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A matching algorithm for catalytic residue site selection in computational enzyme design.

ABSTRACT: A loop closure-based sequential algorithm, PRODA_MATCH, was developed to match catalytic residues onto a scaffold for enzyme design in silico. The computational complexity of this algorithm is polynomial with respect to the number of active sites, the number of catalytic residues, and the maximal iteration number of cyclic coordinate descent steps. This matching algorithm is independent of a rotamer library that enables the catalytic residue to take any required conformation during the reaction coordinate. The catalytic geometric parameters defined between functional groups of transition state (TS) and the catalytic residues are continuously optimized to identify the accurate position of the TS. Pseudo-spheres are introduced for surrounding residues, which make the algorithm take binding into account as early as during the matching process. Recapitulation of native catalytic residue sites was used as a benchmark to evaluate the novel algorithm. The calculation results for the test set show that the native catalytic residue sites were successfully identified and ranked within the top 10 designs for 7 of the 10 chemical reactions. This indicates that the matching algorithm has the potential to be used for designing industrial enzymes for desired reactions.

SUBMITTER: Lei Y

PROVIDER: S-EPMC3190151 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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A matching algorithm for catalytic residue site selection in computational enzyme design.

Lei Yulin Y Luo Wenjia W Zhu Yushan Y

Protein science : a publication of the Protein Society 20110729 9

A loop closure-based sequential algorithm, PRODA_MATCH, was developed to match catalytic residues onto a scaffold for enzyme design in silico. The computational complexity of this algorithm is polynomial with respect to the number of active sites, the number of catalytic residues, and the maximal iteration number of cyclic coordinate descent steps. This matching algorithm is independent of a rotamer library that enables the catalytic residue to take any required conformation during the reaction ...[more]

PMID: 21714026

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Project description: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.

Project description:Collection of accurate and representative data from agricultural fields is required for efficient crop management. Since growers have limited available resources, there is a need for advanced methods to select representative points within a field in order to best satisfy sampling or sensing objectives. The main purpose of this work was to develop a data-driven method for selecting locations across an agricultural field given observations of some covariates at every point in the field. These chosen locations should be representative of the distribution of the covariates in the entire population and represent the spatial variability in the field. They can then be used to sample an unknown target feature whose sampling is expensive and cannot be realistically done at the population scale. An algorithm for determining these optimal sampling locations, namely the multifunctional matching (MFM) criterion, was based on matching of moments (functionals) between sample and population. The selected functionals in this study were standard deviation, mean, and Kendall's tau. An additional algorithm defined the minimal number of observations that could represent the population according to a desired level of accuracy. The MFM was applied to datasets from two agricultural plots: a vineyard and a peach orchard. The data from the plots included measured values of slope, topographic wetness index, normalized difference vegetation index, and apparent soil electrical conductivity. The MFM algorithm selected the number of sampling points according to a representation accuracy of 90% and determined the optimal location of these points. The algorithm was validated against values of vine or tree water status measured as crop water stress index (CWSI). Algorithm performance was then compared to two other sampling methods: the conditioned Latin hypercube sampling (cLHS) model and a uniform random sample with spatial constraints. Comparison among sampling methods was based on measures of similarity between the target variable population distribution and the distribution of the selected sample. MFM represented CWSI distribution better than the cLHS and the uniform random sampling, and the selected locations showed smaller deviations from the mean and standard deviation of the entire population. The MFM functioned better in the vineyard, where spatial variability was larger than in the orchard. In both plots, the spatial pattern of the selected samples captured the spatial variability of CWSI. MFM can be adjusted and applied using other moments/functionals and may be adopted by other disciplines, particularly in cases where small sample sizes are desired.

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Dataset Information

A matching algorithm for catalytic residue site selection in computational enzyme design.

Publications

A matching algorithm for catalytic residue site selection in computational enzyme design.

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