Biology subjects

Bae, J. H.

Publications and source records attributed to Bae, J. H..

3 recordsLinked to original sources

bioRxiv2021

Dendrimer-like supramolecular assembly of proteins with a tunable size and valency through stepwise iterative growth

The assembly of proteins in a programmable manner provides insight into the underlying mechanisms of protein self-assembly in nature as well as the creation of novel functional nanomaterials for practical applications. Despite many advances, however, a rational protein assembly with an easy scalability in terms of size and valency remains a challenging task. Here, we present a simple bottom-up approach to the supramolecular protein assembly with a tunable size and valency in a programmable manner. The dendrimer-like protein assembly, called a "prodrimer," was constructed using a total of three monomeric proteins: a core and two building-block proteins. The prodrimer generations were grown by a stepwise and alternate addition of a building block using two pairs of orthogonal protein-peptide interactions, leading to a higher-generation prodrimer with a mega-dalton size and multi-valency. The valency of the prodrimers at the periphery was tunable with the generation, enabling a single-step functionalization. A second-generation prodrimer functionalized with a target-specific protein binder showed a three-order of magnitude increase in binding affinity compared to a monomeric counterpart due to the avidity. The prodrimers functionalized with a targeting moiety and a cytotoxic protein cargo exhibited a highly enhanced cellular cytotoxicity, exemplifying their utility as a protein delivery platform. The present approach can be effectively used in the creation of protein architectures with new functions for biotechnological and medical applications.

bioengineering↗

bioRxiv2021

CODEC enables 'single duplex' sequencing

Detecting mutations as rare as a single molecule is crucial in many fields such as cancer diagnostics and aging research but remains challenging. Third generation sequencers can read a double-stranded DNA molecule (a single duplex) in whole to identify true mutations on both strands apart from false mutations on either strand but with limited accuracy and throughput. Although next generation sequencing (NGS) can track dissociated strands with Duplex Sequencing, the need to sequence each strand independently severely diminishes its throughput. Here, we developed a hybrid method called Concatenating Original Duplex for Error Correction (CODEC) that combines the massively parallel nature of NGS with the single-molecule capability of third generation sequencing. CODEC physically links both strands to enable NGS to sequence a single duplex with a single read pair. By comparing CODEC and Duplex Sequencing, we showed that CODEC achieved a similar error rate (10-6) with 100 times fewer reads and conferred single duplex resolution to most major NGS workflows.

bioengineering↗

bioRxiv2021

MAESTRO affords 'breadth and depth' for mutation testing

The ability to assay large numbers of low-abundance mutations is crucial in biomedicine. Yet, the technical hurdles of sequencing multiple mutations at extremely high depth and accuracy remain daunting. For sequencing low-level mutations, its either depth or breadth but not both. Here, we report a simple and powerful approach to accurately track thousands of distinct mutations with minimal reads. Our technique called MAESTRO (minor allele enriched sequencing through recognition oligonucleotides) employs massively-parallel mutation enrichment to empower duplex sequencing--one of the most accurate methods--to track up to 10,000 low-frequency mutations with up to 100-fold less sequencing. In example use cases, we show that MAESTRO could enable mutation validation from cancer genome sequencing studies. We also show that it could track thousands of mutations from a patients tumor in cell-free DNA, which may improve detection of minimal residual disease from liquid biopsies. In all, MAESTRO improves the breadth, depth, accuracy, and efficiency of mutation testing.

molecular biology↗