论文标题
具有全球环境的Novo Novo蛋白质设计
Generative De Novo Protein Design with Global Context
论文作者
论文摘要
氨基酸的线性序列决定蛋白质的结构和功能。蛋白质设计被称为蛋白质结构预测的倒数，旨在获得一种新型的蛋白质序列，该蛋白质序列将折叠成定义的结构。有关计算蛋白设计的最新研究研究了具有局部位置信息并实现竞争性能的所需骨干结构的设计序列。但是，不同主干结构中的类似局部环境可能会导致不同的氨基酸，表明蛋白质结构的全球环境很重要。因此，我们提出了由局部和全球模块组成的全球范围内的新生成蛋白设计方法（GCA）。局部模块专注于邻居氨基酸之间的关系，而全局模块明确捕获了非本地环境。实验结果表明，所提出的GCA方法在从头蛋白质设计方面的最先进。我们的代码和预估计的模型将发布。
The linear sequence of amino acids determines protein structure and function. Protein design, known as the inverse of protein structure prediction, aims to obtain a novel protein sequence that will fold into the defined structure. Recent works on computational protein design have studied designing sequences for the desired backbone structure with local positional information and achieved competitive performance. However, similar local environments in different backbone structures may result in different amino acids, indicating that protein structure's global context matters. Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules. While local modules focus on relationships between neighbor amino acids, global modules explicitly capture non-local contexts. Experimental results demonstrate that the proposed GCA method outperforms state-of-the-arts on de novo protein design. Our code and pretrained model will be released.
