bioRxivMolecular BiologyPreprint
Boosting N-terminally anchored yeast surface display via structural insights into S. cerevisiae Pir proteins
Tea Martinić CezariD, Mateja LozančićiD, Ana NovačićiD, Ana Matičević, Dominik Matijević, Béatrice ValléeiD, Vladimir MršaiD, Renata TeparićiD, Bojan ŽunariD
Abstract
Surface display co-opts yeasts innate ability to embellish its cell wall with mannoproteins, thus converting the yeasts outer surface into a growing and self-sustaining catalyst. However, the efficient toolbox for converting the enzyme of interest into its surface-displayed isoform is currently lacking, especially if the isoform needs to be anchored to the cell wall near the isoforms N-terminus. Aiming to advance such N-terminally anchored surface display, we employed in silico and machine-learning strategies to study the 3D structure, function, genomic organisation, and evolution of the Pir protein family, whose members evolved to covalently attach themselves near their N-terminus to the {beta}-1,3-glucan of the cell wall. Through the newly-gained insights, we rationally engineered 14 S. cerevisiae Hsp150 (Pir2)-based fusion proteins. We quantified their performance, uncovering guidelines for efficient yeast surface display while developing a construct that promoted a 2.5-fold more efficient display than the full-length Hsp150 and a Pir-tag, i.e., a peptide spanning only 4.5 kDa but promoting as efficient surface display as the full-length Hsp150. These constructs fortify the existing surface display toolbox, allowing for a prompt and routine refitting of any protein into its N-terminally anchored isoform. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/538238v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@909e7aorg.highwire.dtl.DTLVardef@9480ceorg.highwire.dtl.DTLVardef@1945bdcorg.highwire.dtl.DTLVardef@11ae505_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: yeast
- assay: protein structure
- research focus: protein design
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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