title: Use of an Improved Matching Algorithm to Select Scaffolds for Enzyme Design Based on a Complex Active Site Model.
abstract: Active site preorganization helps native enzymes electrostatically stabilize the transition state better than the ground state for their primary substrates and achieve significant rate enhancement. In this report, we hypothesize that a complex active site model for active site preorganization modeling should help to create preorganized active site design and afford higher starting activities towards target reactions. Our matching algorithm ProdaMatch was improved by invoking effective pruning strategies and the native active sites for ten scaffolds in a benchmark test set were reproduced. The root-mean squared deviations between the matched transition states and those in the crystal structures were < 1.0 Å for the ten scaffolds, and the repacking calculation results showed that 91% of the hydrogen bonds within the active sites are recovered, indicating that the active sites can be preorganized based on the predicted positions of transition states. The application of the complex active site model for de novo enzyme design was evaluated by scaffold selection using a classic catalytic triad motif for the hydrolysis of p-nitrophenyl acetate. Eighty scaffolds were identified from a scaffold library with 1,491 proteins and four scaffolds were native esterase. Furthermore, enzyme design for complicated substrates was investigated for the hydrolysis of cephalexin using scaffold selection based on two different catalytic motifs. Only three scaffolds were identified from the scaffold library by virtue of the classic catalytic triad-based motif. In contrast, 40 scaffolds were identified using a more flexible, but still preorganized catalytic motif, where one scaffold corresponded to the α-amino acid ester hydrolase that catalyzes the hydrolysis and synthesis of cephalexin. Thus, the complex active site modeling approach for de novo enzyme design with the aid of the improved ProdaMatch program is a promising approach for the creation of active sites with high catalytic efficiencies towards target reactions.
authors.affiliation: Department of Chemical Engineering, Tsinghua University, Beijing 100084, P.R. China.
authors.forename: Xiaoqiang
authors.initials: X
authors.lastname: Huang
authors.affiliation: Department of Chemical Engineering, Tsinghua University, Beijing 100084, P.R. China.
authors.forename: Jing
authors.initials: J
authors.lastname: Xue
authors.affiliation: Department of Chemical Engineering, Tsinghua University, Beijing 100084, P.R. China.
authors.forename: Min
authors.initials: M
authors.lastname: Lin
authors.affiliation: Department of Chemical Engineering, Tsinghua University, Beijing 100084, P.R. China.
authors.forename: Yushan
authors.initials: Y
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D002265:Carboxylic Ester Hydrolases / Q000378:metabolism; D002384:Catalysis; D020134:Catalytic Domain / Q000502:physiology*; D002506:Cephalexin / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D006868:Hydrolysis; D008958:Models, Molecular; D011487:Protein Conformation; D015202:Protein Engineering / Q000379:methods*
chemical_list: D002265:Carboxylic Ester Hydrolases; C058058:alpha-amino acid esterase; D002506:Cephalexin