Abstract
Multinuclear metal ion clusters, coordinated by proteins, catalyze various critical biological redox reactions, including water oxidation in photosynthesis, and nitrogen fixation. Designed metalloproteins featuring synthetic metal clusters would aid in the design of bio-inspired catalysts for various applications in synthetic biology. The design of metal ion-binding sites in a protein chain requires geometrically constrained and accurate placement of several (between three and six) polar and/or charged amino acid side chains for every metal ion, making the design problem very challenging to address. Here, we describe a general computational method to redesign oligomeric interfaces of symmetric proteins for the purpose of creating novel multinuclear metalloproteins with tunable geometries, electrochemical environments, and metal cofactor stability via first and second-shell interactions.
The method requires a target symmetric organometallic cofactor whose coordinating ligands resemble the side chains of a natural or unnatural amino acid and a library of oligomeric protein structures featuring the same symmetry as the target cofactor. Geometric interface matches between target cofactor and scaffold are determined using a program that we call symmetric protein recursive ion-cofactor sampler (SyPRIS). First, the amino acid-bound organometallic cofactor model is built and symmetrically aligned to the axes of symmetry of each scaffold. Depending on the symmetry, rigid body and inverse rotameric degrees of freedom of the cofactor model are then simultaneously sampled to locate scaffold backbone constellations that are geometrically poised to incorporate the cofactor. Optionally, backbone remodeling of loops can be performed if no perfect matches are identified. Finally, the identities of spatially proximal neighbor residues of the cofactor are optimized using Rosetta Design. Selected designs can then be produced in the laboratory using genetically incorporated unnatural amino acid technology and tested experimentally for structure and catalytic activity.
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Hansen, W.A., Mills, J.H., Khare, S.D. (2016). Computational Design of Multinuclear Metalloproteins Using Unnatural Amino Acids. In: Stoddard, B. (eds) Computational Design of Ligand Binding Proteins. Methods in Molecular Biology, vol 1414. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3569-7_10
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DOI: https://doi.org/10.1007/978-1-4939-3569-7_10
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