An Adaptive Geometric Search Algorithm for Macromolecular Scaffold Selection

15 A wide variety of protein and peptidomimetic design tasks require matching 16 functional three-dimensional motifs to potential oligomeric scaffolds. Enzyme 17 design, for example, aims to graft active-site patterns typically consisting of 3 to 18 15 residues onto new protein surfaces. Identifying suitable proteins capable of 19 scaffolding such active-site engraftment requires costly searches to identify protein 20 folds that can provide the correct positioning of side chains to host the desired active 21 site. Other examples of biodesign tasks that require simpler fast exact geometric 22 searches of potential side chain positioning include mimicking binding hotspots, 23 design of metal binding clusters and the design of modular hydrogen binding 24 networks for specificity. In these applications the speed and scaling of geometric 25 search limits downstream design to small patterns. Here we present an adaptive 26 algorithm to searching for side chain take-off angles compatible with an arbitrarily 27 specified functional pattern that enjoys substantive performance improvements 28 over previous methods. We demonstrate this method in both genetically encoded 29 (protein) and synthetic (peptidomimetic) design scenarios. Examples of using 30 this method with the Rosetta framework for protein design are provided but our 31 implementation is compatible with multiple protein design frameworks and is 32 freely available as a set of python scripts (https://github.com/JiangTian/adaptive33 geometric-search-for-protein-design). 34