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.

CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort.

Motivation: High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and is practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required. Results: We have developed CellSort, a support vector machine (SVM) algorithm that identifies optimal sorting gates based on machine learning using positive and negative control populations. CellSort can take advantage of more than two dimensions to enhance the ability to distinguish between populations. We also present a Bayesian approach to predict the number of sorting rounds required to enrich a population from a given library size. This Bayesian approach allowed us to determine strategies for biasing the sorting gates in order to reduce the required number of enrichment rounds. This algorithm should be generally useful for improve sorting outcomes and reducing effort when using FACS. Availability and Implementation: Source code available at http://tyolab.northwestern.edu/tools/ . k-tyo@northwestern.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Plasmid-based one-pot saturation mutagenesis.

Deep mutational scanning is a foundational tool for addressing the functional consequences of large numbers of mutants, but a more efficient and accessible method for construction of user-defined mutagenesis libraries is needed. Here we present nicking mutagenesis, a robust, single-day, one-pot saturation mutagenesis method performed on routinely prepped plasmid dsDNA. The method can be used to produce comprehensive or single- or multi-site saturation mutagenesis libraries.

Yeast-based biosensors: design and applications.

Yeast-based biosensing (YBB) is an exciting research area, as many studies have demonstrated the use of yeasts to accurately detect specific molecules. Biosensors incorporating various yeasts have been reported to detect an incredibly large range of molecules including but not limited to odorants, metals, intracellular metabolites, carcinogens, lactate, alcohols, and sugars. We review the detection strategies available for different types of analytes, as well as the wide range of output methods that have been incorporated with yeast biosensors. We group biosensors into two categories: those that are dependent upon transcription of a gene to report the detection of a desired molecule and those that are independent of this reporting mechanism. Transcription-dependent biosensors frequently depend on heterologous expression of sensing elements from non-yeast organisms, a strategy that has greatly expanded the range of molecules available for detection by YBBs. Transcription-independent biosensors circumvent the problem of sensing difficult-to-detect analytes by instead relying on yeast metabolism to generate easily detected molecules when the analyte is present. The use of yeast as the sensing element in biosensors has proven to be successful and continues to hold great promise for a variety of applications.

Improving coiled coil stability while maintaining specificity by a bacterial hitchhiker selection system.

The design and selection of peptides targeting cellular proteins is challenging and often yields candidates with undesired properties. Therefore we deployed a new selection system based on the twin-arginine translocase (TAT) pathway of Escherichia coli, named hitchhiker translocation (HiT) selection. A pool of α-helix encoding sequences was designed and selected for interference with the coiled coil domain (CC) of a melanoma-associated basic-helix-loop-helix-leucine-zipper (bHLHLZ) protein, the microphthalmia associated transcription factor (MITF). One predominant sequence (iM10) was enriched during selection and showed remarkable protease resistance, high solubility and thermal stability while maintaining its specificity. Furthermore, it exhibited nanomolar range affinity towards the target peptide. A mutation screen indicated that target-binding helices of increased homodimer stability and improved expression rates were preferred in the selection process. The crystal structure of the iM10/MITF-CC heterodimer (2.1Å) provided important structural insights and validated our design predictions. Importantly, iM10 did not only bind to the MITF coiled coil, but also to the markedly more stable HLHLZ domain of MITF. Characterizing the selected variants of the semi-rational library demonstrated the potential of the innovative bacterial selection approach.