Understanding Design Rules for Optimizing the Interface between Immobilized Enzymes and Random Copolymer Brushes

• Main Authors: Héctor Sánchez-Morán (7817531), James S. Weltz (8837648), Daniel K. Schwartz (1303779), Joel L. Kaar (124911)
• Format: Other Non-Article Part of Journal/Newspaper
• Language: unknown
• Published: 2021
• Subjects: Biophysics; Biochemistry; Genetics; Biotechnology; Inorganic Chemistry; Biological Sciences not elsewhere classified; Chemical Sciences not elsewhere classified; Information Systems not elsewhere classified; PEGMA; aliphatic residues; material; Candida antarctica lipase B; Design Rules; enzyme surface area; Bacillus subtilis lipase; Immobilized Enzymes; brush composition; Candida rugosa lipase; SBMA; unsupervised cluster analysis; ruggedize enzymes; Random Copolymer Brushes; sulfobetaine methacrylate; brush layer; copolymer brush surfaces; protein surface hydrophobicity; CALB; Rugosa; Antarc*; Antarctica
• Online Access: https://doi.org/10.1021/acsami.1c02443.s001

A long-standing goal in the field of biotechnology is to develop and understand design rules for the stabilization of enzymes upon immobilization to materials. While immobilization has sometimes been successful as a strategy to stabilize enzymes, the design of synthetic materials that stabilize enzymes remains largely empirical. We sought to overcome this challenge by investigating the mechanistic basis for the stabilization of immobilized lipases on random copolymer brush surfaces comprised of poly­(ethylene glycol) methacrylate (PEGMA) and sulfobetaine methacrylate (SBMA), which represent novel heterogeneous supports for immobilized enzymes. Using several related but structurally diverse lipases, including Bacillus subtilis lipase A (LipA), Rhizomucor miehei lipase, Candida rugosa lipase, 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. This dependence was explained by developing and applying a new algorithm to quantify protein surface hydrophobicity, which involved using unsupervised cluster analysis to identify clusters of hydrophobic atoms. Characterization of the lipases showed that the optimal brush composition correlated with the free energy of solvation per enzyme surface area, which ranged from −17.1 kJ/mol·nm 2 for LipA to −11.8 kJ/mol·nm 2 for CALB. Additionally, using this algorithm, we found that hydrophobic patches consisting of aliphatic residues had a higher free energy than patches consisting of aromatic residues. By providing the basis for rationally tuning the interface between enzymes and materials, this understanding will transform the use of materials to reliably ruggedize enzymes under extreme conditions.