The ability to manipulate protein binding affinities is important for the development of proteins as biosensors, industrial reagents, and therapeutics. We have developed a structure-based method to rationally predict single mutations at protein-protein interfaces that enhance binding affinities. The protocol is based on the premise that increasing buried hydrophobic surface area and/or reducing buried hydrophilic surface area will generally lead to enhanced affinity if large steric clashes are not introduced and buried polar groups are not left without a hydrogen bond partner. The procedure selects affinity enhancing point mutations at the protein-protein interface using three criteria: (1) the mutation must be from a polar amino acid to a non-polar amino acid or from a non-polar amino acid to a larger non-polar amino acid, (2) the free energy of binding as calculated with the Rosetta protein modeling program should be more favorable than the free energy of binding calculated for the wild-type complex and (3) the mutation should not be predicted to significantly destabilize the monomers. The performance of the computational protocol was experimentally tested on two separate protein complexes; Galpha(i1) from the heterotrimeric G-protein system bound to the RGS14 GoLoco motif, and the E2, UbcH7, bound to the E3, E6AP from the ubiquitin pathway. Twelve single-site mutations that were predicted to be stabilizing were synthesized and characterized in the laboratory. Nine of the 12 mutations successfully increased binding affinity with five of these increasing binding by over 1.0 kcal/mol. To further assess our approach we searched the literature for point mutations that pass our criteria and have experimentally determined binding affinities. Of the eight mutations identified, five were accurately predicted to increase binding affinity, further validating the method as a useful tool to increase protein-protein binding affinities.
Computational algorithms for protein design can sample large regions of sequence space, but suffer from undersampling of conformational space and energy function inaccuracies. Experimental screening of combinatorial protein libraries avoids the need for accurate energy functions, but has limited access to vast amounts of sequence space. Here, we test if these two traditionally alternative, but potentially complementary approaches can be combined to design a variant of the ubiquitin-ligase E6AP that will bind to a nonnatural partner, the NEDD8-conjugating enzyme Ubc12. Three E6AP libraries were constructed: (i) a naive library in which all 20 amino acids were allowed at every position on the target-binding surface of E6AP (13 positions), (ii) a semidirected library that varied the same residue positions as in the naive library but disallowed mutations computationally predicted to destabilize E6AP, and (iii) a directed library that used docking and sequence optimization simulations to identify mutations predicted to be favorable for binding Ubc12. Both of the directed libraries showed >30-fold enrichment over the naive library after the first round of screening with a split-dihydrofolate reductase complementation assay and produced multiple tight binders (K d < 100 nM) after four rounds of selection. Four rounds of selection with the naive library failed to produce any binders with K d 's lower than 50 μM. These results indicate that protein design simulations can be used to create directed libraries that are enriched in tight binders and that in some cases it is sufficient to computationally screen for well-folded sequences without explicit binding calculations.computational protein design | protein engineering | Rosetta C omputational design and selection from combinatorial libraries are alternative approaches for protein engineering. In computational design, high-resolution models of protein structure are used to predict amino acid sequences that will stabilize target protein structures or complexes. Computational design has been used to stabilize protein structures and interfaces, redesign protein-binding specificities, and in a few cases build protein structures from scratch (1-4). Despite these successes, many problems in protein engineering remain very difficult for computational design: These include the design of protein-protein interactions and the creation of enzymes that compare favorably with naturally occurring enzymes (5-9). In silico ranking of designed interfaces is challenging because large free energies of desolvation have to be correctly balanced by hydrogen bonding and van der Waals interactions that are sensitive to small changes in atomic positions. As a result, there are often significant errors in calculated energies for protein-binding events. One advantage of screening or selecting from large-scale combinatorial libraries is that it does not rely on the accuracy of an energy functioneach sequence is experimentally tested for the desired functionality. Protein selection techniques such as...
The importance of a protein-protein interaction to a signaling pathway can be established by showing that amino acid mutations that weaken the interaction disrupt signaling, and that additional mutations that rescue the interaction recover signaling. Identifying rescue mutations, often referred to as second-site suppressor mutations, controls against scenarios in which the initial deleterious mutation inactivates the protein or disrupts alternative protein-protein interactions. Here, we test a structure-based protocol for identifying second-site suppressor mutations that is based on a strategy previously described by Kortemme and Baker. The molecular modeling software Rosetta is used to scan an interface for point mutations that are predicted to weaken binding but can be rescued by mutations on the partner protein. The protocol typically identifies three types of specificity switches: knob-in-to-hole redesigns, switching hydrophobic interactions to hydrogen bond interactions, and replacing polar interactions with non-polar interactions. Computational predictions were tested with two separate protein complexes; the Gprotein Gα i1 bound to the RGS14 GoLoco motif, and UbcH7 bound to the ubiquitin ligase E6AP. Eight designs were experimentally tested. Swapping a buried hydrophobic residue with a polar residue dramatically weakened binding affinities. In none of these cases were we able to identify compensating mutations that returned binding to wild type affinity, highlighting the challenges inherent in designing buried hydrogen bond networks. The strongest specificity switches were a knob-in-to-hole design (20-fold) and the replacement of a charge-charge interaction with nonpolar interactions (55-fold). In two cases, specificity was further tuned by including mutations distant from the initial design.
Emerging evidence suggests that ubiquitination serves as a protein trafficking signal in addition to its well characterized role in promoting protein degradation. The yeast G protein ␣ subunit Gpa1 represents a rare example of a protein that undergoes both mono-and poly-ubiquitination. Whereas mono-ubiquitinated Gpa1 is targeted to the vacuole, poly-ubiquitinated Gpa1 is directed instead to the proteasome. Here we investigate the structural requirements for mono-and poly-ubiquitination of Gpa1. We find that variants of Gpa1 engineered to be unstable are more likely to be poly-ubiquitinated and less likely to be mono-ubiquitinated. In addition, mutants that cannot be myristoylated are no longer mono-ubiquitinated but are still polyubiquitinated. Finally, we show that the ubiquitin ligase Rsp5 is necessary for Gpa1 mono-ubiquitination in vivo and that the purified enzyme is sufficient to catalyze Gpa1 mono-ubiquitination in vitro. Taken together, these data indicate that mono-and poly-ubiquitination have distinct enzyme and substrate recognition requirements; whereas poly-ubiquitination targets misfolded protein for degradation, a distinct ubiquitination apparatus targets the fully mature, fully myristoylated G protein for mono-ubiquitination and delivery to the vacuole.
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