Presentation Information
[P01-018]Efficient circular DNA replication by design of SSB and DnaG
○Teppei Deguchi1, Yuta Yamagishi1, Masayuki Su’etsugu1 (1. Rikkyo university (Japan))
Keywords:
DNA replication,reconstitution system,protein design,machine learning
Replication Cycle Reaction (RCR) is an in vitro system that enables the exponential amplification of circular DNA [1]. It can generate long DNA products up to 2 Mb [2] and has broad applications in synthetic biology. SSB and DnaG proteins are integral components of RCR and are critical for its function. The single-strand DNA-binding protein (SSB) plays an essential role in DNA replication, repair, and recombination by stabilizing single-stranded DNA and interacting with other proteins. DnaG is a primase responsible for synthesizing RNA primers during DNA replication.
In this study, we aim to improve the replication efficiency of RCR through the design of SSB and DnaG. This approach combines computational variant effect prediction with functional evaluation based on RCR activity. Machine learning–based methods, including protein language model [3], are used to identify variants that enhance RCR replication efficiency. Candidate variants are pre-screened based on prediction results to reduce experimental cost. In addition, We evaluate the RCR activity of the variants based on DNA amplification levels.
We have conducted computational prediction and developed an RCR-based evaluation of SSB and DnaG variants. Both will be integrated to find SSB and DnaG variant which enhance efficient DNA replication in RCR. The enhanced RCR system is expected to enable a wide range of applications in synthetic biology.
[1] Su'etsugu et al., NAR, 2017. [2] Fujita et al., ACS Synth Biol, 2022. [3] Rives et al., PNAS, 2021.
In this study, we aim to improve the replication efficiency of RCR through the design of SSB and DnaG. This approach combines computational variant effect prediction with functional evaluation based on RCR activity. Machine learning–based methods, including protein language model [3], are used to identify variants that enhance RCR replication efficiency. Candidate variants are pre-screened based on prediction results to reduce experimental cost. In addition, We evaluate the RCR activity of the variants based on DNA amplification levels.
We have conducted computational prediction and developed an RCR-based evaluation of SSB and DnaG variants. Both will be integrated to find SSB and DnaG variant which enhance efficient DNA replication in RCR. The enhanced RCR system is expected to enable a wide range of applications in synthetic biology.
[1] Su'etsugu et al., NAR, 2017. [2] Fujita et al., ACS Synth Biol, 2022. [3] Rives et al., PNAS, 2021.
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