De novo design of stable proteins that efficaciously inhibit oncogenic G proteins.

Many protein therapeutics are competitive inhibitors that function by binding to endogenous proteins and preventing them from interacting with native partners. One effective strategy for engineering competitive inhibitors is to graft structural motifs from a native partner into a host protein. Here, we develop and experimentally test a computational protocol for embedding binding motifs in de novo designed proteins. The protocol uses an "inside-out" approach: Starting with a structural model of the binding motif docked against the target protein, the de novo protein is built by growing new structural elements off the termini of the binding motif. During backbone assembly, a score function favors backbones that introduce new tertiary contacts within the designed protein and do not introduce clashes with the target binding partner. Final sequences are designed and optimized using the molecular modeling program Rosetta. To test our protocol, we designed small helical proteins to inhibit the interaction between Gαq and its effector PLC-ß isozymes. Several of the designed proteins remain folded above 90°C and bind to Gαq with equilibrium dissociation constants tighter than 80 nM. In cellular assays with oncogenic variants of Gαq , the designed proteins inhibit activation of PLC-ß isozymes and Dbl-family RhoGEFs. Our results demonstrate that computational protein design, in combination with motif grafting, can be used to directly generate potent inhibitors without further optimization via high throughput screening or selection.

De novo design of stable proteins that efficaciously inhibit oncogenic G proteins.

Many protein therapeutics are competitive inhibitors that function by binding to endogenous proteins and preventing them from interacting with native partners. One effective strategy for engineering competitive inhibitors is to graft structural motifs from a native partner into a host protein. Here, we develop and experimentally test a computational protocol for embedding binding motifs in de novo designed proteins. The protocol uses an "inside-out" approach: Starting with a structural model of the binding motif docked against the target protein, the de novo protein is built by growing new structural elements off the termini of the binding motif. During backbone assembly, a score function favors backbones that introduce new tertiary contacts within the designed protein and do not introduce clashes with the target binding partner. Final sequences are designed and optimized using the molecular modeling program Rosetta. To test our protocol, we designed small helical proteins to inhibit the interaction between Gα q and its effector PLC-ß isozymes. Several of the designed proteins remain folded above 90°C and bind to Gα q with equilibrium dissociation constants tighter than 80 nM. In cellular assays with oncogenic variants of Gα q , the designed proteins inhibit activation of PLC-ß isozymes and Dbl-family RhoGEFs. Our results demonstrate that computational protein design, in combination with motif grafting, can be used to directly generate potent inhibitors without further optimization via high throughput screening or selection. statement for broader audience: Engineered proteins that bind to specific target proteins are useful as research reagents, diagnostics, and therapeutics. We used computational protein design to engineer de novo proteins that bind and competitively inhibit the G protein, Gα q , which is an oncogene for uveal melanomas. This computational method is a general approach that should be useful for designing competitive inhibitors against other proteins of interest.

AlphaFold accurately predicts distinct conformations based on the oligomeric state of a de novo designed protein.

Using the molecular modeling program Rosetta, we designed a de novo protein, called SEWN0.1, which binds the heterotrimeric G protein Gαq. The design is helical, well-folded, and primarily monomeric in solution at a concentration of 10 µM. However, when we solved the crystal structure of SEWN0.1 at 1.9 Å, we observed a dimer in a conformation incompatible with binding Gαq . Unintentionally, we had designed a protein that adopts alternate conformations depending on its oligomeric state. Recently, there has been tremendous progress in the field of protein structure prediction as new methods in artificial intelligence have been used to predict structures with high accuracy. We were curious if the structure prediction method AlphaFold could predict the structure of SEWN0.1 and if the prediction depended on oligomeric state. When AlphaFold was used to predict the structure of monomeric SEWN0.1, it produced a model that resembles the Rosetta design model and is compatible with binding Gαq , but when used to predict the structure of a dimer, it predicted a conformation that closely resembles the SEWN0.1 crystal structure. AlphaFold's ability to predict multiple conformations for a single protein sequence should be useful for engineering protein switches.

Designer proteins that competitively inhibit Gαq by targeting its effector site.

