ACS Synthetic BiologyBioengineeringPeer-reviewed journal article
A cyanobacterial screening platform for Rubisco mutant variants
Ute A. HoffmanniD, Anna Zoey SchuppeiD, Axel KnaveiD, Emil SporreiD, Hjalmar BrismariD, Elias EnglundiD, Per‐Olof SyréniD, Elton Paul HudsoniD
Abstract
Rubisco is the main entry point of inorganic carbon into the biosphere and a central player in the global carbon system. Its relatively low catalytic constant as well as its tendency to also accept O2 as a substrate have made it a common target of enzyme engineering. We have developed an enzyme engineering and screening platform for Rubisco using the model cyanobacterium Synechocystis sp. PCC 6803. Starting with the Form II Rubisco from Gallionella, we first show that the enzyme can replace the native Form I Rubisco in Synechocystis and that growth rates become sensitive to CO2 and O2 levels. We address the challenge of designing a zero-shot input library, without prior experimental knowledge, by coupling the phylogenetically-guided model EVmutation with "in silico evolution". Starting with this targeted mutagenesis library, we used competitive growth coupled to deep sequencing to compare the properties of Rubisco protein variants under different cultivation conditions. We identified an amino acid exchange which increased the thermostability of Gallionella Rubisco and conveyed resilience to detrimental exchanges. The establishment of this platform is a first step towards high-throughput screening of Rubisco variants in Synechocystis and creating optimized enzyme variants to accelerate the Calvin-Benson-Bassham cycle in cyanobacteria and possibly chloroplasts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/633911v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@b0e549org.highwire.dtl.DTLVardef@19d2a56org.highwire.dtl.DTLVardef@1468557org.highwire.dtl.DTLVardef@dc3e7e_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: C. elegans
- assay: proteomics
- research focus: protein folding & dynamics
- approach: deep learning
- Citations
- 10
- FWCI
- 2.98
- Field percentile
- top 8%
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