Advances in computational protein design

Computational Design of a Single Amino Acid Sequence that Can Switch between Two Distinct Protein Folds

The functions of many proteins are mediated by specific conformational changes, and therefore the ability to design primary sequences capable of secondary and tertiary changes is an important step toward the creation of novel functional proteins. To this end, we have developed an algorithm that can optimize a single amino acid sequence for multiple target structures. The algorithm consists of an outer loop, in which sequence space is sampled by a Monte Carlo search with simulated annealing, and an inner loop, in which the effect of a given mutation is evaluated on the various target structures by using the rotamer packing routine and composite energy function of the protein design software, RosettaDesign. We have experimentally tested the method by designing a peptide, Sw2, which can be switched from a 2Cys-2His zinc finger-like fold to a trimeric coiled-coil fold, depending upon the pH or the presence of transition metals. Physical characterization of Sw2 confirms that it is able to reversibly adopt each intended target fold.

Electrostatics in computational protein design

Catalytic activity and protein-protein recognition have proven to be significant challenges for computational protein design. Electrostatic interactions are crucial for these and other protein functions, and therefore accurate modeling of electrostatics is necessary for successfully advancing protein design into the realm of protein function. This review focuses on recent progress in modeling electrostatic interactions in computational protein design, with particular emphasis on continuum models.

Artificial diiron proteins: from structure to function

De novo protein design provides an attractive approach for the construction of models to probe the features required for the function of complex metalloproteins. These minimal models contain the essential elements believed necessary for activity of the protein. In this article, we summarize the design, structure determination, and functional properties of a family of artificial diiron proteins.

Solving and analyzing side-chain positioning problems using linear and integer programming

MOTIVATION

Side-chain positioning is a central component of homology modeling and protein design. In a common formulation of the problem, the backbone is fixed, side-chain conformations come from a rotamer library, and a pairwise energy function is optimized. It is NP-complete to find even a reasonable approximate solution to this problem. We seek to put this hardness result into practical context.

RESULTS

We present an integer linear programming (ILP) formulation of side-chain positioning that allows us to tackle large problem sizes. We relax the integrality constraint to give a polynomial-time linear programming (LP) heuristic. We apply LP to position side chains on native and homologous backbones and to choose side chains for protein design. Surprisingly, when positioning side chains on native and homologous backbones, optimal solutions using a simple, biologically relevant energy function can usually be found using LP. On the other hand, the design problem often cannot be solved using LP directly; however, optimal solutions for large instances can still be found using the computationally more expensive ILP procedure. While different energy functions also affect the difficulty of the problem, the LP/ILP approach is able to find optimal solutions. Our analysis is the first large-scale demonstration that LP-based approaches are highly effective in finding optimal (and successive near-optimal) solutions for the side-chain positioning problem.