Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 7982-7987 |
| Number of pages | 6 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Volume | 105 |
| Issue number | 23 |
| DOIs | |
| State | Published - Jun 10 2008 |
| Externally published | Yes |
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Keywords
- Canonical sampling
- Free energy
- Molecular simulation
- Non-Boltzmann
ASJC Scopus subject areas
- General
Cite this
A black-box re-weighting analysis can correct flawed simulation data. / Ytreberg, F. Marty; Zuckerman, Daniel.
In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 105, No. 23, 10.06.2008, p. 7982-7987.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A black-box re-weighting analysis can correct flawed simulation data
AU - Ytreberg, F. Marty
AU - Zuckerman, Daniel
PY - 2008/6/10
Y1 - 2008/6/10
N2 - 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.
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.
KW - Canonical sampling
KW - Free energy
KW - Molecular simulation
KW - Non-Boltzmann
UR - http://www.scopus.com/inward/record.url?scp=45849133760&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=45849133760&partnerID=8YFLogxK
U2 - 10.1073/pnas.0706063105
DO - 10.1073/pnas.0706063105
M3 - Article
C2 - 18544653
AN - SCOPUS:45849133760
VL - 105
SP - 7982
EP - 7987
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
SN - 0027-8424
IS - 23
ER -