Year: 2026
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide Efficient enzymatic depolymerization of recalcitrant polysaccharides such as chitin and cellulose relies on processive glycoside hydrolases (GHs), whose efficiency can be enhanced through cooperation with lytic polysaccharide monooxygenases (LPMOs). For processive GHs, retained binding during successive catalytic events aids depolymerization of crystalline substrates but comes at the expense of slow dissociation (low k off ) that limits turnover. Here, we engineered a series of mutants of the processive exochitobiohydrolase Sm ChiB from Serratia marcescens, in which a major determinant of substrate affinity and processivity, Trp220, was replaced by Tyr, Phe, His, Gln, or Ala. Functional analysis showed that these mutants had stepwise reductions in substrate affinity and processivity, with the latter being a signature of an increased k off . Molecular dynamics simulations confirmed that Trp220 plays an important role in substrate binding. The less processive Sm ChiB variants, and in particular W220Y, were able to reach wild-type-like performance only at high substrate concentrations. Importantly, the use of LPMOs to decrystallize the substrate and thereby increase its effective concentration, enhanced the performance of the less processive mutants to a much greater extent than for wild-type Sm ChiB. In some reaction setups, the combination of W220Y with an LPMO yielded twice as much soluble product compared to the wild-type enzyme under identical conditions. Thus, when combined with LPMOs, less processive GHs become more favorable because they are intrinsically more efficient catalysts when acting on noncrystalline substrates. These findings shed light on how the interplay between GHs and LPMOs can be optimized for efficient enzymatic conversion of recalcitrant polysaccharides.
Source: Biochemistry
Institutions: Norwegian University of Life Sciences, Politecnico di Milano
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Award: G6074949978
OpenAlex ID: W7129753439