The Design and Structure Prediction of Protein Oligomers

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

Automated de novo phasing and model building of coiled-coil proteins.

Models generated by de novo structure prediction can be very useful starting points for molecular replacement for systems where suitable structural homologues cannot be readily identified. Protein-protein complexes and de novo-designed proteins are examples of systems that can be challenging to phase. In this study, the potential of de novo models of protein complexes for use as starting points for molecular replacement is investigated. The approach is demonstrated using homomeric coiled-coil proteins, which are excellent model systems for oligomeric systems. Despite the stereotypical fold of coiled coils, initial phase estimation can be difficult and many structures have to be solved with experimental phasing. A method was developed for automatic structure determination of homomeric coiled coils from X-ray diffraction data. In a benchmark set of 24 coiled coils, ranging from dimers to pentamers with resolutions down to 2.5 Å, 22 systems were automatically solved, 11 of which had previously been solved by experimental phasing. The generated models contained 71-103% of the residues present in the deposited structures, had the correct sequence and had free R values that deviated on average by 0.01 from those of the respective reference structures. The electron-density maps were of sufficient quality that only minor manual editing was necessary to produce final structures. The method, named CCsolve, combines methods for de novo structure prediction, initial phase estimation and automated model building into one pipeline. CCsolve is robust against errors in the initial models and can readily be modified to make use of alternative crystallographic software. The results demonstrate the feasibility of de novo phasing of protein-protein complexes, an approach that could also be employed for other small systems beyond coiled coils

A de Novo Designed Coiled-Coil Peptide with a Reversible pH-Induced Oligomerization Switch

Protein conformational switches have many useful applications but are difficult to design rationally. Here we demonstrate how the isoenergetic energy landscape of higher-order coiled coils can enable the formation of an oligomerization switch by insertion of a single destabilizing element into an otherwise stable computationally designed scaffold. We describe a de novo designed peptide that was discovered to switch between a parallel symmetric pentamer at pH 8 and a trimer of antiparallel dimers at pH 6. The transition between pentamer and hexamer is caused by changes in the protonation states of glutamatic acid residues with highly upshifted pKa values in both oligomer forms. The drastic conformational change coupled with the narrow pH range makes the peptide sequence an attractive candidate for introduction of pH sensing into other proteins. The results highlight the remarkable ability of simple-α helices to self-assemble into a vast range of structural states

BioMAX the first macromolecular crystallography beamline at MAX IV Laboratory

BioMAX is the first macromolecular crystallography beamline at the MAX IV Laboratory 3 GeV storage ring, which is the first operational multi-bend achromat storage ring. Due to the low-emittance storage ring, BioMAX has a parallel, high-intensity X-ray beam, even when focused down to 20 μm × 5 μm using the bendable focusing mirrors. The beam is tunable in the energy range 5-25 keV using the in-vacuum undulator and the horizontally deflecting double-crystal monochromator. BioMAX is equipped with an MD3 diffractometer, an ISARA high-capacity sample changer and an EIGER 16M hybrid pixel detector. Data collection at BioMAX is controlled using the newly developed MXCuBE3 graphical user interface, and sample tracking is handled by ISPyB. The computing infrastructure includes data storage and processing both at MAX IV and the Lund University supercomputing center LUNARC. With state-of-the-art instrumentation, a high degree of automation, a user-friendly control system interface and remote operation, BioMAX provides an excellent facility for most macromolecular crystallography experiments. Serial crystallography using either a high-viscosity extruder injector or the MD3 as a fixed-target scanner is already implemented. The serial crystallography activities at MAX IV Laboratory will be further developed at the microfocus beamline MicroMAX, when it comes into operation in 2022. MicroMAX will have a 1 μm × 1 μm beam focus and a flux up to 1015 photons s with main applications in serial crystallography, room-temperature structure determinations and time-resolved experiments