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

Meng, Shuaiqi; Li, Zhongyu; Zhang, Peng; Contreras, Francisca; Ji, Yu; Schwaneberg, Ulrich

doi:10.1016/s1872-2067(23)64470-5

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…However, these data do not necessary represent the highest activity achievable by each enzyme in its own optimized conditions 67 . For example, some PET hydrolases are suggested to have improved activity in the presence of Ca 2+ , which was a factor not considered in this study 47 , 55 . We intended these data to give information about the relative activities of these PET hydrolases across a broad range of conditions and showcase the wealth of data that can be achieved from these small-scale enzyme purifications.…”

Section: Discussionmentioning

confidence: 94%

“…BhrPETase comes from the bacterium HR29 and an engineered variant, TurboPETase, was reported with increased activity 43 , 44 . Tf Cut2, a cutinase from Thermobifida fusca, has been engineered to produce improved variants Tf Cut2 S121P/D174S/D204P and Tf Cut2 L32E/S113E/T237Q 45 – 47 . Is PETase was identified from Ideonella sakaiensis , a bacterium capable of using PET as its major carbon source, and has been engineered extensively, with ThermoPETase, DuraPETase, FAST-PETase, HotPETase, DepoPETase, and Z1-PETase among the notable variants 21 , 48 – 52 .…”

Section: Resultsmentioning

confidence: 99%

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Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

Norton-Baker,

Denton,

Murphy

et al. 2024

Sci Rep

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As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

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Section: Discussionmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

Norton-Baker,

Denton,

Murphy

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…[13] Similarly, MutCompute was applied to TfCut2 wild type cutinase to identify beneficial mutations and create an enhanced variant, which exhibited a 5.3-fold improved depolymerization of crystalline PET. [31]] Most recent examples include the adoption of machine learning-assisted prediction of the degrading activity of certain plastics [32] or the use of machine learning to guide the directed evolution of plastic degrading enzymes. [33] This paper has applied different approaches such as structure-based design, ancestral sequence reconstruction and machine learning to engineer a new variant of PsPETase.…”

Section: Introductionmentioning

confidence: 99%

Enhancing PET Degrading Enzymes: A Combinatory Approach

Joho,

Royan,

Caputo

et al. 2024

ChemBioChem

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Plastic waste has become a substantial environmental issue. A potential strategy to mitigate this problem is to use enzymatic hydrolysis of plastics to depolymerize post‐consumer waste and allow it to be reused. Over the last few decades, the use of enzymatic PET‐degrading enzymes has shown promise as a great solution for creating a circular plastic waste economy. PsPETase from Piscinibacter sakaiensis has been identified as an enzyme with tremendous potential for such applications. But to improve its efficiency, enzyme engineering has been applied aiming at enhancing its thermal stability, enzymatic activity, and ease of production. Here, we combine different strategies such as structure‐based rational design, ancestral sequence reconstruction and machine learning to engineer more highly active Combi‐PETase variants with a melting temperature of 70°C and optimal performance at 60°C. Furthermore, this study demonstrates that these approaches, commonly used in other works of enzyme engineering, are most effective when utilized in combination, enabling the improvement of enzymes for industrial applications.

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Application of Digital Methods in Polymer Science and Engineering

Schuett,

Endres,

Standau

et al. 2023

Adv Funct Materials

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The development of new polymer materials is an emerging field for more than 100 years. However, it is currently facing major challenges and the application of digital methods can help to develop new processes, discover new materials and, thus, contribute to the challenges of today and the future. Though, the application of digital methods in the field of polymer science is currently very limited, when compared to other classes of materials such as small molecules or inorganic high‐performance materials. Nevertheless, there are already first, very promising approaches. The current review article focuses on the application of these methods in different aspects of polymer research including polymer design, synthesis, and characterization. Furthermore, the digital discovery of polymer engineering is highlighted in detail showing the broad range of potential applications of these methods in polymer science. Finally, future possibilities and opportunities are derived from the current state‐of‐the‐art and perspectives for a potential evolution of polymer science are provided.

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

Cited by 6 publications

References 42 publications

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

Enhancing PET Degrading Enzymes: A Combinatory Approach

Application of Digital Methods in Polymer Science and Engineering

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