Ovchinnikov Sergey, Park Hahnbeom, Varghese Neha, Huang Po-Ssu, Pavlopoulos Georgios A, Kim David E, Kamisetty Hetunandan, Kyrpides Nikos C, Baker David
Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.
Science. 2017 Jan 20;355(6322):294-298. doi: 10.1126/science.aah4043.
Despite decades of work by structural biologists, there are still ~5200 protein families with unknown structure outside the range of comparative modeling. We show that Rosetta structure prediction guided by residue-residue contacts inferred from evolutionary information can accurately model proteins that belong to large families and that metagenome sequence data more than triple the number of protein families with sufficient sequences for accurate modeling. We then integrate metagenome data, contact-based structure matching, and Rosetta structure calculations to generate models for 614 protein families with currently unknown structures; 206 are membrane proteins and 137 have folds not represented in the Protein Data Bank. This approach provides the representative models for large protein families originally envisioned as the goal of the Protein Structure Initiative at a fraction of the cost.
尽管结构生物学家已经进行了数十年的研究工作,但仍有大约5200个蛋白质家族的结构在比较建模范围之外尚属未知。我们表明,由从进化信息推断出的残基-残基接触所引导的罗塞塔结构预测,能够准确地对属于大型家族的蛋白质进行建模,而且宏基因组序列数据使具有足够序列用于准确建模的蛋白质家族数量增加了两倍多。然后,我们整合宏基因组数据、基于接触的结构匹配和罗塞塔结构计算,为614个目前结构未知的蛋白质家族生成模型;其中206个是膜蛋白,137个具有蛋白质数据库中未呈现的折叠方式。这种方法以一小部分成本为大型蛋白质家族提供了最初被设想为蛋白质结构计划目标的代表性模型。