用递归训练提升晶体蛋白结构预测精度
RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs
- 结合Patterson图与残基模板，直接预测电子密度图
- 在合成肽片段数据上实现高精度结构预测
- 适合需要高精度结构解析的生物医学研究者
原子级蛋白质结构解析仍是结构生物学的重大挑战。我们提出$ exttt{RecCrysFormer}$，一种融合Transformer优势的混合模型，旨在整合实验与机器学习方法，从晶体学数据中推断蛋白质结构。该模型利用Patterson图，并引入已知的标准氨基酸残基部分结构，直接预测电子密度图，这是通过晶体学精修构建详细原子模型的关键步骤。$ exttt{RecCrysFormer}$采用“递归”训练策略，将晶体学精修结果及前期训练输出作为模板图输入以迭代优化。基于蛋白质数据库中的合成肽片段初步数据集，该模型在结构预测中表现出良好精度，并对晶胞参数（如晶胞尺寸和角度）变化具有鲁棒性。
原文摘要 · Abstract (English)
Determining protein structures at an atomic level remains a significant challenge in structural biology. We introduce $\texttt{RecCrysFormer}$, a hybrid model that exploits the strengths of transformers with the aim of integrating experimental and ML approaches to protein structure determination from crystallographic data. $\texttt{RecCrysFormer}$ leverages Patterson maps and incorporates known standardized partial structures of amino acid residues to directly predict electron density maps, which are essential for constructing detailed atomic models through crystallographic refinement processes. $\texttt{RecCrysFormer}$ benefits from a ``recycling'' training regimen that iteratively incorporates results from crystallographic refinements and previous training runs as additional inputs in the form of template maps. Using a preliminary dataset of synthetic peptide fragments based on Protein Data Bank, $\texttt{RecCrysFormer}$ achieves good accuracy in structural predictions and shows robustness against variations in crystal parameters, such as unit cell dimensions and angles.
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