Computational Design of Repeat-Proteins with a Predefined Geometry

Abstract

New protein design methods are needed to further improve the development of protein-binding scaffolds. Repeat proteins are linear tandem arrays of structurally similar building blocks, and they are established platforms for engineering proteins inhibitors and biosensors. However, current sequence-based engineering approaches lack the possibility of customizing the overall shape of a binder to its target molecule. Structure-based protein design offers a possibility of optimizing the overall shape of engineered binding scaffolds to better match their targets. We developed a protocol for the computational design of shape-optimized binding scaffolds that can better match their targets. By combining sequence optimization of existing repeats and de novo design of capping structures, we designed leucine-rich repeat (LRR) proteins where the building blocks assemble with a novel geometry. We validated the geometric design approach by engineering an artificial donut-like ring structure constructed from ten self-compatible repeats. Characterization of the design constructs revealed that buried cysteines play a central role for stability and folding cooperativity in certain LRR proteins. This may be used to selectively stabilize or destabilize specific parts of a protein. The computational procedure may now be employed to develop repeat proteins with various geometrical shapes for applications where greater control of the interface geometry is desired.