Observing Biosynthetic Activity Utilizing Next Generation Sequencing and the DNA Linked Enzyme Coupled Assay

Abstract

Currently, the identification of new genes drastically outpaces current experimental methods for determining their enzymatic function. This disparity necessitates the development of high-throughput techniques that operate with the same scalability as modern gene synthesis and sequencing technologies. In this paper, we demonstrate the versatility of the recently reported DNA-Linked Enzyme-Coupled Assay (DLEnCA) and its ability to support high-throughput data acquisition through next-generation sequencing (NGS). Utilizing methyltransferases, we highlight DLEnCA's ability to rapidly profile an enzyme's substrate specificity, determine relative enzyme kinetics, detect biosynthetic formation of a target molecule, and its potential to benefit from the scales and standardization afforded by NGS. This improved methodology minimizes the effort in acquiring biosynthetic knowledge by tying biochemical techniques to the rapidly evolving abilities in sequencing and synthesizing DNA.

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

“…Despite this progress, the relationship between a genetic sequence and its functional properties is poorly understood, and thus the question “what to write” remains largely unanswered 3, 4 . Since the number of possible sequences scales exponentially with their length, the theoretical sequence space cannot be exhaustively explored by experiments, even for small GREs 5–7 . Therefore, innovative high-throughput (HTP) approaches are required that allow to collect a quantitative functional readout for large numbers of genetic sequences 7, 8 .…”

“…Since the number of possible sequences scales exponentially with their length, the theoretical sequence space cannot be exhaustively explored by experiments, even for small GREs 5–7 . Therefore, innovative high-throughput (HTP) approaches are required that allow to collect a quantitative functional readout for large numbers of genetic sequences 7, 8 . At the same time, novel methods are required that identify statistical patterns and dependencies in the resulting datasets to generate models that accurately predict the properties of untested sequences.…”

“…This introduces errors and limits data quality 21, 22 impairing prediction accuracy. In other cases, quantitative readouts, such as ribosome loading 23 , DNA methylation status 7, 24 , or enrichment by growth selection 25 , are technically challenging and case-specific. RNA sequencing techniques avoid some of these limitations but are restricted to transcriptional effects and can be greatly biased due to variability in reverse transcription, barcode-induced bias, and DNA amplification efficiencies 26, 27 .…”

Enzyme promiscuity shapes adaptation to novel growth substrates

“…The activities of the wild‐type YihS and three mutant YihS enzymes (YihS R315S, YihS V314L + R315C, and YihS V314L + R315S) were tested in vitro . A cell‐free in vitro transcription and translation system (Shimizu et al , ; de Raad et al , ) was used to express the enzymes and examine conversions of D‐mannose to D‐fructose (a primary activity; Itoh et al , ) and D‐lyxose to D‐xylulose (side activity) (Fig A, Appendix Fig S5). The ratios of the turnover rates of D‐lyxose to the turnover rates of D‐mannose were calculated and compared (Fig B).…”

Enzyme promiscuity shapes evolutionary innovation and optimization

“…The activities of the wild-type YihS and three mutant YihS enzymes (YihS R315S, YihS V314L + R315C, and YihS V314L + R315S) were tested in vitro. A cell-free in vitro transcription and translation system[32, 33] was used to express the enzymes and examine conversions of D-mannose to D-fructose (a primary activity[34]) and D-lyxose to D-xylulose (side activity) (Fig. 2A, Fig.…”