AI 中文总结
研究沿基因组的DNA结合蛋白能量景观，核心方法是用含蛋白质结构等的数据集训练机器学习算法，主要贡献是确定转录因子PU.1在染色体上的能量分布，预测结合区域并量化相关统计特性。
AI 中文摘要
重建沿基因组的DNA结合蛋白的能量分布需要一种能有效量化结合自由能的算法。我们组装了一个包含蛋白质结构、DNA结合位点及其结合能的数据集，并用其训练机器学习算法，该算法学习蛋白质界面和DNA序列的潜在不变表示，将它们结合以预测自由能。验证方法后，我们用其确定单域转录因子（PU.1）在哺乳动物染色体上滑动的能量分布，预测其结合区域并量化决定其稳定性和动力学可及性的统计特性。
英文摘要
Reconstructing the energy profile of DNA-binding proteins along the genome requires an algorithm that quantifies efficiently the binding free energy. We assembled a dataset of protein structures and DNA binding sites, together with their binding energies, and used it to train a machine-learning algorithm that learns a latent invariant representation of the protein interface and of the DNA sequence, combining them to predict the free energy. After validating the method, we used it to determine the energy profile of a single-domain transcription factor (PU.1) sliding on mammalian chromosomes, predicting its binding regions and quantifying the statistical properties that determine their stability and their kinetic accessibility.
