Thermostability enhancement of polyethylene terephthalate degrading PETase using self‐ and nonself‐ligating protein scaffolding approaches

Abstract

Polyethylene terephthalate (PET) hydrolase enzymes show promise for enzymatic PET degradation and green recycling of single‐use PET vessels representing a major source of global pollution. Their full potential can be unlocked with enzyme engineering to render activities on recalcitrant PET substrates commensurate with cost‐effective recycling at scale. Thermostability is a highly desirable property in industrial enzymes, often imparting increased robustness and significantly reducing quantities required. To date, most engineered PET hydrolases show improved thermostability over their parental enzymes. Here, we report engineered thermostable variants of Ideonella sakaiensis PET hydrolase enzyme (IsPETase) developed using two scaffolding strategies. The first employed SpyCatcher‐SpyTag technology to covalently cyclize IsPETase, resulting in increased thermostability that was concomitant with reduced turnover of PET substrates compared to native IsPETase. The second approach using a GFP‐nanobody fusion protein (vGFP) as a scaffold yielded a construct with a melting temperature of 80°C. This was further increased to 85°C when a thermostable PETase variant (FAST PETase) was scaffolded into vGFP, the highest reported so far for an engineered PET hydrolase derived from IsPETase. Thermostability enhancement using the vGFP scaffold did not compromise activity on PET compared to IsPETase. These contrasting results highlight potential topological and dynamic constraints imposed by scaffold choice as determinants of enzyme activity.

Modulation ofIdeonella sakaiensisPETase active site flexibility and activity on morphologically distinct substrates by surface charge engineering

“…Is PETase (hereafter referred to as PETase) elaborated by the bacterium Ideonella sakaiensis has also garnered much attention 2 . Numerous variants with improved activity and robustness have been developed by employing various engineering strategies 12-22 . Computational design and machine learning approaches were respectively used to derive the more efficient and thermostable Dura- and FAST PETase variants 8,9 .…”