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Development and application of computational tools for the design of ligand binding in proteins

Subject Area Structural Biology
Biochemistry
Bioinformatics and Theoretical Biology
Term from 2010 to 2018
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 177832239
 
Final Report Year 2019

Final Report Abstract

Ligand binding specificity and affinity are exceptionally well tailored in natural proteins. They are governed by a well-defined architecture of the binding pocket and the protein in general. Crafting such architectures by constructing custom-made proteins has been a long-standing vision. Computational methods have rapidly advanced in recent years and proteins have been designed to catalyze new reactions. However, while moderate turnover numbers can be achieved, ligand recognition seems to be particularly problematic. We have concentrated our efforts on this aspect of protein-small molecule interactions. To systematically investigate how the design of a binding pocket and its specificity can be improved, we constructed the modular computational framework PocketOptimizer and assembled benchmark sets for further testing and development. Crucial to the improvement of computational methods is the feedback from experiments. Thus, we used two experimental systems, namely the tryptophan repressor TrpR and the periplasmic polyamine binding protein PotF, whose specificity we modified in the context of biosensor systems. The work not only generated two sensor systems that can be used to visualize in vivo a plant hormone and a neurotransmitter, respectively. The experimental validation that used robust biophysical and structural characterization techniques further can be used as benchmark sets and serve as feedback to computational ligand binding predictions. With this systematic approach we implemented a reliable design strategy and contribute to a general understanding of protein-ligand interactions and associated dynamics.

Publications

  • (2014) Change in protein-ligand specificity through binding pocket grafting. J Struc Biol 185:186-92
    Scheib, U., Shanmugaratnam, S., Farias-Rico, J.A. & Höcker, B.
    (See online at https://doi.org/10.1016/j.jsb.2013.06.002)
  • (2014) Identification of protein scaffolds for enzyme design using ScaffoldSelection. Methods Mol Biol 1216:183-96
    Stiel, A., Feldmeier, K. & Höcker, B.
    (See online at https://doi.org/10.1007/978-1-4939-1486-9_9)
  • (2016) De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy. Nature Chem Biol 12:29-34
    Huang, P.-S., Feldmeier, K., Parmeggiani, F., Fernandez Velasco, D.A., Höcker, B. & Baker, D.
    (See online at https://doi.org/10.1038/NCHEMBIO.1966)
  • (2016) PocketOptimizer and the design of ligand binding sites. Methods Mol Biol 1414:63-75
    Stiel, A., Nellen, M. & Höcker, B.
    (See online at https://doi.org/10.1007/978-1-4939-3569-7_5)
  • (2018) Redesign of LAOBP to bind novel L-amino acid ligands. Protein Sci 7:957-968
    Banda-Vázquez, J., Shanmugaratnam, S., Rodríguez-Sotres, R., Torres-Larios, A., Höcker, B. & Sosa-Peinado, A.
    (See online at https://doi.org/10.1002/pro.3403)
  • (2018) Strategies for designing non-natural enzymes and binders. Curr Opin Chem Biol 47:67-76
    Lechner, H., Ferruz N., Höcker B.
    (See online at https://doi.org/10.1016/j.cbpa.2018.07.022)
 
 

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