Biology subjects

Golinski, A. W.

Publications and source records attributed to Golinski, A. W..

2 recordsLinked to original sources

bioRxiv2021

A platform for deep sequence-activity mapping and engineering antimicrobial peptides

Developing potent antimicrobials, and platforms for their study and engineering, is critical as antibiotic resistance grows. A high-throughput method to quantify antimicrobial peptide and protein (AMP) activity across a broad continuum can elucidate sequence-activity landscapes and identify potent mutants. We developed a platform to perform sequence-activity mapping of AMPs via depletion (SAMP-Dep): a bacterial host culture is transformed with an AMP mutant library, induced to express AMPs, grown, and deep sequenced to quantify mutant frequency. The slope of mutant growth rate versus induction level indicates potency. Using SAMP-Dep, we screened 170,000 mutants of oncocin, a proline-rich AMP, for intracellular activity against Escherichia coli. Clonal validation of 36 mutants supported SAMP-Dep sensitivity and accuracy. The efficiency and accuracy of SAMP-Dep enabled mapping the oncocin sequence-activity space with remarkable detail and scale and guided focused, successful synthetic peptide library design, yielding a mutant with two-fold enhancement in both intracellular and extracellular activity.

bioengineering↗

bioRxiv2020

High-Throughput Developability Assays Enable Library-Scale Identification of Producible Protein Scaffold Variants

Proteins require high developability - quantified by expression, solubility, and stability - for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low-throughput in nature, often slowing the developmental pipeline. We evaluated the ability of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 105 variants was calculated via deep sequencing of populations sorted by proxied developability. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a sitewise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.

bioengineering↗