Next-generation sequencing (NGS) workflows have several steps with each step potentially allowing for the introduction of errors that could significantly impact the quality and reliability of sequencing results. Reagents and methods used to generate NGS data should therefore be of the highest quality, and need to be tested and validated for robust precision and accuracy. Although several reagent options are available for various steps of the NGS workflow, the difference among them exists primarily due to the composition of buffers or the concentration of enzymes. The majority of DNA- and RNA-modifying enzymes used in common NGS reagents belong to “wild-type”, isolated from nature without modification, and these enzymes were never intended as tools for molecular biology. Directed evolution technology selects enzymes used in NGS reagents through a simulated natural selection process in the lab for high performance efficiency.
The process starts with a gene coding for a “wild-type”, or unmodified, enzyme of interest. Random variation is introduced into the gene through a process of mutagenesis, generating a library of millions of genes, each coding for a unique enzyme variant. A functional selection pressure is then applied to the library where only the genes that coded for the highest performing enzymes “survive”. This process of random mutation and selection is repeated until the desired enzyme function evolves.
Since introducing random mutations into a gene is mostly harmful to the function of the encoded enzyme, unlocking the power of directed evolution requires the ability to screen very large numbers of enzyme variants. Our core technology combines a high-throughput emulsion format where aqueous droplets suspended in oil, which serve as microreactors, are combined with high-throughput selection assays to enable the functional screening of hundreds of millions of enzyme variants in parallel.