Protein language model empowered the robust ASR-driven PET hydrolase featured with two PET binding motifs

Abstract

Various tools specifically designed to accelerate evolutionary processes for biocatalysis and biotransformation have been developed in the field of protein engineering. Among them, protein language modeling (PLM) is extremely efficient for large-scale screening, thus initiating a new era of accelerated prediction. Therefore, this study considered the highly promising ancestral sequence reconstruction 1(ASR1)-polyethylene terephthalate hydrolase (PETase), previously obtained via ancestral sequence reconstruction, as a representative model. The PLM Evolutionary Scale Modeling-1V was used as an amino acid optimizer to efficiently identify four beneficial variants that improved terephthalic acid (TPA) yield by 1.7-fold. The triple variant ASR1-HRT (N81H/W120R/V265T) showed a 6.1-fold increase in TPA yield compared with that of the five-site variant FAST-PETase (N233K/R224Q/S121E/D186H/R280A) through the recombination of a single beneficial variant. Moreover, ASR1-HRT achieved a depolymerization rate of 96.1% for commercial polyethylene terephthalate (PET) plastics. Molecular dynamics simulations showed that the enhancement of structural stability at high temperatures and changes in catalytic reactions due to solvation contributed to efficient and stable properties. In addition, through exploring the enzyme-PET film interaction landscape at the molecular level, the two motifs of ASR1-PETase were found to play key roles in the catalytic process at the solid-liquid interface. This enhanced the initial adsorption of the enzyme on PET film, thereby enhancing the hydrolysis performance. Overall, the PLM optimization strategy has the potential to be applied to other enzymes, thereby efficiently accelerating protein engineering.