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Raman hyperspectral imaging with multivariate analysis for investigating enzyme immobilization

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Abstract

Directed enzyme evolution has led to significant application of biocatalysis for improved chemical transformations throughout the scientific and industrial communities. Biocatalytic reactions utilizing evolved enzymes immobilized within microporous supports have realized unique advantages, including notably higher enzyme stability, higher enzyme load, enzyme reusability, and efficient product-enzyme separation. To date, limited analytical methodology is available to discern the spatial and chemical distribution of immobilized enzymes, in which techniques for surface visualization, enzyme stability, or activity are instead employed. New analytical tools to investigate enzyme immobilization are therefore needed. In this work, development, application, and evaluation of an analytical methodology to study enzyme immobilization is presented. Specifically, Raman hyperspectral imaging with principal component analysis, a multivariate method, is demonstrated for the first time to investigate evolved enzymes immobilized onto microporous supports for biocatalysis. Herein we demonstrate the ability to spatially and spectrally resolve evolved pantothenate kinase (PanK) immobilized onto two commercially-available, chemically-diverse porous resins. This analytical methodology is able to chemically distinguish evolved enzyme, resin, and chemical species pertinent to immobilization. As such, a new analytical approach to study immobilized biocatalysts is demonstrated, offering potential wide application for analysis of protein or biomolecule immobilization.

Graphical abstract: Raman hyperspectral imaging with multivariate analysis for investigating enzyme immobilization

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Article information


Submitted
22 Jun 2020
Accepted
27 Sep 2020
First published
29 Sep 2020

Analyst, 2020, Advance Article
Article type
Paper

Raman hyperspectral imaging with multivariate analysis for investigating enzyme immobilization

J. P. Smith, M. Liu, M. L. Lauro, M. Balasubramanian, J. H. Forstater, S. T. Grosser, Z. E. X. Dance, T. A. Rhodes, X. Bu and K. S. Booksh, Analyst, 2020, Advance Article , DOI: 10.1039/D0AN01244K

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