Automated selection of stabilizing mutations in designed and natural proteins.
The ability to engineer novel protein folds, conformations, and enzymatic activities offers enormous potential for the development of new protein therapeutics and biocatalysts. However, many de novo and redesigned proteins exhibit poor hydrophobic packing in their predicted structures, leading to instability or insolubility. The general utility of rational, structure-based design would greatly benefit from an improved ability to generate well-packed conformations. Here we present an automated protocol within the RosettaDesign framework that can identify and improve poorly packed protein cores by selecting a series of stabilizing point mutations. We apply our method to previously characterized designed proteins that exhibited a decrease in stability after a full computational redesign. We further demonstrate the ability of our method to improve the thermostability of a well-behaved native protein. In each instance, biophysical characterization reveals that we were able to stabilize the original proteins against chemical and thermal denaturation. We believe our method will be a valuable tool for both improving upon designed proteins and conferring increased stability upon native proteins.
Program in Computational and Systems Biology, Washington University in St Louis, St Louis, MO 63110, USA.
Benjamin
B
Borgo
James J
JJ
Havranek
D001331:Automation; D055162:Biocatalysis; D008958:Models, Molecular; D009154:Mutation; D011506:Proteins / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism
D011506:Proteins