Designing protein beta-sheet surfaces by Z-score optimization.

Studies of lattice models of proteins have suggested that the appropriate energy expression for protein design may include nonthermodynamic terms to accommodate negative design concerns. One method, developed in lattice model studies, maximizes a quantity known as the " Z-score," which compares the lowest energy sequence whose ground state structure is the target structure to an ensemble of random sequences. Here we show that, in certain circumstances, the technique can be applied to real proteins. The resulting energy expression is used to design the beta-sheet surfaces of two real proteins. We find experimentally that the designed proteins are stable and well folded, and in one case is even more thermostable than the wild type.

Intrinsic beta-sheet propensities result from van der Waals interactions between side chains and the local backbone.

The intrinsic secondary structure-forming propensities of the naturally occurring amino acids have been measured both experimentally in host-guest studies and statistically by examination of the protein structure databank. There has been significant progress in understanding the origins of intrinsic alpha-helical propensities, but a unifying theme for understanding intrinsic beta-sheet propensities has remained elusive. To this end, we modeled dipeptides by using a van der Waals energy function and derived Ramachandran plots for each of the amino acids. These data were used to determine the entropy and Helmholtz free energy of placing each amino acid in the beta-sheet region of phi-psi space. We quantitatively establish that the dominant cause of intrinsic beta-sheet propensity is the avoidance of steric clashes between an amino acid side chain and its local backbone. Standard implementations of coulombic and solvation effects are seen to be less important.

Computational protein design.

A 'protein design cycle', involving cycling between theory and experiment, has led to recent advances in rational protein design. A reductionist approach, in which protein positions are classified by their local environments, has aided development of an appropriate energy expression. The computational principles and practicalities of the protein design cycle are discussed.

Pairwise calculation of protein solvent-accessible surface areas.

BACKGROUND: The tractability of many algorithms for determining the energy state of a system depends on the pairwise nature of an energy expression. Some energy terms, such as the standard implementation of the van der Waals potential, satisfy this criterion whereas others do not. One class of important potentials that are not pairwise involves benefits and penalties for burying hydrophobic and/or polar surface areas. It has been found previously that, in some cases, a pairwise approximation to these surface areas correlates with the true surface areas. We set out to generalize the applicability of this approximation. RESULTS: We develop a pairwise expression with one scalable parameter that closely reproduces both the true buried and the true exposed solvent-accessible surface areas. We then refit our previously published coiled-coil stability data to give solvation parameters of 26 cal/mol A2 favoring hydrophobic burial and 100 cal/mol A2 opposing polar burial. CONCLUSIONS: An accurate pairwise approximation to calculate exposed and buried protein solvent-accessible surface area is achieved.