A solid-phase platform for combinatorial and scarless multipart gene assembly

Peptide vectors for gene delivery: from single peptides to multifunctional peptide nanocarriers

The therapeutic use of nucleic acids relies on the availability of sophisticated delivery systems for targeted and intracellular delivery of these molecules. Such a gene delivery should possess essential characteristics to overcome several extracellular and intracellular barriers. Peptides offer an attractive platform for nonviral gene delivery, as several functional peptide classes exist capable of overcoming these barriers. However, none of these functional peptide classes contain all the essential characteristics required to overcome all of the barriers associated with successful gene delivery. Combining functional peptides into multifunctional peptide vectors will be pivotal for improving peptide-based gene delivery systems. By using combinatorial strategies and high-throughput screening, the identification of multifunctional peptide vectors will accelerate the optimization of peptide-based gene delivery systems.

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs.