论文标题
蛋白质结构和序列产生具有deo的扩散概率模型
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models
论文作者
论文摘要
蛋白质是大分子，可介导生命依据的细胞过程的很大一部分。生物工程的一个重要任务是设计具有特定3D结构和化学特性的蛋白质，以实现目标功能。为此，我们引入了一种蛋白质结构和序列的生成模型，该模型可以比以前的分子生成建模方法在明显更大的尺度下运行。该模型完全是从实验数据中学到的，并根据蛋白质拓扑的紧凑规范的生成条件，以产生全部原子的骨干构型以及序列和侧链预测。我们通过对样品的定性和定量分析来证明模型的质量。采样轨迹的视频可在https://nanand2.github.io/proteins上找到。
Proteins are macromolecules that mediate a significant fraction of the cellular processes that underlie life. An important task in bioengineering is designing proteins with specific 3D structures and chemical properties which enable targeted functions. To this end, we introduce a generative model of both protein structure and sequence that can operate at significantly larger scales than previous molecular generative modeling approaches. The model is learned entirely from experimental data and conditions its generation on a compact specification of protein topology to produce a full-atom backbone configuration as well as sequence and side-chain predictions. We demonstrate the quality of the model via qualitative and quantitative analysis of its samples. Videos of sampling trajectories are available at https://nanand2.github.io/proteins .
