Expanding the space of protein geometries by computational design of de novo fold families
Category
Published on
Type
journal-article
Author
Xingjie Pan and Michael C. Thompson and Yang Zhang and Lin Liu and James S. Fraser and Mark J. S. Kelly and Tanja Kortemme
Citation
Pan, X. et al., 2020. Expanding the space of protein geometries by computational design of de novo fold families. Science, 369(6507), pp.1132–1136. Available at: http://dx.doi.org/10.1126/science.abc0881.
Abstract
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.
