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.

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.