Dataset Information

Construction and in vivo assembly of a catalytically proficient and hyperthermostable de novo enzyme.

ABSTRACT: Although catalytic mechanisms in natural enzymes are well understood, achieving the diverse palette of reaction chemistries in re-engineered native proteins has proved challenging. Wholesale modification of natural enzymes is potentially compromised by their intrinsic complexity, which often obscures the underlying principles governing biocatalytic efficiency. The maquette approach can circumvent this complexity by combining a robust de novo designed chassis with a design process that avoids atomistic mimicry of natural proteins. Here, we apply this method to the construction of a highly efficient, promiscuous, and thermostable artificial enzyme that catalyzes a diverse array of substrate oxidations coupled to the reduction of H₂O₂. The maquette exhibits kinetics that match and even surpass those of certain natural peroxidases, retains its activity at elevated temperature and in the presence of organic solvents, and provides a simple platform for interrogating catalytic intermediates common to natural heme-containing enzymes.Catalytic mechanisms of enzymes are well understood, but achieving diverse reaction chemistries in re-engineered proteins can be difficult. Here the authors show a highly efficient and thermostable artificial enzyme that catalyzes a diverse array of substrate oxidations coupled to the reduction of H₂O₂.

SUBMITTER: Watkins DW

PROVIDER: S-EPMC5572459 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Construction and in vivo assembly of a catalytically proficient and hyperthermostable de novo enzyme.

Watkins Daniel W DW Jenkins Jonathan M X JMX Grayson Katie J KJ Wood Nicola N Steventon Jack W JW Le Vay Kristian K KK Goodwin Matthew I MI Mullen Anna S AS Bailey Henry J HJ Crump Matthew P MP MacMillan Fraser F Mulholland Adrian J AJ Cameron Gus G Sessions Richard B RB Mann Stephen S Anderson J L Ross JLR

Nature communications 20170825 1

Although catalytic mechanisms in natural enzymes are well understood, achieving the diverse palette of reaction chemistries in re-engineered native proteins has proved challenging. Wholesale modification of natural enzymes is potentially compromised by their intrinsic complexity, which often obscures the underlying principles governing biocatalytic efficiency. The maquette approach can circumvent this complexity by combining a robust de novo designed chassis with a design process that avoids ato ...[more]

PMID: 28842561

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Project description:MotivationThere are very few methods for de novo genome assembly based on the overlap graph approach. It is considered as giving more exact results than the so-called de Bruijn graph approach but in much greater time and of much higher memory usage. It is not uncommon that assembly methods involving the overlap graph model are not able to successfully compute greater datasets, mainly due to memory limitation of a computer. This was the reason for developing in last decades mainly de Bruijn-based assembly methods, fast and fairly accurate. However, the latter methods can fail for longer or more repetitive genomes, as they decompose reads to shorter fragments and lose a part of information. An efficient assembler for processing big datasets and using the overlap graph model is still looked out.ResultsWe propose a new genome-scale de novo assembler based on the overlap graph approach, designed for short-read sequencing data. The method, ALGA, incorporates several new ideas resulting in more exact contigs produced in short time. Among these ideas, we have creation of a sparse but quite informative graph, reduction of the graph including a procedure referring to the problem of minimum spanning tree of a local subgraph, and graph traversal connected with simultaneous analysis of contigs stored so far. What is rare in genome assembly, the algorithm is almost parameter-free, with only one optional parameter to be set by a user. ALGA was compared with nine state-of-the-art assemblers in tests on genome-scale sequencing data obtained from real experiments on six organisms, differing in size, coverage, GC content and repetition rate. ALGA produced best results in the sense of overall quality of genome reconstruction, understood as a good balance between genome coverage, accuracy and length of resulting sequences. The algorithm is one of tools involved in processing data in currently realized national project Genomic Map of Poland.Availability and implementationALGA is available at http://alga.put.poznan.pl.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Construction and in vivo assembly of a catalytically proficient and hyperthermostable de novo enzyme.

Publications

Construction and in vivo assembly of a catalytically proficient and hyperthermostable de novo enzyme.

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