论文标题
使用序列发生器和AlphaFold2设计新型蛋白质结构
Designing novel protein structures using sequence generator and AlphaFold2
论文作者
论文摘要
蛋白质结构和功能由氨基酸序列的连续排列确定。设计具有所需几何形状和功能的新型蛋白质序列和结构是具有较大状态空间的复杂任务。在这里，我们开发了一种新型的蛋白质设计管道，该蛋白质设计管道由两种深度学习算法组成，即蛋白素和alphafold2。 Proteinsolver是一个深图神经网络，它会产生氨基酸序列，以便相互作用的氨基酸之间的力与折叠相互兼容，而Alphafold2是一种深度学习算法，可以预测蛋白质序列的蛋白质结构。我们提出了PTP1B和p53蛋白的40个从头设计的结合位点，其精度很高，其中30个蛋白质是新颖的。使用ProteInsolver和Alphafold2结合使用，我们可以修剪大蛋白构象空间的探索，从而扩大找到新颖和多样化的从头蛋白质设计的能力。
Protein structures and functions are determined by a contiguous arrangement of amino acid sequences. Designing novel protein sequences and structures with desired geometry and functions is a complex task with large state spaces. Here we develop a novel protein design pipeline consisting of two deep learning algorithms, ProteinSolver and AlphaFold2. ProteinSolver is a deep graph neural network that generates amino acid sequences such that the forces between interacting amino acids are favorable and compatible with the fold while AlphaFold2 is a deep learning algorithm that predicts the protein structures from protein sequences. We present forty de novo designed binding sites of the PTP1B and P53 proteins with high precision, out of which thirty proteins are novel. Using ProteinSolver and AlphaFold2 in conjunction, we can trim the exploration of the large protein conformation space, thus expanding the ability to find novel and diverse de novo protein designs.
