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Area of Science:

  • Biotechnology
  • Enzyme Engineering
  • Polymer Science

Background:

  • Enzymatic depolymerization of poly-(ethylene terephthalate) (PET) is a key chemical recycling strategy.
  • Identifying novel PET-degrading enzymes (PET hydrolases) from natural sources is crucial for enzyme evolution and engineering.

Purpose of the Study:

  • To improve enzyme discovery for PET recycling using an iterative machine learning strategy.
  • To identify novel PET hydrolases with high activity under industrially relevant conditions.

Main Methods:

  • Employed an iterative machine learning strategy combined with HMM searches to identify putative PET hydrolases.
  • Utilized high-throughput screening to express, purify, and assay >200 enzyme candidates for PET hydrolysis.
  • Investigated enzyme activity across varying pH, temperature, and substrate crystallinity.

Main Results:

  • Discovered 91 previously unknown PET hydrolases, with 35 active at pH 4.5 on crystalline PET.
  • Identified four enzymes with activity comparable to or exceeding the benchmark LCC-ICCG under challenging conditions.
  • Found statistical correlations between enzyme regions and low-pH activity, improving machine learning predictor precision by up to 30%.

Conclusions:

  • Iterative machine learning and experimental screening efficiently discover active enzymes for PET recycling.
  • Identified novel PET hydrolases suitable for developing more efficient commercial recycling processes.
  • Understanding structure-activity relationships at specific conditions enhances enzyme discovery and engineering efforts.