Building bugs

Amir Aharoni, Department of Life Sciences and the National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University, Beer-Sheva, Israel. The size of the microbial world is beyond our imagination. In last year’s crystal ball article, Tom Curtis compared the size of the microbial world to the size of the universe pointing out that the number of microorganisms in the world is billions of times larger than the number of stars in the sky (Curtis, 2007). There is little doubt that such unlimited microbial biodiversity holds great promise for enriching our repertoire of known enzymes, with many novel enzymes awaiting discovery. The development of new sophisticated sequencing technologies, including the 454 pyrosequencer and Solexa technology, allow for accumulation of extremely large amounts of sequence information. Such rapidly accumulated information will allow microbiologists, bioinformaticians and system biologists to perform extensive surveys on microbial lineages, lead to the description of new metabolic pathways and allow for the identification of new regulatory mechanisms. These data also represent an invaluable resource in our quest for new enzymes. Still the question of how do we actually find these precious needles in the large haystack arises. Namely, what will be the best way to find new enzymes in the huge space that is the microbial world? The discovery of new enzymes is of ultimate importance for fundamental knowledge regarding enzyme evolution, enzyme structure-function relationships, basic mechanisms of enzyme catalysis and even for the identification of novel protein folds. Novel enzymes could also serve as corner stones in catalysing industrial chemical synthesis reactions, thus serving as ‘clean’ alternatives for producing chemicals by large-scale chemical synthesis. Currently, new highly robust enzymes are urgently needed as biocatalysts in many different industries, including the pharmaceutical and agricultural industries. The dream of significantly expanding the repertoire of known enzymes, both for research and industrial applications, is currently the subject of intensive research and will keep many scientists busy in years to come (Ferrer et al., 2007). One of the main methods for the identification of novel enzymes is by virtual sequence homology screens. Using this approach, new enzymes are identified by comparing vast number of sequences from genomic/metagenomic sources to the sequences of known enzymes (Ferrer et al., 2007). In this process, the immense repertoire of millions of proteins predicted from the microbial sequence database is compared with known enzymes to identify new homologues. One of the obvious limitations of such an approach is that it exclusively relies on existing gene annotations that are difficult to predict and often prone to errors due to their reliance on machine-based techniques (Hallin et al., 2008). No doubt that improved annotation will enable more accurate gene function predictions from microbial sequencing data. However, the main conceptual drawback of such an approach is that truly novel enzymes that are only remotely related to known existing enzymes will never be found. Genetic drift can significantly alter any obvious homology between functionally similar enzymes, thereby restricting the search to only related enzymes with similar sequences. An unbiased manner to mine natural microbial biodiversity for new enzymes is by functional screening for the desired enzymatic activity. Such direct screening for new enzymes will allow the identification of novel enzymes that are not related by sequence homology to any other known enzyme. A prerequisite for any functional screening procedure is the availability of high-quality genomic/ metagenomic libraries and the use of an adequate host organism that is able to express the target genes to yield functional proteins. Currently, most functional screens rely on spatial separation between the different samples either on agar plates (by direct screening of colonies) or using microtitre plates. To perform such screens for large libraries, access to heavy robotic systems usually accessible only for large laboratories, is a prerequisite. Even with the aid of robotics, however, the number of genes that can be screened is on the order of ~10. Given the unlimited amount of diversity in the microbial world, this number will allow sampling of only a tiny fraction of the functional enzymes out there awaiting to be discovered. To allow for a more efficient route for functional mining of new enzymes, we must adopt high-throughput approaches that will allow us to rapidly screen 10–10 samples. These numbers are clearly far beyond reach of Microbial Biotechnology (2009) 2(2), 128–156 doi:10.1111/j.1751-7915.2009.00090.x