The design of stable, functional proteins is difficult. Improved design requires a deeper knowledge of the molecular basis for design outcomes and properties. We previously used a bioinformatics and energy function method to design a symmetric superfold protein composed of repeating structural elements with multivalent carbohydrate-binding function, called ThreeFoil. This and similar methods have produced a notably high yield of stable proteins. Using a battery of experimental and computational analyses we show that despite its small size and lack of disulfide bonds, ThreeFoil has remarkably high kinetic stability and its folding is specifically chaperoned by carbohydrate binding. It is also extremely stable against thermal and chemical denaturation and proteolytic degradation. We demonstrate that the kinetic stability can be predicted and modeled using absolute contact order (ACO) and long-range order (LRO), as well as coarse-grained simulations; the stability arises from a topology that includes many long-range contacts which create a large and highly cooperative energy barrier for unfolding and folding. Extensive data from proteomic screens and other experiments reveal that a high ACO/ LRO is a general feature of proteins with strong resistances to denaturation and degradation. These results provide tractable approaches for predicting resistance and designing proteins with sufficient topological complexity and long-range interactions to accommodate destabilizing functional features as well as withstand chemical and proteolytic challenge.SDS/protease resistance | protein folding | coarse-grained simulations | protein topology | contact order

One of the main barriers to accurate computational protein structure prediction is searching the vast space of protein conformations. Distance restraints or inter-residue contacts have been used to reduce this search space, easing the discovery of the correct folded state. It has been suggested that about 1 contact for every 12 residues may be sufficient to predict structure at fold level accuracy. Here, we use coarse-grained structure-based models in conjunction with molecular dynamics simulations to examine this empirical prediction. We generate sparse contact maps for 15 proteins of varying sequence lengths and topologies and find that given perfect secondary-structural information, a small fraction of the native contact map (5%-10%) suffices to fold proteins to their correct native states. We also find that different sparse maps are not equivalent and we make several observations about the type of maps that are successful at such structure prediction. Long range contacts are found to encode more information than shorter range ones, especially for α and αβ-proteins. However, this distinction reduces for β-proteins. Choosing contacts that are a consensus from successful maps gives predictive sparse maps as does choosing contacts that are well spread out over the protein structure. Additionally, the folding of proteins can also be used to choose predictive sparse maps. Overall, we conclude that structure-based models can be used to understand the efficacy of structure-prediction restraints and could, in future, be tuned to include specific force-field interactions, secondary structure errors and noise in the sparse maps.