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BIOspektrum

, Volume 24, Issue 1, pp 96–98 | Cite as

Neue in silico-Methoden für die Etablierung einer Grünen Chemie

Biotechnologie Enzymtechnologie
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Abstract

Many fine chemicals have to meet high demands in the chemical and pharmaceutical industries in terms of optical purity, which can lead to high costs in the production and, in addition, to high amounts of waste. Catalyst optimization is necessary here because the observed substrates are mostly „man-made materials“ and evolution has not yet had time to evolve biocatalysts for this purpose. This is where advanced computational concepts such as simulation or machine learning help to improve such designs in terms of efficiency and sustainability.

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Literatur

  1. [1]
    Garske AL, Kapp G, McAuliffe J (2017) Industrial Enzymes and Biocatalysis. In: Kent JA, Bommaraju TV, Barnicki SD (Hrsg) Handbook of Industrial Chemistry and Biotechnology. Springer International Publishing, Basel, S. 1625Google Scholar
  2. [2]
    Anastas PT, Warner JC (1998) Green Chemistry: Theory and Practice. Oxford University Press, New YorkGoogle Scholar
  3. [3]
    DiCosimo R, McAuliffe J, Poulose JA et al. (2013) Industrial use of immobilized enzymes. Chem Soc Rev 42:6437–6474CrossRefPubMedGoogle Scholar
  4. [4]
    Markets and Markets (2014) Specialty enzymes market by source, type, application & geography global trends & forecasts to 2018. https://www.marketsandmarkets.com/Market- Reports/specialty-enzymes-market-21682828.htmlGoogle Scholar
  5. [5]
    Daiha K de G, Angeli R, de Oliveira SD et al. (2015) Are lipases still important biocatalysts? A study of scientific publications and patents for technological forecasting. PLoS One 10:e0131624CrossRefGoogle Scholar
  6. [6]
    Jordan A, Gathergood N (2013) Designing safer and greener antibiotics. Antibiotics 2:419–438CrossRefPubMedPubMedCentralGoogle Scholar
  7. [7]
    Bedbrook CN, Yang KK, Rice AJ et al. (2017) Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization. PLoS Comput Biol 13:e1005786CrossRefGoogle Scholar
  8. [8]
    He D, Huang L, Xu Y et al. (2017) Molecular dynamics directed rational design and fluorescence binding assay of phosphopeptide ligands for PLK polo-box domain. Mol Simul 43:176–182CrossRefGoogle Scholar
  9. [9]
    Kutzner C, Páll S, Fechner M et al. (2015) Best bang for your buck: GPU nodes for GROMACS biomolecular simulations. J Comput Chem 36:1990–2008CrossRefPubMedPubMedCentralGoogle Scholar
  10. [10]
    Giguère S, Laviolette F, Marchand M et al. (2015) Machine learning assisted design of highly active peptides for drug discovery. PLoS Comput Biol 11:e1004074CrossRefGoogle Scholar
  11. [11]
    Quang NN, Perret G, Ducongé F (2016) Applications of high-throughput sequencing for in vitro selection and characterization of aptamers. Pharmaceuticals 9:76CrossRefGoogle Scholar
  12. [12]
    Alkawaa F, Chaudhary K, Garmire LX (2018) Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data. J Proteome Res 17:337–347CrossRefGoogle Scholar
  13. [13]
    Chen Y, Ellenee Argentinis JD, Weber G (2016) IBM Watson: How cognitive computing can be applied to big data challenges in life sciences research. Clin Ther 38:688–701CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2018

Authors and Affiliations

  1. 1.Computational Biology and SimulationTU DarmstadtDarmstadtDeutschland
  2. 2.Institut für Bio- und Lebensmitteltechnologie, Bereich II – Technische BiologieKarlsruher Institut für Technologie (KIT)KarlsruheDeutschland

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