We describe a method for efficient sampling of the energy landscape of a protein in atomic molecular dynamics simulations. A simulation is divided into alternatively occurring relaxation phases and excitation phases. In the relaxation phase (conventional simulation), we use a frequently updated reference structure and deviations from this reference structure to mark whether the system has been trapped in a local minimum. In that case, the simulation enters the excitation phase, during which a few slow collective modes of the system are coupled to a higher temperature bath. After the system has escaped from the minimum (also judged by deviations from the reference structure) the simulation reenters the relaxation phase. The collective modes are obtained from a coarse-grained Gaussian elastic network model. The scheme, which we call ACM-AME (amplified collective motion-assisted minimum escaping), is compared with conventional simulations as well as an alternative scheme that elevates the temperature of all degrees of freedom during the excitation phase (amplified overall motion-assisted minimum escaping, or AOM-AME). Comparison is made using simulations on four peptides starting from non-native extended or all helical structures. In terms of sampling low energy conformations and continuously sampling new conformations throughout a simulation, the ACM-AME scheme demonstrates very good performance while the AOM-AME scheme shows little improvement upon conventional simulations. Limited success is achieved in producing structures close to the native structures of the peptides: for an S-peptide analog, the ACM-AME approach is able to reproduce its native helical structure, and starting from an all-helical structure of the villin headpiece subdomain (HP-36) in implicit solvent, two out of three 150 ns ACM-AME runs are able to sample structures with 3-4 Angstrom backbone root-mean-square deviations from the nuclear magnetic resonance structure of the protein. (C) 2003 American Institute of Physics.