A black-box re-weighting analysis can correct flawed simulation data

F. Marty Ytreberg, Daniel M. Zuckerman

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)7982-7987
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number23
StatePublished - Jun 10 2008
Externally publishedYes


  • Canonical sampling
  • Free energy
  • Molecular simulation
  • Non-Boltzmann

ASJC Scopus subject areas

  • General


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