Deep learning guided enzyme engineering of Thermobifida fusca cutinase for increased PET depolymerization

Abstract

A responsible and sustainable circular economy of polymers requires efficient recycling processes with a low CO2 footprint. Enzymatic depolymerization of polyethylene terephthalate (PET) is a first step to make PET polymers a part of a circular economy of polymers. In this study, a structure-based deep learning model was utilized to identify residues in TfCut2 that are responsible for improved hydrolytic activity and enhanced stability. Machine learning guided design identified novel beneficial positions (L32E, S35E, H77Y, R110L, S113E, T237Q, R245Q, and E253H), which were evaluated and stepwise recombined yielding finally the beneficial variant L32E/S113E/T237Q. The latter TfCut2 variant exhibited improved PET depolymerization when compared with TfCut2 WT (amorphous PET film, 2.9-fold improvement // crystalline PET powder (crystallinity > 40%), 5.3-fold improvement). In terms of thermal resistance the variant L32E/S113E/T237Q showed a 5.7 °C increased half-inactivation temperature (T5060). The PET-hydrolysis process was monitored via a quartz crystal microbalance with dissipation monitoring (QCM-D) in real-time to determine depolymerization kinetics of PET coated onto the gold sensor. Finally, conformational dynamics analysis revealed that the substitutions induced a conformational change in the variant L32E/S113E/T237Q, in which the dominant conformation enabled a closer contact between the catalytic site and PET resulting in increased PET-hydrolysis. Overall, this study demonstrates the potential of deep learning models in protein engineering for identifying and designing efficient PET depolymerization enzymes.