论文标题
终结器：使用第三重复基序的基于结构蛋白质设计的神经框架
TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs
论文作者
论文摘要
计算蛋白设计有可能为无数应用提供新颖的分子结构，粘合剂和催化剂。使用骨干坐标衍生功能的最新基于神经图的模型在本机序列恢复任务上显示出非凡的性能，并且是设计的有前途的框架。使用三级基序（术语），蛋白质中重复结构的紧凑单位对蛋白质序列进行建模的统计框架，在蛋白质设计任务上也表现出良好的性能。在这项工作中，我们调查了将术语衍生数据作为神经蛋白设计框架中的特征的使用。我们的基于图形的架构终端结合了基于术语的和基于坐标的信息，并在序列空间上输出了Potts模型。终结器在天然序列恢复任务上的表现优于最先进的模型，这表明使用基于术语和基于坐标的特征一起对蛋白质设计有益。
Computational protein design has the potential to deliver novel molecular structures, binders, and catalysts for myriad applications. Recent neural graph-based models that use backbone coordinate-derived features show exceptional performance on native sequence recovery tasks and are promising frameworks for design. A statistical framework for modeling protein sequence landscapes using Tertiary Motifs (TERMs), compact units of recurring structure in proteins, has also demonstrated good performance on protein design tasks. In this work, we investigate the use of TERM-derived data as features in neural protein design frameworks. Our graph-based architecture, TERMinator, incorporates TERM-based and coordinate-based information and outputs a Potts model over sequence space. TERMinator outperforms state-of-the-art models on native sequence recovery tasks, suggesting that utilizing TERM-based and coordinate-based features together is beneficial for protein design.
