Expanding the space of protein geometries by computational design of de novo fold families.
Expanding the space of protein geometries by computational design of de novo fold families.
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DOI：
10.1126/science.abc0881
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发表时间：
2020-08-28
期刊：
影响因子：
--
通讯作者：
Kortemme T
中科院分区：
文献类型：
--
作者：
Pan X;Thompson MC;Zhang Y;Liu L;Fraser JS;Kelly MJS;Kortemme T
Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. Here we present a computational design method termed 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 shows that 17 (38%) out 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 match the designs. LUCS greatly expands the designable structure space and provides a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions. A computational method to systematically sample loop-helix-loop geometries expands the structure space of designer proteins.
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发表时间：
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期刊：
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