方面均显著优于传统Phenix建模方法，克服了其局限性与低效问题。
原文摘要 · Abstract (English)
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.
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