Samuel Thompson

Computationally modeling changes in binding free energies upon mutation (interface ΔΔ G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ΔΔ G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting nonlinear reweighting model improved the agreement with experimentally determined interface ΔΔ G values but also highlighted the necessity of future energy function improvements.

Biological microscopy would benefit from smaller alternatives to green fluorescent protein for imaging specific proteins in living cells. Here we introduce PRIME (PRobe Incorporation Mediated by Enzymes), a method for fluorescent labeling of peptide-fused recombinant proteins in living cells with high specificity. PRIME uses an engineered fluorophore ligase, which is derived from the natural Escherichia coli enzyme lipoic acid ligase (LplA). Through structure-guided mutagenesis, we created a mutant ligase capable of recognizing a 7-hydroxycoumarin substrate and catalyzing its covalent conjugation to a transposable 13-amino acid peptide called LAP (LplA Acceptor Peptide). We showed that this fluorophore ligation occurs in cells in 10 min and that it is highly specific for LAP fusion proteins over all endogenous mammalian proteins. By genetically targeting the PRIME ligase to specific subcellular compartments, we were able to selectively label spatially distinct subsets of proteins, such as the surface pool of neurexin and the nuclear pool of actin.

Escherichia coli lipoic acid ligase (LplA) catalyzes ATP-dependent covalent ligation of lipoic acid onto specific lysine side chains of three acceptor proteins involved in oxidative metabolism. Our lab has shown that LplA and engineered mutants can ligate useful small-molecule probes such as alkyl azides ( Nat. Biotechnol. 2007 , 25 , 1483 - 1487 ) and photo-cross-linkers ( Angew. Chem., Int. Ed. 2008 , 47 , 7018 - 7021 ) in place of lipoic acid, facilitating imaging and proteomic studies. Both to further our understanding of lipoic acid metabolism, and to improve LplA's utility as a biotechnological platform, we have engineered a novel 13-amino acid peptide substrate for LplA. LplA's natural protein substrates have a conserved beta-hairpin structure, a conformation that is difficult to recapitulate in a peptide, and thus we performed in vitro evolution to engineer the LplA peptide substrate, called "LplA Acceptor Peptide" (LAP). A approximately 10(7) library of LAP variants was displayed on the surface of yeast cells, labeled by LplA with either lipoic acid or bromoalkanoic acid, and the most efficiently labeled LAP clones were isolated by fluorescence activated cell sorting. Four rounds of evolution followed by additional rational mutagenesis produced a "LAP2" sequence with a k(cat)/K(m) of 0.99 muM(-1) min(-1), >70-fold better than our previous rationally designed 22-amino acid LAP1 sequence (Nat. Biotechnol. 2007, 25, 1483-1487), and only 8-fold worse than the k(cat)/K(m) values of natural lipoate and biotin acceptor proteins. The kinetic improvement over LAP1 allowed us to rapidly label cell surface peptide-fused receptors with quantum dots.

Ubiquitin is essential for eukaryotic life and varies in only 3 amino acid positions between yeast and humans. However, recent deep sequencing studies indicate that ubiquitin is highly tolerant to single mutations. We hypothesized that this tolerance would be reduced by chemically induced physiologic perturbations. To test this hypothesis, a class of first year UCSF graduate students employed deep mutational scanning to determine the fitness landscape of all possible single residue mutations in the presence of five different small molecule perturbations. These perturbations uncover 'shared sensitized positions' localized to areas around the hydrophobic patch and the C-terminus. In addition, we identified perturbation specific effects such as a sensitization of His68 in HU and a tolerance to mutation at Lys63 in DTT. Our data show how chemical stresses can reduce buffering effects in the ubiquitin proteasome system. Finally, this study demonstrates the potential of lab-based interdisciplinary graduate curriculum.

Protein mutational landscapes are shaped by the cellular environment, but key factors and their quantitative effects are often unknown. Here we show that Lon, a quality control protease naturally absent in common E. coli expression strains, drastically reshapes the mutational landscape of the metabolic enzyme dihydrofolate reductase (DHFR). Selection under conditions that resolve highly active mutants reveals that 23.3% of all single point mutations in DHFR are advantageous in the absence of Lon, but advantageous mutations are largely suppressed when Lon is reintroduced. Protein stability measurements demonstrate extensive activity-stability tradeoffs for the advantageous mutants and provide a mechanistic explanation for Lon’s widespread impact. Our findings suggest possibilities for tuning mutational landscapes by modulating the cellular environment, with implications for protein design and combatting antibiotic resistance.