During signal transduction, the G protein, Gαq, binds and activates phospholipase C-ß isozymes. Several diseases have been shown to manifest upon constitutively activating mutation of Gαq, such as uveal melanoma. Therefore, methods are needed to directly inhibit Gαq. Previously, we demonstrated that a peptide derived from a helix-turn-helix (HTH) region of PLC-ß3 (residues 852-878) binds Gαq with low micromolar affinity and inhibits Gαq by competing with full-length PLC-ß isozymes for binding. Since the HTH peptide is unstructured in the absence of Gαq, we hypothesized that embedding the HTH in a folded protein might stabilize the binding-competent conformation and further improve the potency of inhibition. Using the molecular modeling software Rosetta, we searched the Protein Data Bank for proteins with similar HTH structures near their surface. The candidate proteins were computationally docked against Gαq, and their surfaces were redesigned to stabilize this interaction. We then used yeast surface display to affinity mature the designs. The most potent design bound Gαq/i with high affinity in vitro (KD = 18 nM) and inhibited activation of PLC-ß isozymes in HEK293 cells. We anticipate that our genetically encoded inhibitor will help interrogate the role of Gαq in healthy and disease model systems. Our work demonstrates that grafting interaction motifs into folded proteins is a powerful approach for generating inhibitors of protein-protein interactions.

Perturbing the energy landscape for improved packing during computational protein design.

The FastDesign protocol in the molecular modeling program Rosetta iterates between sequence optimization and structure refinement to stabilize de novo designed protein structures and complexes. FastDesign has been used previously to design novel protein folds and assemblies with important applications in research and medicine. To promote sampling of alternative conformations and sequences, FastDesign includes stages where the energy landscape is smoothened by reducing repulsive forces. Here, we discover that this process disfavors larger amino acids in the protein core because the protein compresses in the early stages of refinement. By testing alternative ramping strategies for the repulsive weight, we arrive at a scheme that produces lower energy designs with more native-like sequence composition in the protein core. We further validate the protocol by designing and experimentally characterizing over 4000 proteins and show that the new protocol produces higher stability proteins.

5-Hydroxymethylcytosine (5hmC) and Ten-eleven translocation 1-3 (TET1-3) proteins in the dorsal root ganglia of mouse: Expression and dynamic regulation in neuropathic pain.

Epigenetic mechanisms are increasingly implicated in chronic pain pathology. In this study, we demonstrate that the novel epigenetic mark 5-hydroxymethylcytosine (5hmC) is present in dorsal root ganglia (DRG) neurons and glia, and its levels increase following nerve injury. Furthermore, we show that the 5hmC-generating Ten-eleven translocation 1-3 (TET1-3) proteins are expressed in a cell-type specific manner in the DRG, with Tet3 displaying differential upregulation after injury, suggesting a potential role in neuropathic pain.

An Adaptation To Life In Acid Through A Novel Mevalonate Pathway.

Extreme acidophiles are capable of growth at pH values near zero. Sustaining life in acidic environments requires extensive adaptations of membranes, proton pumps, and DNA repair mechanisms. Here we describe an adaptation of a core biochemical pathway, the mevalonate pathway, in extreme acidophiles. Two previously known mevalonate pathways involve ATP dependent decarboxylation of either mevalonate 5-phosphate or mevalonate 5-pyrophosphate, in which a single enzyme carries out two essential steps: (1) phosphorylation of the mevalonate moiety at the 3-OH position and (2) subsequent decarboxylation. We now demonstrate that in extreme acidophiles, decarboxylation is carried out by two separate steps: previously identified enzymes generate mevalonate 3,5-bisphosphate and a new decarboxylase we describe here, mevalonate 3,5-bisphosphate decarboxylase, produces isopentenyl phosphate. Why use two enzymes in acidophiles when one enzyme provides both functionalities in all other organisms examined to date? We find that at low pH, the dual function enzyme, mevalonate 5-phosphate decarboxylase is unable to carry out the first phosphorylation step, yet retains its ability to perform decarboxylation. We therefore propose that extreme acidophiles had to replace the dual-purpose enzyme with two specialized enzymes to efficiently produce isoprenoids in extremely acidic environments.