bioRxivBiochemistryPreprint
Full-Atom MPNN Based Redesign of Plant Dehydrogenase Enables Thermostability Enhancement Without Loss of Stereoselectivity
Bruno Di GeronimoiD, Jasmin ZusoniD, Ana Udženija, Andrea M. ChániqueiD, Robert KouristiD, Shina Caroline Lynn KamerliniD
Abstract
Protein stabilization is a "Holy Grail" of biocatalysis, and stability design is an area of intense research interest. While it is increasingly feasible to effectively increase enzyme thermostability, optimization without compromising activity or selectivity remains a significant challenge. Here, we use full-atom protein sequence design with sidechain conditioning (FAMPNN) to engineer thermostable variants of the borneol dehydrogenase from Salvia rosmarinus (SrBDH1), an enzyme from a family where unselective enzymes dominate, and selectivity is determined by dynamical considerations. By combining FAMPNN design with residue conservation analysis and avoiding active site residues, we were able to computationally design SrBDH1 variants with up to 10 {degrees}C enhanced thermostability and strongly increased half-life time at elevated temperature, while retaining selectivity towards (+)-borneol. This design framework, integrating de novo and physics-based protein design tools, demonstrates that stability can be enhanced without disrupting functionally relevant dynamics, providing a route to engineer robust and selective biocatalysts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/719482v1_ufig1.gif" ALT="Figure 1"> View larger version (97K): org.highwire.dtl.DTLVardef@1a35073org.highwire.dtl.DTLVardef@f6c56dorg.highwire.dtl.DTLVardef@11b965forg.highwire.dtl.DTLVardef@2d6eef_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG
- organism: plant (other)
- assay: protein sequence
- research focus: protein design
- research focus: protein folding & dynamics
- approach: deep learning
- approach: graph neural network
- Citations
- 0
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