Expanding the space of protein geometries by computational design of de novo fold families

doi:10.1126/science.abc0881

GSTDTAP > 气候变化

DOI	10.1126/science.abc0881
	Expanding the space of protein geometries by computational design of de novo fold families
	Xingjie Pan; Michael C. Thompson; Yang Zhang; Lin Liu; James S. Fraser; Mark J. S. Kelly; Tanja Kortemme
	2020-08-28
发表期刊	Science
出版年	2020
英文摘要	Protein design typically selects a protein topology and then identifies the geometries (secondary-structure lengths and orientations) that give the most stable structures. A challenge for this approach is that functional sites in natural proteins often adopt nonideal geometries. Pan et al. addressed this issue by exploring the diversity of geometries that can be sampled by a given topology. They developed a computational method called LUCS that systematically samples geometric variation in loop-helix-loop elements and applied it to two different topologies. This method generated families of well-folded proteins that include structures with non-native geometries. The ability to tune protein geometry may enable the custom design of new functions. Science , this issue p. [1132][1] Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (LUCS), that mimics nature’s ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization showed that 17 (38%) of 45 tested LUCS designs encompassing two different structural topologies were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely matched the designs. LUCS greatly expands the designable structure space and offers a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions. [1]: /lookup/doi/10.1126/science.abc0881
领域	气候变化 ; 资源环境
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引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/293235
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	Xingjie Pan,Michael C. Thompson,Yang Zhang,et al. Expanding the space of protein geometries by computational design of de novo fold families[J]. Science,2020.
APA	Xingjie Pan.,Michael C. Thompson.,Yang Zhang.,Lin Liu.,James S. Fraser.,...&Tanja Kortemme.(2020).Expanding the space of protein geometries by computational design of de novo fold families.Science.
MLA	Xingjie Pan,et al."Expanding the space of protein geometries by computational design of de novo fold families".Science (2020).