Dataset Information

Molecular Engineering of Robustness and Resilience in Enzymatic Reaction Networks.

ABSTRACT: Living systems rely on complex networks of chemical reactions to control the concentrations of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. One of the greatest challenges in chemistry is the creation of such functionality from chemical reactions. A key limitation is our lack of understanding of how molecular structure impacts on the dynamics of chemical reaction networks, preventing the design of networks that are robust (i.e., function in a large parameter space) and resilient (i.e., reach their out-of-equilibrium function rapidly). Here we demonstrate that reaction rates of individual reactions in the network can control the dynamics by which the system reaches limit cycle oscillations, thereby gaining information on the key parameters that govern the dynamics of these networks. We envision that these principles will be incorporated into the design of network motifs, enabling chemists to develop "molecular software" to create functional behavior in chemical systems.

SUBMITTER: Wong ASY

PROVIDER: S-EPMC5481813 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Publications

Molecular Engineering of Robustness and Resilience in Enzymatic Reaction Networks.

Wong Albert S Y ASY Pogodaev Aleksandr A AA Vialshin Ilia N IN Helwig Britta B Huck Wilhelm T S WTS

Journal of the American Chemical Society 20170613 24

Living systems rely on complex networks of chemical reactions to control the concentrations of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. One of the greatest challenges in chemistry is the creation of such functionality from chemical reactions. A key limitation is our lack of understanding of how molecular structure impacts on the dynamics of ch ...[more]

PMID: 28582616

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Project description:Cell-free synthetic biology system organizes multiple enzymes (parts) from different sources to implement unnatural catalytic functions. Highly adaption between the catalytic parts is crucial for building up efficient artificial biosynthetic systems. Protein engineering is a powerful technology to tailor various enzymatic properties including catalytic efficiency, substrate specificity, temperature adaptation and even achieve new catalytic functions. However, altering enzymatic pH optimum still remains a challenging task. In this study, we proposed a novel sequence homolog-based protein engineering strategy for shifting the enzymatic pH optimum based on statistical analyses of sequence-function relationship data of enzyme family. By two statistical procedures, artificial neural networks (ANNs) and least absolute shrinkage and selection operator (Lasso), five amino acids in GH11 xylanase family were identified to be related to the evolution of enzymatic pH optimum. Site-directed mutagenesis of a thermophilic xylanase from Caldicellulosiruptor bescii revealed that four out of five mutations could alter the enzymatic pH optima toward acidic condition without compromising the catalytic activity and thermostability. Combination of the positive mutants resulted in the best mutant M31 that decreased its pH optimum for 1.5 units and showed increased catalytic activity at pH < 5.0 compared to the wild-type enzyme. Structure analysis revealed that all the mutations are distant from the active center, which may be difficult to be identified by conventional rational design strategy. Interestingly, the four mutation sites are clustered at a certain region of the enzyme, suggesting a potential "hot zone" for regulating the pH optima of xylanases. This study provides an efficient method of modulating enzymatic pH optima based on statistical sequence analyses, which can facilitate the design and optimization of suitable catalytic parts for the construction of complicated cell-free synthetic biology systems.

Dataset Information

Molecular Engineering of Robustness and Resilience in Enzymatic Reaction Networks.

Publications

Molecular Engineering of Robustness and Resilience in Enzymatic Reaction Networks.

Similar Datasets

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