Natural Biocatalyst Sampling and Screening

Microorganisms have been exposed to a myriad of substrates and environmental conditions throughout evolution resulting in countless metabolites and enzymatic activities. Therefore, it worth the time to identify new microbial biocatalysts by screening a multitude of organisms isolated from an environmental sample for a desired enzymatic activity. Since most microorganisms from nature can not be cultured in the lab, DNA-based, culture-independent approaches have now become the state-of-the-art in biocatalyst discovery. These methods rely on library-based screening efforts, where an expression library from environmental DNA is screened for a certain activity.

Latest techniques are focusing on (i) screen method development to identify commercially interesting enzymatic activities in bacterial cells, (ii) approaches to improve natural biocatalysts, and (iii) the development of non-conventional media. To search for the interested biocatalysts from nature, various metagenomic strategies have been developed, for example: sequence-based metagenomic approaches that look for enzymes homologous to known biocatalysts; PCR-based methods that use primers designed according to conserved regions of known enzymes; or by functional metagenomics, where metagenomic libraries are built and screened using DNA cloned directly from environmental metagenomes. On the other hand, natural biocatalysts can be improved with techniques such as recombinant DNA, metabolic engineering and combinatorial biosynthesis.

Creative Enzymes offers high-throughput screening and microbiological techniques to select the suitable natural biocatalysts for custom requirements, as well as strategies to improve the target. Enabled by our best knowledge of metaproteomics and a broad database, we offer professional one-stop services:

• Metagenomic strategy design.
• High-throughput screening for natural biocatalysts.
• Natural biocatalyst optimization.
• Alternative non-conventional culture.

Figure 1. An example of a functional metaproteomics workflow
(Microbiome, 2017)