深度学习指导的热裂褐藻角质酶工程用于促进PET解聚

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

有效和可持续的聚合物循环经济需要低二氧化碳排放和高效的回收工艺.聚对苯二甲酸乙二醇酯(PET)的酶解聚是使PET聚合物成为聚合物循环经济一部分的第一步.本文利用基于结构的深度学习模型来识别TfCut2中负责提高水解活性和增强稳定性的残基.机器学习引导设计确定了新的有益位置(L32E,S35E,H77Y,R110L,S113E,T237Q,R245Q和E253H),对其进行评估并逐步重组,最终获得有益变体L32E/S113E/T237Q.与TfCut2WT相比,后一种TfCut2变体表现出更好的PET解聚性能:无定形PET膜,2.9倍的改进;结晶PET粉末(结晶度>40%),5.3倍的改进.就耐热性而言,变体L32E/S113E/T237Q的半灭活温度(T_(50)^(60))增加5.7℃.利用带耗散监测的石英晶体微量天平(QCM-D)实时监测PET水解过程,以研究涂覆在金传感器上的PET解聚动力学.构象动力学分析结果表明,取代诱导了变体L32E/S113E/T237Q的构象变化,其中主要构象使催化位点和PET之间的接触更紧密,促进了PET水解.总之,本文展示了蛋白质工程中深度学习模型在识别和设计高效PET解聚酶方面的潜力.

A responsible and sustainable circular economy of polymers requires efficient recycling processes with a low CO_(2 )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℃ increased half‐inactivation temperature(T_(50)^(60)).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.