FoldToken：通过向量量化学习蛋白质语言及其超越
FoldToken: Learning Protein Language via Vector Quantization and Beyond
AI总结：
本文提出FoldToken，通过软条件向量量化将蛋白质序列与结构统一表示为离散符号，形成新蛋白质语言，并构建首个GPT风格模型FoldGPT，在骨架修复和抗体设计任务中取得良好效果。
摘要：
是否存在一种外语能够同时描述蛋白质序列和结构？蛋白质结构由连续的3D点表示，长期以来由于与离散序列的对比建模范式不同而构成挑战。我们引入了FoldTokenizer，将蛋白质序列-结构表示为离散符号。这种创新方法涉及将残基类型和结构投影到离散空间中，并通过重建损失指导信息保留。我们将学习到的离散符号称为FoldToken，FoldToken序列作为一种新的蛋白质语言，将蛋白质序列-结构转化为统一模态。我们将所创建的蛋白质语言应用于通用骨架修复和抗体设计任务，构建了第一个用于序列-结构共生成的GPT风格模型（FoldGPT），并取得了有前景的结果。我们成功的关键在于对向量量化模块——软条件向量量化（SoftCVQ）——的实质性增强。
英文摘要：
Is there a foreign language describing protein sequences and structures simultaneously? Protein structures, represented by continuous 3D points, have long posed a challenge due to the contrasting modeling paradigms of discrete sequences. We introduce \textbf{FoldTokenizer} to represent protein sequence-structure as discrete symbols. This innovative approach involves projecting residue types and structures into a discrete space, guided by a reconstruction loss for information preservation. We refer to the learned discrete symbols as \textbf{FoldToken}, and the sequence of FoldTokens serves as a new protein language, transforming the protein sequence-structure into a unified modality. We apply the created protein language on general backbone inpainting and antibody design tasks, building the first GPT-style model (\textbf{FoldGPT}) for sequence-structure co-generation with promising results. Key to our success is the substantial enhancement of the vector quantization module, Soft Conditional Vector Quantization (\textbf{SoftCVQ}).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
