Thursday, 17 May 2007 - 9:30 AM
207 (Pfahler Hall)
226

Computational design of single-chain four-helix bundle proteins that bind non-biological cofactors

Andreas Lehmann1, Gretchen M. Bender1, H. Christopher Fry1, Don Engel2, Michael J. Therien1, William F. DeGrado1, and Jeffery G. Saven1. (1) University of Pennsylvania, Philadelphia, PA, (2) American Physical Society Congressional Science Fellow, Washington, DC

Combining proteins with functional non-biological cofactors holds great potential for the development of new materials and nanostructures. The protein may serve as a scaffold to modulate the local cofactor environment and control solubility and processing. The targeted de-novo design of protein structure can facilitate the self-assembly of the protein-cofactor complexes into higher-order structures with desired cofactor orientation and alignment. This effort is inspired in part by natural systems for light harvesting and electron transfer, such as cytochrome bc1. De novo designed proteins may be tailored, however, to accommodate a wide variety of non-biological cofactors, having functionalities not known in nature.

We present a protein that binds two metal-porphyrin cofactors, non-biological diphenyl iron- and zinc-based porphyrins. A de novo designed scaffold comprises four helices connected via three interhelical loops and provides histidine residues for coordinating the porphyrin metal. A sequence is designed for this 108-residue protein that is consistent with this scaffold and binding the cofactors.

We also discuss a single-chain four-helix bundle protein designed to form a complex with a Ru(II)polypyridyl-(porphinato)Zn(II) [Ru-PZn] cofactor. The bare cofactor shows substantial dynamic hyperpolarizability, a property which may potentially be leveraged in devices for optoelectronics or light manipulation (waveguide switches, modulators, or filters). The Zn-porphyrin part of the cofactor is designed to be pentacoordinated through four equatorial porphyrin nitrogen atoms and one axial histidine. For this 108-residue protein, a sequence is designed consistent with the structure and cofactor binding.

We present the design algorithm, which is guided by a self-consistent probabilistic approach to determining site-specific amino acid probabilities at variable positions. The use of this approach is referred to as a Statistical Computationally Assisted Design Strategy (SCADS). Experimental characterization of these proteins will also be discussed.


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