Feng Geng1, Cheng-Wei Ma1, An-Ping Zeng2. 1. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Denickestrasse 15, 21073, Hamburg, Germany. 2. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Denickestrasse 15, 21073, Hamburg, Germany. aze@tuhh.de.
Abstract
OBJECTIVE: To re-engineer the active site of proteins for non-natural substrates using a position-based prediction method (PBPM). RESULTS: The approach has been applied to re-engineer the E. coli glutamate dehydrogenase to alter its substrate from glutamate to homoserine for a de novo 1,3-propanediol biosynthetic pathway. After identification of key residues that determine the substrate specificity, residue K92 was selected as a candidate site for mutation. Among the three mutations (K92V, K92C, and K92M) suggested by PBPM, the specific activity of the best mutant (K92 V) was increased from 171 ± 35 to 1328 ± 71 μU mg-1. CONCLUSION: The PBPM approach has a high efficiency for re-engineering the substrate specificity of natural enzymes for new substrates.
OBJECTIVE: To re-engineer the active site of proteins for non-natural substrates using a position-based prediction method (PBPM). RESULTS: The approach has been applied to re-engineer the E. coliglutamate dehydrogenase to alter its substrate from glutamate to homoserine for a de novo 1,3-propanediol biosynthetic pathway. After identification of key residues that determine the substrate specificity, residue K92 was selected as a candidate site for mutation. Among the three mutations (K92V, K92C, and K92M) suggested by PBPM, the specific activity of the best mutant (K92 V) was increased from 171 ± 35 to 1328 ± 71 μU mg-1. CONCLUSION: The PBPM approach has a high efficiency for re-engineering the substrate specificity of natural enzymes for new substrates.