Protecting P-type plasma membrane H+-ATPases from ROS.

P-type ATPase are ubiquitous transport proteins across all kingdoms of life. These proteins share a common mechanism involving phosphorylation of an invariant aspartate to facilitate movement of substrates from protons to phospholipids across cellular membranes. In this issue of the Biochemical Journal, Welle et al. identify a conserved cysteine near the functionally critical aspartate of P-type plasma membrane H+-ATPases that protects the protein from reactive oxygen species.

Model-guided mechanism discovery and parameter selection for directed evolution.

Directed evolution is frequently applied to identify genetic variants with improvements in a single or multiple properties. When used to improve multiple properties simultaneously, a common strategy is to apply alternating rounds of selection criteria to enrich for variants with each desirable trait. In particular, counterselection, or selection against undesired traits rather than for desired ones, has been successfully employed in many studies. Although the sequence and stringency of alternating selective pressures for different traits are known to be highly consequential for the outcome of the screen, the effects of these parameters have not been systematically evaluated. We developed a method for producing a statistical modeling framework to elucidate these effects. The model uses single-cell fluorescence intensity distributions to estimate the proportions of phenotypic populations within a library and then predicts the changes in these proportions depending on specified positive selective or counterselective pressures. We validated the approach using recently described systems for metabolite-responsive bacterial transcription factors and yeast G-protein-coupled receptors. Finally, we applied the model to identify biological sources that exert undesirable selective pressure on libraries during sorting. Notably, these pressures produce substantial artifacts that, if unaddressed, can lead to failure of the screen. This method for model generation can be applied to FACS-based directed evolution experiments to create a quantitative framework that identifies subtle population effects. Such models can guide the choice of experimental design parameters to better enrich for true positive genetic variants and improve the chance of successful directed evolution.

Detection of a Peptide Biomarker by Engineered Yeast Receptors.

Directed evolution of membrane receptors is challenging as the evolved receptor must not only accommodate a non-native ligand, but also maintain the ability to transduce the detection of the new ligand to any associated intracellular components. The G-protein coupled receptor (GPCR) superfamily is the largest group of membrane receptors. As members of the GPCR family detect a wide range of ligands, GPCRs are an incredibly useful starting point for directed evolution of user-defined analytical tools and diagnostics. The aim of this study was to determine if directed evolution of the yeast Ste2p GPCR, which natively detects the α-factor peptide, could yield a GPCR that detects Cystatin C, a human peptide biomarker. We demonstrate a generalizable approach for evolving Ste2p to detect peptide sequences. Because the target peptide differs significantly from α-factor, a single evolutionary step was infeasible. We turned to a substrate walking approach and evolved receptors for a series of chimeric intermediates with increasing similarity to the biomarker. We validate our previous model as a tool for designing optimal chimeric peptide steps. Finally, we demonstrate the clinical utility of yeast-based biosensors by showing specific activation by a C-terminally amidated Cystatin C peptide in commercially sourced human urine. To our knowledge, this is the first directed evolution of a peptide GPCR.

Modulating and evaluating receptor promiscuity through directed evolution and modeling.

The promiscuity of G-protein-coupled receptors (GPCRs) has broad implications in disease, pharmacology and biosensing. Promiscuity is a particularly crucial consideration for protein engineering, where the ability to modulate and model promiscuity is essential for developing desirable proteins. Here, we present methodologies for (i) modifying GPCR promiscuity using directed evolution and (ii) predicting receptor response and identifying important peptide features using quantitative structure-activity relationship models and grouping-exhaustive feature selection. We apply these methodologies to the yeast pheromone receptor Ste2 and its native ligand α-factor. Using directed evolution, we created Ste2 mutants with altered specificity toward a library of α-factor variants. We then used the  Vectors of Hydrophobic, Steric, and Electronic properties and partial least squares regression to characterize receptor-ligand interactions, identify important ligand positions and properties, and predict receptor response to novel ligands. Together, directed evolution and computational analysis enable the control and evaluation of GPCR promiscuity. These approaches should be broadly useful for the study and engineering of GPCRs and other protein-small molecule interactions.

Evaluating enzymatic synthesis of small molecule drugs.

There have been many achievements in applying biochemical synthetic routes to the synthesis of commodity chemicals. However, most of these endeavors have focused on optimizing and increasing the yields of naturally existing pathways. We sought to evaluate the potential for biosynthesis beyond the limits of known biochemistry towards the production of small molecule drugs that do not exist in nature. Because of the potential for improved yields compared to total synthesis, and therefore lower manufacturing costs, we focused on drugs for diseases endemic to many resource poor regions, like tuberculosis and HIV. Using generalized biochemical reaction rules, we were able to design biochemical pathways for the production of eight small molecule drugs or drug precursors and identify potential enzyme-substrate pairs for nearly every predicted reaction. All pathways begin from native metabolites, abrogating the need for specialized precursors. The simulated pathways showed several trends with the sequential ordering of reactions as well as the types of chemistries used. For some compounds, the main obstacles to finding feasible biochemical pathways were the lack of appropriate, natural starting compounds and a low diversity of biochemical coupling reactions necessary to synthesize molecules with larger molecular size.