Sequence Determinants of E2-E6AP Binding Affinity and Specificity

The conjugation of ubiquitin to substrates requires a series of enzymatic reactions consisting of an activating enzyme (E1), conjugating enzymes (E2) and ligases (E3). Tagging the appropriate substrate with ubiquitin is achieved by specific E2-E3 and E3-substrate interactions. E6AP, a member of the HECT family of E3s, has been previously shown to bind and function with the E2s UbcH7 and UbcH8. To decipher the sequence determinants of this specificity we have developed a quantitative E2-E3 binding assay based on fluorescence polarization and used this assay to measure the affinity of wild type and mutant E2–E6AP interactions. Alanine scanning of the E6AP–UbcH7 binding interface identified 4 side chains on UbcH7 and 6 side chains on E6AP that contribute more than 1 kcal /mol to the binding free energy. Two of the hot spot residues from UbcH7 (K96 and K100) are conserved in UbcH8 but vary across other E2s. To determine if these are key specificity determining residues, we attempted to induce a tighter association between the E2 UbcH5b and E6AP by mutating the corresponding positions in UbcH5b to lysines. Surprisingly, the mutations had little effect, but rather a mutation at UbcH7 position 4, which is not at a hot spot on the UbcH7–E6AP interface, significantly strengthened UbcH5bs affinity for E6AP. This result indicates that E2-E3 binding specificities are a function of both favorable interactions that promote binding, and unfavorable interactions that prevent binding with unwanted partners

Computational design of second-site suppressor mutations at protein-protein interfaces

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 G-protein Gαi1 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 non-polar interactions (55-fold). In two cases, specificity was further tuned by including mutations distant from the initial design

Structure-based Protocol for Identifying Mutations that Enhance Protein–Protein Binding Affinities

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 Rosetta energy function emphasizes short-range interactions: steric repulsion, Van der Waals forces, hydrogen bonding, and an implicit solvation model that penalizes placing atoms adjacent to polar groups. The performance of the computational protocol was experimentally tested on two separate protein complexes; Gαi1 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. 12 single-site mutations that were predicted to be stabilizing were synthesized and characterized in the laboratory. 9 of the 12 mutations successfully increased binding affinity with 5 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 8 mutations identified, 5 were accurately predicted to increase binding affinity, further validating the method as a useful tool to increase protein-protein binding affinities

High-resolution structural and thermodynamic analysis of extreme stabilization of human procarboxypeptidase by computational protein design

Recent efforts to design de novo or redesign the sequence and structure of proteins using computational techniques have met with significant success. Most, if not all, of these computational methodologies attempt to model atomic-level interactions, and hence high-resolution structural characterization of the designed proteins is critical for evaluating the atomic-level accuracy of the underlying design force-fields. We previously used our computational protein design protocol RosettaDesign to completely redesign the sequence of the activation domain of human procarboxypeptidase A2. With 68 % of the wild-type sequence changed, the designed protein, AYEdesign, is over 10 kcal/mol more stable than the wild-type protein. Here, we describe the high-resolution crystal structure and solution NMR structure of AYEdesign, which show that the experimentally determined backbone and side-chains conformations are effectively superimposable with the computational model at atomic resolution. To isolate the origins of the remarkable stabilization, we have designed an