| 1. |
- Johnson, Sean R., et al.
(författare)
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Computational scoring and experimental evaluation of enzymes generated by neural networks
- 2024
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Ingår i: Nature Biotechnology. - 1087-0156 .- 1546-1696. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate a set of 20 diverse computational metrics to assess the quality of enzyme sequences produced by three contrasting generative models: ancestral sequence reconstruction, a generative adversarial network and a protein language model. Focusing on two enzyme families, we expressed and purified over 500 natural and generated sequences with 70–90% identity to the most similar natural sequences to benchmark computational metrics for predicting in vitro enzyme activity. Over three rounds of experiments, we developed a computational filter that improved the rate of experimental success by 50–150%. The proposed metrics and models will drive protein engineering research by serving as a benchmark for generative protein sequence models and helping to select active variants for experimental testing.
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| 2. |
- Zrimec, Jan, 1981, et al.
(författare)
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Controlling gene expression with deep generative design of regulatory DNA
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1, s. 5099-
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Tidskriftsartikel (refereegranskat)abstract
- Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.
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| 3. |
- Cao, Zhejian, 1991, et al.
(författare)
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Synthesis of Metal-Organic Frameworks through Enzymatically Recycled Polyethylene Terephthalate
- 2023
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Ingår i: ACS Sustainable Chemistry & Engineering. - 2168-0485. ; 11:43, s. 15506-15512
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Tidskriftsartikel (refereegranskat)abstract
- Polyethylene terephthalate (PET) as one of the most produced plastics contributes to global waste pollution. Upcycling PET into value-added products therefore is of environmental and economic interest. Terephthalic acid (TPA), the monomer of PET, is a common linker for metal-organic framework (MOF) synthesis; thus, PET-to-MOF upcycling raises much research attention. However, conventional PET-to-MOF upcycling often requires PET depolymerization with strong acids or bases and high temperatures, which can lead to environmental and energy penalties. As an alternative, PETase offers a sustainable approach to depolymerizing PET under mesophilic and mild pH conditions. Here we report UiO-66, MOF-5, and MIL-101 syntheses using enzymatically recycled TPA as linkers. The enzymatically recycled TPA demonstrated low impurity, and the obtained MOFs possessed comparable crystallinity, thermal stability, and surface area. These results reveal the feasibility of MOF synthesis by using enzymatically recycled PET.
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| 4. |
- Fu, Xiaozhi, 1990, et al.
(författare)
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Recent Advances on Sorting Methods of High-Throughput Droplet-Based Microfluidics in Enzyme Directed Evolution
- 2021
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Ingår i: Frontiers in Chemistry. - : Frontiers Media SA. - 2296-2646. ; 9
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Forskningsöversikt (refereegranskat)abstract
- Droplet-based microfluidics has been widely applied in enzyme directed evolution (DE), in either cell or cell-free system, due to its low cost and high throughput. As the isolation principles are based on the labeled or label-free characteristics in the droplets, sorting method contributes mostly to the efficiency of the whole system. Fluorescence-activated droplet sorting (FADS) is the mostly applied labeled method but faces challenges of target enzyme scope. Label-free sorting methods show potential to greatly broaden the microfluidic application range. Here, we review the developments of droplet sorting methods through a comprehensive literature survey, including labeled detections [FADS and absorbance-activated droplet sorting (AADS)] and label-free detections [electrochemical-based droplet sorting (ECDS), mass-activated droplet sorting (MADS), Raman-activated droplet sorting (RADS), and nuclear magnetic resonance-based droplet sorting (NMR-DS)]. We highlight recent cases in the last 5 years in which novel enzymes or highly efficient variants are generated by microfluidic DE. In addition, the advantages and challenges of different sorting methods are briefly discussed to provide an outlook for future applications in enzyme DE.
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