Tools for computational design and high-throughput screening of therapeutic enzymes

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Authors

VAŠINA Michal VELECKÝ Jan PLANAS IGLESIAS Joan MARQUES Sérgio Manuel ŠKAŘUPOVÁ Jana DAMBORSKÝ Jiří BEDNÁŘ David MAZURENKO Stanislav PROKOP Zbyněk

Year of publication 2022
Type Article in Periodical
Magazine / Source ADVANCED DRUG DELIVERY REVIEWS
MU Faculty or unit

Faculty of Science

Citation
Web https://www.sciencedirect.com/science/article/pii/S0169409X22000333?via%3Dihub
Doi http://dx.doi.org/10.1016/j.addr.2022.114143
Keywords Big data; Bioinformatics; Biopharmaceuticals; Biocatalysts; Enzyme characterization; Enzyme diversity; Machine learning; Microfluidics; Rational design
Description Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.
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