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Molecular Modeling for Artificial Metalloenzyme Design and Optimization.

Lur Alonso-Cotchico1, Jaime Rodrı Guez-Guerra1, Agustí Lledós1

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We developed computational methods to design artificial metalloenzymes (ArMs) by predicting host-cofactor binding and catalytic mechanisms. This approach aids in creating novel biocatalysts with improved activity and selectivity.

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Area of Science:

  • Bioinorganic Chemistry
  • Computational Chemistry
  • Biocatalysis

Background:

  • Artificial metalloenzymes (ArMs) combine homogeneous catalysts with biological scaffolds for novel biocatalysis.
  • Understanding host-cofactor-substrate interactions is crucial for ArM design but often lacks structural data.
  • Current molecular modeling methods require advancement to address key questions in ArM development.

Purpose of the Study:

  • To present an integrative computational framework for studying artificial metalloenzymes.
  • To demonstrate the application of these computational strategies in predicting ArM behavior and guiding design.
  • To address limitations in structural characterization by providing molecular insights into ArM function.

Main Methods:

  • Development of advanced protein-ligand docking strategies for transition metal complexes.
  • Integration of molecular dynamics (MD) and quantum mechanical (QM) methods for comprehensive simulations.
  • Application of 'Theozyme'-like docking and QM/MM for mechanism and binding studies.

Main Results:

  • Accurate prediction of host-cofactor binding and identification of substrate binding modes.
  • Decoding of catalytic mechanisms, including transition states and second coordination sphere effects.
  • Successful computer-aided design of ArMs, leading to improved activity and enantioselectivity.

Conclusions:

  • The developed computational framework provides crucial molecular insights for ArM design.
  • This approach overcomes limitations in experimental structural characterization.
  • Computational strategies are essential for advancing the field of artificial metalloenzymes and biocatalysis.