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A full quantitative understanding of the protein folding problem is now becoming possible with the help of the energy landscape theory and the protein folding funnel concept. Good folding sequences have a landscape that resembles a rough funnel where the energy bias towards the native state is larger than its ruggedness. Such a landscape leads not only to fast folding and stable native conformations but, more importantly, to sequences that are robust to variations in the protein environment and to sequence mutations. In this paper, an off-lattice model of sequences that fold into a ␤-barrel native structure is used to describe a framework that can quantitatively distinguish good and bad folders. The two sequences analyzed have the same native structure, but one of them is minimally frustrated whereas the other one exhibits a high degree of frustration.

We compare simulations using the generalized Born͞surface area (GB͞SA) implicit solvent model with simulations using explicit solvent (transferable intermolecular potential 3 point, TIP3P) to test the GB͞SA algorithm. We use the replica exchange molecular dynamics method to sample the conformational phase space of two ␣-helical peptides, A21 and the Fs, by using two different classical potentials and both water models. We find that when using GB͞SA: (i) A 21 is predicted to be more helical than the Fs peptide at all temperatures; (ii) the native structure of the Fs peptide is predicted to be a helical bundle instead of a single helix; and (iii) the persistence length and most probable end-to-end distance are too large in the unfolded state when compared against the explicit solvent simulations. We find that the potential of mean force in the plane is markedly different in the two solvents, making the two simulated peptides respond differently when the backbone torsions are perturbed. A fit of the temperature melting curves obtained in these simulations to a Lifson-Roig model finds that the GB͞SA model has an unphysically large nucleation parameter, whereas the explicit solvent model produces values similar to experiment. C lassical molecular dynamics (MD) is limited by the amount of real time that can be simulated with current methods and computers. Most of that time is usually spent computing the interactions among water atoms. This fact has provided a strong impetus to replace the explicitly represented water in simulations with implicit solvent. Implicit solvent models are continuum models that attempt to capture the average effect of the water on a solute.The generalized Born͞surface area (GB͞SA) models (1) are implicit solvent models that are often used in biomolecular simulations. GB͞SA models have been used in protein loop prediction algorithms (2), protein-protein docking (3, 4), pK a shift calculations (5, 6), the refinement of NMR-derived structures (7), and MD simulations to sample limited regions of phase space in several different proteins, peptides, and nucleic acid structures (8 -16). The GB and GB͞SA implicit solvent models have also been used to study folding peptides (17-20), mini-proteins (21-23), and protein fragments (24,25). Despite the widespread use of the GB͞SA model, the effect that replacing explicit water with GB͞SA implicit water has on the stability and structure of proteins and peptides is unclear.Other implicit solvent models have also been used to study peptide folding (26).The use of enhanced sampling algorithm allows for the simulation of peptides in explicit solvent. The replica exchange MD (REMD) algorithm (27) has been used to study the structure and thermodynamics of peptides, using explicit solvent models, over a wide range of temperatures (28 -32). The determination of the thermodynamics equilibrium without biasing the sampling provides a way to test the accuracy of force fields in describing the equilibrium between folded and unfolded states.The GB͞SA model estimates the...

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