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Using computer simulations to model the folding of proteins into their native states is computationally expensive due to the extraordinarily low degeneracy of the ground state. In this paper, we develop an efficient way to sample these folded conformations using Wang Landau sampling coupled with the configurational bias method (which uses an unphysical 'temperature' that lies between the collapse and folding transition temperatures of the protein). This method speeds up the folding process by roughly an order of magnitude over existing algorithms for the sequences studied. We apply this method to study the adsorption of intrinsically disordered hydrophobic polar protein fragments on a hydrophobic surface. We find that these fragments, which are unstructured in the bulk, acquire secondary structure upon adsorption onto a strong hydrophobic surface. Apparently, the presence of a hydrophobic surface allows these random coil fragments to fold by providing hydrophobic contacts that were lost in protein fragmentation. [ABSTRACT FROM AUTHOR]

This paper describes a new Monte Carlo method based on a novel stochastic potential switching algorithm. This algorithm enables the equilibrium properties of a system with potential V to be computed using a Monte Carlo simulation for a system with a possibly less complex stochastically altered potential V. By proper choices of the stochastic switching and transition probabilities, it is shown that detailed balance can be strictly maintained with respect to the original potential V. The validity of the method is illustrated with a simple one-dimensional example. The method is then generalized to multidimensional systems with any additive potential, providing a framework for the design of more efficient algorithms to simulate complex systems. A near-critical Lennard-Jones fluid with more than 20 000 particles is used to illustrate the method. The new algorithm produced a much smaller dynamic scaling exponent compared to the Metropolis method and improved sampling efficiency by over an order of magnitude. [ABSTRACT FROM AUTHOR]

Metadynamics is an efficient method for simulation of the free energy of many-particle systems. Over the last several years it has been applied to study a wide variety of systems, ranging from simple fluids to biological macromolecules. The method relies on uniform sampling along specified collective variables or order parameters. Such order parameters, however, are often bounded, and metadynamics algorithms as originally developed suffer from systematic errors at the corresponding boundaries. While several approaches have been proposed in the past to correct these errors for unidimensional systems, no method exists to fully correct these errors in multi-dimensional systems at points where multiple boundaries meet. Here we present a correction scheme that circumvents this limitation. [ABSTRACT FROM AUTHOR]

Configurational-bias Monte Carlo has been incorporated into the Wang-Landau method. Although the Wang-Landau algorithm enables the calculation of the complete density of states, its applicability to continuous molecular systems has been limited to simple models. With the inclusion of more advanced sampling techniques, such as configurational-bias, the Wang-Landau method can be used to simulate complex chemical systems. The accuracy and efficiency of the method is assessed using as a test case systems of linear alkanes represented by a united-atom model. With strict convergence criteria, the density of states derived from the Wang-Landau algorithm yields the correct heat capacity when compared to conventional Boltzmann sampling simulations. [ABSTRACT FROM AUTHOR]