Screening of large protein libraries by the ?cell immobilized on adsorbed bead? approach
- 20 April 2004
- journal article
- research article
- Published by Wiley in Biotechnology & Bioengineering
- Vol. 86 (2) , 196-200
- https://doi.org/10.1002/bit.10883
Abstract
Screening of mutant libraries for in vitro enzyme evolution is carried out primarily by physical separation of the cells, followed by growth of individual clones and screening of biocatalytic activity on the basis of color or fluorescence signal development. Currently, most frequently employed methods are labor-intensive or require robotic equipment, resulting in screening limited to a relatively small fraction of the potential inherent in a given library. In this study we present a design, development, and feasibility demonstration of a new screening approach, providing convenient handling of large libraries consisting of 106 to 107 clones and screening based on a simultaneous enzymatic assay with commercially available substrates. This new screening method is based on the "cell immobilized on adsorbed bead" approach: the cell population to be screened is mixed with an excess of medium pre-equilibrated polyacrylamide beads, chemically derivatized to affect quantitative cell immobilization by adsorption. The resulting bead population, comprising of single cell on a bead or blank beads, is then immobilized on a solid glass support. After removal of the freely flowing liquid, the cells immobilized on the adsorbed beads are allowed to grow into microcolonies, utilizing the medium retained within the supporting hydrogel matrix. These colonies are subsequently equilibrated with chromogenic or fluorogenic substrate and screening is affected under a stereomicroscope, resulting in readily retrieved of the most active colonies. This technique may be particularly useful when the screened mutants are expressed and displayed on the cell surface, providing an active and homogeneous "naturally immobilized" enzyme population with minimal substrate diffusion limitations.Keywords
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