The manipulation of protein backbone structure to control interaction and function is a challenge for protein engineering. We integrated computational design with experimental selection for grafting the backbone and side chains of a two-segment HIV gp120 epitope, targeted by the cross-neutralizing antibody b12, onto an unrelated scaffold protein. The final scaffolds bound b12 with high specificity and with affinity similar to that of gp120, and crystallographic analysis of a scaffold bound to b12 revealed high structural mimicry of the gp120-b12 complex structure. The method can be generalized to design other functional proteins through backbone grafting.

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Experimental autoimmune encephalomyelitis (EAE), a Th1-mediated inflammatory disease of the central nervous system (CNS), is a model of human multiple sclerosis. Cytosolic phospholipase A2 α (cPLA2 α), which initiates production of prostaglandins, leukotrienes, and platelet-activating factor, is present in EAE lesions. Using myelin oligodendrocyte glycoprotein (MOG) immunization, as well as an adoptive transfer model, we showed that cPLA2 α −/− mice are resistant to EAE. Histologic examination of the CNS from MOG-immunized mice revealed extensive inflammatory lesions in the cPLA2 α +/− mice, whereas the lesions in cPLA2 α −/− mice were reduced greatly or completely absent. MOG-specific T cells generated from WT mice induced less severe EAE in cPLA2 α −/− mice compared with cPLA2 α +/− mice, which indicates that cPLA2 α plays a role in the effector phase of EAE. Additionally, MOG-specific T cells from cPLA2 α −/− mice, transferred into WT mice, induced EAE with delayed onset and lower severity compared with EAE that was induced by control cells; this indicates that cPLA2 α also plays a role in the induction phase of EAE. MOG-specific T cells from cPLA2 α −/− mice were deficient in production of Th1-type cytokines. Consistent with this deficiency, in vivo administration of IL-12 rendered cPLA2 α −/− mice susceptible to EAE. Our data indicate that cPLA2 α plays an important role in EAE development and facilitates differentiation of T cells toward the Th1 phenotype.

Computational grafting of functional motifs onto scaffold proteins is a promising way to engineer novel proteins with pre-specified functionalities. Typically, protein grafting involves the transplantation of protein side chains from a functional motif onto structurally homologous regions of scaffold proteins. Using this approach, we previously transplanted the human immunodeficiency virus 2F5 and 4E10 epitopes onto heterologous proteins to design novel "epitope-scaffold" antigens. However, side-chain grafting is limited by the availability of scaffolds with compatible backbone for a given epitope structure and offers no route to modify backbone structure to improve mimicry or binding affinity. To address this, we report here a new and more aggressive computational method-backbone grafting of linear motifs-that transplants the backbone and side chains of linear functional motifs onto scaffold proteins. To test this method, we first used side-chain grafting to design new 2F5 epitope scaffolds with improved biophysical characteristics. We then independently transplanted the 2F5 epitope onto three of the same parent scaffolds using the newly developed backbone grafting procedure. Crystal structures of side-chain and backbone grafting designs showed close agreement with both the computational models and the desired epitope structure. In two cases, backbone grafting scaffolds bound antibody 2F5 with 30- and 9-fold higher affinity than corresponding side-chain grafting designs. These results demonstrate that flexible backbone methods for epitope grafting can significantly improve binding affinities over those achieved by fixed backbone methods alone. Backbone grafting of linear motifs is a general method to transplant functional motifs when backbone remodeling of the target scaffold is necessary.

Here we generate FRET biosensors for guanine exchange factors (GEFs) by inserting a fluorescent protein pair in a structural “hinge” common to many GEFs. Fluorescent biosensors can map the activation of signaling molecules in space and time, but it has not been possible to quantify how different activation events affect one another or contribute to a specific cell behavior. By imaging the GEF biosensors in the same cells as red-shifted biosensors of Rho GTPases, we can apply partial correlation analysis to parse out the extent to which each GEF contributes to the activation of a specific GTPase in regulating cell movement. Through analysis of spontaneous cell protrusion events we identify when and where the GEF Asef regulates the GTPases Cdc42 and Rac1 to control cell edge dynamics. This approach exemplifies a powerful means to elucidate the real-time connectivity of signal transduction networks.

Rho family GTPases are activated with precise spatiotemporal control by guanine nucleotide exchange factors (GEFs). Guanine exchange factor H1 (GEF-H1), a RhoA activator, is thought to act as an integrator of microtubule (MT) and actin dynamics in diverse cell functions. Here we identify a GEF-H1 autoinhibitory sequence and exploit it to produce an activation biosensor to quantitatively probe the relationship between GEF-H1 conformational change, RhoA activity, and edge motion in migrating cells with micrometer- and second-scale resolution. Simultaneous imaging of MT dynamics and GEF-H1 activity revealed that autoinhibited GEF-H1 is localized to MTs, while MT depolymerization subadjacent to the cell cortex promotes GEF-H1 activation in an ~5-µm-wide peripheral band. GEF-H1 is further regulated by Src phosphorylation, activating GEF-H1 in a narrower band ~0–2 µm from the cell edge, in coordination with cell protrusions. This indicates a synergistic intersection between MT dynamics and Src signaling in RhoA activation through GEF-H1.

Lattice light-sheet microscopy (LLSM) is valuable for its combination of reduced photobleaching and outstanding spatiotemporal resolution in 3D. Using LLSM to image biosensors in living cells could provide unprecedented visualization of rapid, localized changes in protein conformation or posttranslational modification. However, computational manipulations required for biosensor imaging with LLSM are challenging for many software packages. The calculations require processing large amounts of data even for simple changes such as reorientation of cell renderings or testing the effects of user-selectable settings, and lattice imaging poses unique challenges in thresholding and ratio imaging. We describe here a new software package, named ImageTank, that is specifically designed for practical imaging of biosensors using LLSM. To demonstrate its capabilities, we use a new biosensor to study the rapid 3D dynamics of the small GTPase Rap1 in vesicles and cell protrusions.