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ABSTRACT We present a novel method that uses the collective modes obtained with a coarse-grained model/anisotropic network model to guide the atomic-level simulations. Based on this model, local collective modes can be calculated according to a single configuration in the conformational space of the protein. In the molecular dynamics simulations, the motions along the slowest few modes are coupled to a higher temperature by the weak coupling method to amplify the collective motions. This amplified-collective-motion (ACM) method is applied to two test systems. One is an S-peptide analog. We realized the refolding of the denatured peptide in eight simulations out of 10 using the method. The other system is bacteriophage T4 lysozyme. Much more extensive domain motions between the N-terminal and C-terminal domain of T4 lysozyme are observed in the ACM simulation compared to a conventional simulation. The ACM method allows for extensive sampling in conformational space while still restricting the sampled configurations within low energy areas. The method can be applied in both explicit and implicit solvent simulations, and may be further applied to important biological problems, such as long timescale functional motions, protein folding/unfolding, and structure prediction.
INTRODUCTION
In recent years there has been an increasing use of theoretical methods, especially molecular dynamics (MD) simulations, to gain insights into structure-function relationships in proteins (Doniach and Eastman, 1999). One of the main questions to be answered when assessing the usefulness of MD simulations of proteins in understanding biological functions is the degree to which the simulations adequately sample the conformational space of the protein. If a given property is poorly sampled over the MD simulations, the results obtained have a limited usefulness.
A straightforward way to solve this problem is to increase the simulation time. With the improvements in computer power and algorithms, we have progressed to tens of nano-seconds now (Daggett, 2000). This timescale is still too short to observe many important protein processes, such as slow conformational changes and protein folding/unfolding. The inefficiency in sampling is a result of the frustrated nature of the energy landscape. While the exploration of different conformational states and the mechanism of transitions between different conformational states are of more interest than examining local fluctuations during a simulation, the system will spend most of the time...