Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis

The accurate prediction of protein stability upon sequence mutation is an important but unsolved challenge in protein engineering. Large mutational datasets are required to train computational predictors, but traditional methods for collecting stability data are either low-throughput or measure protein stability indirectly. Here, we develop an automated method to generate thermodynamic stability data for nearly every single mutant in a small 56-residue protein. Analysis reveals that most single mutants have a neutral effect on stability, mutational sensitivity is largely governed by residue burial, and unexpectedly, hydrophobics are the best tolerated amino acid type. Correlating the output of various stability-prediction algorithms against our data shows that nearly all perform better on boundary and surface positions than for those in the core and are better at predicting large-to-small mutations than small-to-large ones. We show that the most stable variants in the single-mutant landscape are better identified using combinations of 2 prediction algorithms and including more algorithms can provide diminishing returns. In most cases, poor in silico predictions were tied to compositional differences between the data being analyzed and the datasets used to train the algorithm. Finally, we find that strategies to extract stabilities from high-throughput fitness data such as deep mutational scanning are promising and that data produced by these methods may be applicable toward training future stability-prediction tools

Mechanism of an ATP-independent Protein Disaggregase. II. Distinct Molecular Interactions Drive Multiple Steps During Aggregate Disassembly

The ability of molecular chaperones to overcome the misfolding and aggregation of proteins is essential for the maintenance of proper protein homeostasis in all cells. Thus far, the best studied disaggregase systems are the Clp/Hsp100 family of “ATPases associated with various cellular activities” (AAA^+) ATPases, which use mechanical forces powered by ATP hydrolysis to remodel protein aggregates. An alternative system to disassemble large protein aggregates is provided by the 38-kDa subunit of the chloroplast signal recognition particle (cpSRP43), which uses binding energy with its substrate proteins to drive disaggregation. The mechanism of this novel chaperone remains unclear. Here, molecular genetics and structure-activity analyses show that the action of cpSRP43 can be dissected into two steps with distinct molecular requirements: (i) initial recognition, during which cpSRP43 binds specifically to a recognition motif displayed on the surface of the aggregate; and (ii) aggregate remodeling, during which highly adaptable binding interactions of cpSRP43 with hydrophobic transmembrane domains of the substrate protein compete with the packing interactions within the aggregate. This establishes a useful framework to understand the molecular mechanism by which binding interactions from a molecular chaperone can be used to overcome protein aggregates in the absence of external energy input from ATP

Particulate methane monooxygenase contains only mononuclear copper centers

Bacteria that oxidize methane to methanol are central to mitigating emissions of methane, a potent greenhouse gas. The nature of the copper active site in the primary metabolic enzyme of these bacteria, particulate methane monooxygenase (pMMO), has been controversial owing to seemingly contradictory biochemical, spectroscopic, and crystallographic results. We present biochemical and electron paramagnetic resonance spectroscopic characterization most consistent with two monocopper sites within pMMO: one in the soluble PmoB subunit at the previously assigned active site (CuB) and one ~2 nanometers away in the membrane-bound PmoC subunit (CuC). On the basis of these results, we propose that a monocopper site is able to catalyze methane oxidation in pMMO

Cell-free protein synthesis enables high yielding synthesis of an active multicopper oxidase

Multicopper oxidases (MCOs) are broadly distributed in all kingdoms of life and perform a variety of important oxidative reactions. These enzymes have potential biotechnological applications; however, the applications are impeded by low expression yields in traditional recombinant hosts, solubility issues, and poor copper cofactor assembly. As an alternative to traditional recombinant protein expression, we show the ability to use cell-free protein synthesis (CFPS) to produce complex MCO proteins with high soluble titers. Specifically, we report the production of MCOs in an Escherichia coli-based cell-free transcription-translation system. Total yields as high as 1.2 mg mL-1 were observed after a 20-h batch reaction. More than 95% of the protein was soluble and activity was obtained by simple post-CFPS addition of copper ions in the form of CuSO4. Scale-up reactions were achieved from 15 to 100 μL without a decrease in productivity and solubility. CFPS titers were higher than in vivo expression titers and more soluble, avoiding the formation of inclusion bodies. Our work extends the utility of the cell-free platform to the production of active proteins containing copper cofactors and demonstrates a simple method for producing MCOs

Accelerating the Interplay Between Theory and Experiment in Protein Design

Protein engineering techniques such as directed evolution and structure-based design aim to improve the properties of natural proteins. The next step, the de novo insertion of function into previously inert protein scaffolds, is the lofty promise of computational protein design. In order to achieve this goal reliably and efficiently, computational methods can be iteratively improved by cycling between theory and experiment. Efforts to both accelerate the rate and broaden the information exchanged within protein design cycles form the core of this thesis. Improvements in the throughput of experimental stability determination allowed the thorough assessment of new multi-state and library design tools. Intending to alleviate the fixed backbone, single native state design approximation, the study found constrained molecular dynamics ensembles useful for core repacking applications. The subsequent development of automated liquid handling protocols for common molecular biology techniques brings design experiments to new levels of sample throughput. This technology facilitated the creation of a stability database encompassing every single mutant in a small protein domain. Although constructed to facilitate future computational training efforts, we answer a multitude of questions pertaining to mutational outcomes, distributions, positional sensitivity, tolerance, and additivity in the context of a protein domain. By expanding the constraints of experimental molecular biology, this work opens up new possibilities in the efforts to train and assay new computational methodologies for protein engineering applications.</p