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An Automated Data-Driven Pipeline for Improving Heterologous Enzyme Expression.

ABSTRACT: Enzymes are the ultimate entities responsible for chemical transformations in natural and engineered biosynthetic pathways. However, many natural enzymes suffer from suboptimal functional expression due to poor intrinsic protein stability. Further, stability enhancing mutations often come at the cost of impaired function. Here we demonstrate an automated protein engineering strategy for stabilizing enzymes while retaining catalytic function using deep mutational scanning coupled to multiple-filter based screening and combinatorial mutagenesis. We validated this strategy by improving the functional expression of a Type III polyketide synthase from the Atropa belladonna biosynthetic pathway for tropane alkaloids. The best variant had a total of 8 mutations with over 25-fold improved activity over wild-type in E. coli cell lysates, an improved melting temperature of 11.5 ± 0.6 °C, and only minimal reduction in catalytic efficiency. We show that the multiple-filter approach maintains acceptable sensitivity with homology modeling structures up to 4 Å RMS. Our results highlight an automated protein engineering tool for improving the stability and solubility of difficult to express enzymes, which has impact for biotechnological applications.

SUBMITTER: Wrenbeck EE

PROVIDER: S-EPMC6855305 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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An Automated Data-Driven Pipeline for Improving Heterologous Enzyme Expression.

Wrenbeck Emily E EE Bedewitz Matthew A MA Klesmith Justin R JR Noshin Syeda S Barry Cornelius S CS Whitehead Timothy A TA

ACS synthetic biology 20190208 3

Enzymes are the ultimate entities responsible for chemical transformations in natural and engineered biosynthetic pathways. However, many natural enzymes suffer from suboptimal functional expression due to poor intrinsic protein stability. Further, stability enhancing mutations often come at the cost of impaired function. Here we demonstrate an automated protein engineering strategy for stabilizing enzymes while retaining catalytic function using deep mutational scanning coupled to multiple-filt ...[more]

PMID: 30721031

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Project description:BACKGROUNDResearch involving microbial ecosystems has drawn increasing attention in recent years. Studying microbe-microbe, host-microbe, and environment-microbe interactions are essential for the understanding of microbial ecosystems. Currently, metaproteomics provide qualitative and quantitative information of proteins, providing insights into the functional changes of microbial communities. However, computational analysis of large-scale data generated in metaproteomic studies remains a challenge. Conventional proteomic software have difficulties dealing with the extreme complexity and species diversity present in microbiome samples leading to lower rates of peptide and protein identification. To address this issue, we previously developed the MetaPro-IQ approach for highly efficient microbial protein/peptide identification and quantification.RESULTHere, we developed an integrated software platform, named MetaLab, providing a complete and automated, user-friendly pipeline for fast microbial protein identification, quantification, as well as taxonomic profiling, directly from mass spectrometry raw data. Spectral clustering adopted in the pre-processing step dramatically improved the speed of peptide identification from database searches. Quantitative information of identified peptides was used for estimating the relative abundance of taxa at all phylogenetic ranks. Taxonomy result files exported by MetaLab are fully compatible with widely used metagenomics tools. Herein, the potential of MetaLab is evaluated by reanalyzing a metaproteomic dataset from mouse gut microbiome samples.CONCLUSIONMetaLab is a fully automatic software platform enabling an integrated data-processing pipeline for metaproteomics. The function of sample-specific database generation can be very advantageous for searching peptides against huge protein databases. It provides a seamless connection between peptide determination and taxonomic profiling; therefore, the peptide abundance is readily used for measuring the microbial variations. MetaLab is designed as a versatile, efficient, and easy-to-use tool which can greatly simplify the procedure of metaproteomic data analysis for researchers in microbiome studies.

Dataset Information

An Automated Data-Driven Pipeline for Improving Heterologous Enzyme Expression.

Publications

An Automated Data-Driven Pipeline for Improving Heterologous Enzyme Expression.

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