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.
Highlights
Designing protein active sites to catalyze chemical reactions with a designated substrate is a grand challenge of chemical biology, and the ability to design effective enzymes is ultimatePLOS ONE | DOI:10.1371/journal.pone.0156559 May 31, 2016Scaffold Selection for Enzyme Design proof of understanding enzyme catalysis [1]
The root mean standard deviations (RMSDs) of the matched transition state (TS) for ten scaffolds from those in the crystal structures are less than 1.0 Å, as shown in Table 1, which is much better than the results obtained by the minimal active site model
We proposed the concept of a complex active site model to capture the major defining characteristics of the active site preorganization towards a target reaction, and the native active sites in a benchmark set were reproduced accurately based on this model with aid of an improved version of our matching algorithm ProdaMatch for enzyme design
Summary
Designing protein active sites to catalyze chemical reactions with a designated substrate is a grand challenge of chemical biology, and the ability to design effective enzymes is ultimatePLOS ONE | DOI:10.1371/journal.pone.0156559 May 31, 2016Scaffold Selection for Enzyme Design proof of understanding enzyme catalysis [1]. In most cases, enzymes isolated from natural sources are not optimal for the desired chemical transformations, or enzymes may not exist for the non-biological reactions required. A promising approach to create protein catalysts that can carry out various chemical reactions is computational enzyme design [8], which can create active sites using any type of scaffold and the low activities of the computational-designed enzymes can be amplified by directed evolution. Such advantages have helped pioneering investigators construct enzyme catalysts for several representative chemical reactions [9]
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