One of the persistent challenges in the field of biocatalysis is to improve the catalytic performance of enzymes in industrially relevant conditions. Among a plethora of stabilization strategies, some of the leading ones have been the immobilization of enzymes on biomaterial interfaces or enzyme tethering to polymers to form soluble protein-polymer conjugates. However, even though some homopolymeric materials are conventionally used for these applications by default, they sometimes provoke destabilization or denaturation of enzymes without entirely knowing why. In the last decade, it has been shown that certain complex heterogeneous materials are able to promote the folded conformation of proteins. The optimal composition and architecture of these materials is highly protein specific, however, their design remains largely empirical. We combined random copolymer brushes comprised of ethylene glycol methacrylate (PEGMA) and sulfobetaine methacrylate (SBMA); with several related but structurally diverse lipases, including Bacillus subtilis lipase A (LipA), Rhizomucor miehei lipase (RML), Candida rugosa lipase (CRL) and Candida antarctica lipase B (CALB). We showed that the stability of each lipase at elevated temperatures was strongly dependent on the fraction of PEGMA in the brush layer. We hypothesized that the hydrophilic/hydrophobic character of the enzyme surface plays a key role in its stability. To get a detailed and complete picture of proteins’ surface hydrophobicity, we developed a novel algorithm for the identification of clustered hydrophobically interacting protein-surface moieties. Utilizing a three-dimensional Gaussian blur, we are able to identify areas with superimposed hydrophobicity, and through machine-learning based density scanning, we define the shape, surface area, hydrophobic intensity and atoms forming hydrophobic patches. Characterization of the lipases showed that the optimal brush composition correlated with the free energy of solvation per enzyme surface area. By correlating enzyme stability with information of hydrophobicity on the surface of each enzyme, we were able to test hypotheses about how different brush compositions stabilized different enzymes based on their surface characteristics.

Understanding design rules for optimizing the interface between enzymes and copolymer brushes

ACS Spring 2022

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