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Benchmark Analysis of Native and Artificial NAD+-Dependent Enzymes Generated by a Sequence-Based Design Method with or without Phylogenetic Data.

Shogo NakanoTomoharu MotoyamaYurina MiyashitaYuki IshizukaNaoya MatsuoHiroaki TokiwaSuguru ShinodaYasuhisa AsanoSohei Ito
Published in: Biochemistry (2018)
The expansion of protein sequence databases has enabled us to design artificial proteins by sequence-based design methods, such as full-consensus design (FCD) and ancestral-sequence reconstruction (ASR). Artificial proteins with enhanced activity levels compared with native ones can potentially be generated by such methods, but successful design is rare because preparing a sequence library by curating the database and selecting a method is difficult. Utilizing a curated library prepared by reducing conservation energies, we successfully designed two artificial l-threonine 3-dehydrogenases (SDR-TDH) with higher activity levels than native SDR-TDH, FcTDH-N1, and AncTDH, using FCD and ASR, respectively. The artificial SDR-TDHs had excellent thermal stability and NAD+ recognition compared to native SDR-TDH from Cupriavidus necator (CnTDH); the melting temperatures of FcTDH-N1 and AncTDH were about 10 and 5 °C higher than that of CnTDH, respectively, and the dissociation constants toward NAD+ of FcTDH-N1 and AncTDH were 2- and 7-fold lower than that of CnTDH, respectively. Enzymatic efficiency of the artificial SDR-TDHs were comparable to that of CnTDH. Crystal structures of FcTDH-N1 and AncTDH were determined at 2.8 and 2.1 Å resolution, respectively. Structural and MD simulation analysis of the SDR-TDHs indicated that only the flexibility at specific regions was changed, suggesting that multiple mutations introduced in the artificial SDR-TDHs altered their flexibility and thereby affected their enzymatic properties. Benchmark analysis of the SDR-TDHs indicated that both FCD and ASR can generate highly functional proteins if a curated library is prepared appropriately.
Keyphrases
  • amino acid
  • high resolution
  • molecular dynamics
  • small molecule
  • clinical practice
  • density functional theory
  • artificial intelligence
  • protein protein
  • data analysis