PT - JOURNAL ARTICLE
AU - Ytreberg, F. Marty
AU - Zuckerman, Daniel M.
TI - A black-box re-weighting analysis can correct flawed simulation data
AID - 10.1073/pnas.0706063105
DP - 2008 Jun 10
TA - Proceedings of the National Academy of Sciences
PG - 7982--7987
VI - 105
IP - 23
4099 - http://www.pnas.org/content/105/23/7982.short
4100 - http://www.pnas.org/content/105/23/7982.full
SO - Proc Natl Acad Sci USA2008 Jun 10; 105
AB - There is a great need for improved statistical sampling in a range of physical, chemical, and biological systems. Even simulations based on correct algorithms suffer from statistical error, which can be substantial or even dominant when slow processes are involved. Further, in key biomolecular applications, such as the determination of protein structures from NMR data, non-Boltzmann-distributed ensembles are generated. We therefore have developed the “black-box” strategy for reweighting a set of configurations generated by arbitrary means to produce an ensemble distributed according to any target distribution. In contrast to previous algorithmic efforts, the black-box approach exploits the configuration-space density observed in a simulation, rather than assuming a desired distribution has been generated. Successful implementations of the strategy, which reduce both statistical error and bias, are developed for a one-dimensional system, and a 50-atom peptide, for which the correct 250-to-1 population ratio is recovered from a heavily biased ensemble.