the slowest part of the algorithm (aside from energy calculations). This is a dramatic improvement over the brute-force enumeration which scales as . A large-scale benchmark of DEE compared with alternative methods of protein structure prediction and design finds that DEE reliably converges to the optimal solution for protein lengths for which it runs in a reasonable amount of time. It significantly outperforms the alternatives under consideration, which involved techniques derived from mean field theory, genetic algorithms, and the Monte Carlo method. However, the other algorithms are appreciably faster than DEE and thus can be applied to larger and more complex problems; their relative accuracy can be extrapolated from a comparison to the DEE solution within the scope of problems accessible to DEE. Protein design The preceding discussion implicitly assumed that the rotamers are all different orientations of the same amino acid side chain. That is, the sequence of the protein was assumed to be fixed. It is also possible to allow multiple side chains to "compete" over a position by including both types of side chains in the set of rotamers for that position. This allows a novel sequence to be designed onto a given protein backbone. A short zinc finger protein fold has been redesigned this way. However, this greatly increases the number of rotamers per position and still requires a fixed protein length. Generalizations More powerful and more general criteria have been introduced that improve both the efficiency and the eliminating power of the method for both prediction and design applications. One example is a refinement of the singles elimination criterion known as the Goldstein criterion, which arises from fairly straightforward algebraic manipulation before applying the minimization: Thus rotamer can be eliminated if any alternative rotamer from the set at contributes less to the total energy than . This is an improvement over the original criterion, which requires comparison of the best possible (that is, the smallest) energy contribution from with the worst possible contribution from an alternative rotamer. An extended discussion of elaborate DEE criteria and a benchmark of their relative performance can be found in . References Desmet J, de Maeyer M, Hazes B, Lasters I. (1992). The dead-end elimination theorem and its use in protein side-chain positioning. Nature, 356, 539-542. . Voigt CA, Gordon DB, Mayo SL. (2000). Trading accuracy for speed: A quantitative comparison of search algorithms in protein sequence design. J Mol