PT  - JOURNAL ARTICLE
AU  - Xingjie Pan
AU  - Michael Thompson
AU  - Yang Zhang
AU  - Lin Liu
AU  - James S. Fraser
AU  - Mark J. S. Kelly
AU  - Tanja Kortemme
TI  - Expanding the space of protein geometries by computational design of <em>de novo</em> fold families
AID  - 10.1101/2020.04.14.041772
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.04.14.041772
4099  - http://biorxiv.org/content/early/2020/04/15/2020.04.14.041772.short
4100  - http://biorxiv.org/content/early/2020/04/15/2020.04.14.041772.full
AB  - Naturally occurring proteins use a limited set of fold topologies, but 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 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 customizable for novel functions.One Sentence Summary A computational method to systematically sample loop-helix-loop geometries expands the structure space of designer proteins.Competing Interest StatementThe authors have declared no competing interest.