dorsal/arxiv
View SchemaThe structural de-correlation time: A robust statistical measure of convergence of biomolecular simulations
| Authors | Edward Lyman, Daniel M. Zuckerman |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0607037 |
| URL | https://arxiv.org/abs/q-bio/0607037 |
Abstract
Although atomistic simulations of proteins and other biological systems are approaching microsecond timescales, the quality of trajectories has remained difficult to assess. Such assessment is critical not only for establishing the relevance of any individual simulation but also in the extremely active field of developing computational methods. Here we map the trajectory assessment problem onto a simple statistical calculation of the ``effective sample size'' - i.e., the number of statistically independent configurations. The mapping is achieved by asking the question, ``How much time must elapse between snapshots included in a sample for that sample to exhibit the statistical properties expected for independent and identically distributed configurations?'' The resulting ``structural de-correlation time'' is robustly calculated using exact properties deduced from our previously developed ``structural histograms,'' without any fitting parameters. We show the method is equally and directly applicable to toy models, peptides, and a 72-residue protein model. Variants of our approach can readily be applied to a wide range of physical and chemical systems.
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"abstract": "Although atomistic simulations of proteins and other biological systems are\napproaching microsecond timescales, the quality of trajectories has remained\ndifficult to assess. Such assessment is critical not only for establishing the\nrelevance of any individual simulation but also in the extremely active field\nof developing computational methods. Here we map the trajectory assessment\nproblem onto a simple statistical calculation of the ``effective sample size\u0027\u0027\n- i.e., the number of statistically independent configurations. The mapping is\nachieved by asking the question, ``How much time must elapse between snapshots\nincluded in a sample for that sample to exhibit the statistical properties\nexpected for independent and identically distributed configurations?\u0027\u0027 The\nresulting ``structural de-correlation time\u0027\u0027 is robustly calculated using exact\nproperties deduced from our previously developed ``structural histograms,\u0027\u0027\nwithout any fitting parameters. We show the method is equally and directly\napplicable to toy models, peptides, and a 72-residue protein model. Variants of\nour approach can readily be applied to a wide range of physical and chemical\nsystems.",
"arxiv_id": "q-bio/0607037",
"authors": [
"Edward Lyman",
"Daniel M. Zuckerman"
],
"categories": [
"q-bio.QM",
"q-bio.BM"
],
"title": "The structural de-correlation time: A robust statistical measure of convergence of biomolecular simulations",
"url": "https://arxiv.org/abs/q-bio/0607037"
},
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