The Design and Structure Prediction of Protein Oligomers

Abstract: The minimum free energy state of a protein (the native state) is encoded by its amino-acid sequence. Due to the many torsional degrees of freedom (DOF) available to a polypeptide chain, a vast number of conformations is possible. Therefore, to predict the native state of a protein directly from sequence, a computer algorithm must evaluate a large number of possible conformations using accurate scoring functions. Due to their larger size and the presence of extra rigid-body DOF, protein oligomers present additional challenges to structure prediction algorithms. These problems are somewhat alleviated if the simulated system possesses some form of symmetry, which limits the sampling of its configuration space and makes the folding simulation computationally tractable. First we focused on developing a method for the structure prediction of a special class of symmetrical protein oligomers: the coiled coils. The major challenge faced was to come up with a scheme that could discriminate between the native and the multiple alternate oligomeric states. We tested several approaches to predict both native subunit orientation and oligomeric state, and found that the free energy of helix folding may be an important factor in determining oligomer stability. Our most successful prediction approach was able to correctly predict native oligomeric states (and topologies) for 23 out of 33 coiled coils in a benchmark set. The accuracy of our prediction method was further evaluated by examining whether the obtained structural models could be used to determine the structures of crystallized coiled coils using molecular replacement (MR) phasing techniques. To that end, we implemented an automated structure-solving pipeline (CCsolve) that combines MR, model building, and refinement. We found that our de novo coiled-coil models were sufficiently accurate to enable effective structure determination for nearly all of the 24 test cases in our benchmark set. Somewhat inverse to the structure prediction problem is the problem of protein design. Here the goal is to find an amino-acid sequence that is most compatible with a desired backbone conformation. First we focused on the design of higher-order coiled coils and how their innate structural and energetic similarities could be exploited to create conformational switches (i.e. proteins that interconvert between two or more distinct structural states as a response to an external stimulus). To that end we designed a de novo peptide sequence (termed pHios) that switches between a symmetrical pentameric and a new type of hexameric assembly as a function of pH. We then focused on designing a protein-based nanocage that could be disassembled and reassembled in order to enable the encapsulation of various cargo molecules. Such a system would be useful as a drug delivery device, as well as a nanobioreactor for the study of enzyme catalysis in confined space. We used the icosahedral Hepatitis B virus capsid as a scaffold, and introduced an affinity motif that enabled the encapsulation of specifically tagged cargo proteins. We subsequently showed that our designed encapsulation system was able to load significant quantities of guest molecules, and thereby demonstrated its potential in the abovementioned applications.