美国华盛顿大学David Baker研究小组发现，使用RoseTTAFold序列空间扩散可进行多态和功能性蛋白质设计。该项研究成果于2024年9月25日在线发表在《自然—生物技术》杂志上。

研究人员表示，蛋白质去噪扩散概率模型用于从头生成蛋白质骨架，但在指导生成具有序列特异性属性和功能特性的蛋白质方面存在限制。

为克服这一局限性，研究人员ProteinGenerator（PG），这是一种基于RoseTTAFold的序列空间扩散模型，能够同时生成蛋白质序列和结构。从噪声序列表示开始，PG通过迭代去噪生成序列和结构对，受期望序列和结构属性的引导。研究人员设计了具有不同氨基酸组成和内部序列重复的热稳定蛋白，以及生物活性肽如蜜蜂毒素。

通过在具有不同结构约束的扩散轨迹之间平均序列logits，研究人员设计了多态亲子蛋白三元组，其中相同的序列在完整的亲本中折叠成不同的超次级结构，而在分裂成两个子域时则呈现不同构象。PG设计轨迹可以通过实验序列-活性数据进行引导，提供了一种用于蛋白质功能的综合计算和实验优化的通用方法。

附：英文原文

Title: Multistate and functional protein design using RoseTTAFold sequence space diffusion

Author: Lisanza, Sidney Lyayuga, Gershon, Jacob Merle, Tipps, Samuel W. K., Sims, Jeremiah Nelson, Arnoldt, Lucas, Hendel, Samuel J., Simma, Miriam K., Liu, Ge, Yase, Muna, Wu, Hongwei, Tharp, Claire D., Li, Xinting, Kang, Alex, Brackenbrough, Evans, Bera, Asim K., Gerben, Stacey, Wittmann, Bruce J., McShan, Andrew C., Baker, David

Issue&Volume: 2024-09-25

Abstract: Protein denoising diffusion probabilistic models are used for the de novo generation of protein backbones but are limited in their ability to guide generation of proteins with sequence-specific attributes and functional properties. To overcome this limitation, we developed ProteinGenerator (PG), a sequence space diffusion model based on RoseTTAFold that simultaneously generates protein sequences and structures. Beginning from a noised sequence representation, PG generates sequence and structure pairs by iterative denoising, guided by desired sequence and structural protein attributes. We designed thermostable proteins with varying amino acid compositions and internal sequence repeats and cage bioactive peptides, such as melittin. By averaging sequence logits between diffusion trajectories with distinct structural constraints, we designed multistate parent–child protein triples in which the same sequence folds to different supersecondary structures when intact in the parent versus split into two child domains. PG design trajectories can be guided by experimental sequence–activity data, providing a general approach for integrated computational and experimental optimization of protein function.

DOI: 10.1038/s41587-024-02395-w

Source: https://www.nature.com/articles/s41587-024-02395-w