Title: RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs Show Chinese title

Title: RecCrysFormer：通过循环训练运行从三维帕特森图中精炼蛋白质结构预测

Abstract: 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. Show Chinese abstract

Abstract: 确定蛋白质结构在原子水平上仍然是结构生物学中的一个重要挑战。 我们引入了$\texttt{RecCrysFormer}$，这是一种混合模型，利用变换器的优势，旨在将实验方法和机器学习方法整合到从晶体学数据中确定蛋白质结构的过程中。 $\texttt{RecCrysFormer}$利用 Patterson 图并结合已知的标准部分氨基酸残基结构，直接预测电子密度图，这些图通过晶体学精修过程构建详细的原子模型是必不可少的。 $\texttt{RecCrysFormer}$从一种“循环”训练方案中受益，该方案迭代地将晶体学精修结果和之前的训练运行结果作为模板图以额外输入的形式进行整合。 使用基于蛋白质数据银行的合成肽片段的初步数据集， $\texttt{RecCrysFormer}$在结构预测中取得了良好的准确性，并对晶体参数的变化表现出鲁棒性，例如晶胞尺寸和角度。