dorsal/arxiv
View SchemaEnsemble based convergence assessment of biomolecular trajectories
| Authors | Edward Lyman, Daniel M. Zuckerman |
|---|---|
| Categories | |
| ArXiv ID | physics/0601104 |
| URL | https://arxiv.org/abs/physics/0601104 |
| DOI | 10.1529/biophysj.106.082941 |
Abstract
Assessing the convergence of a biomolecular simulation is an essential part of any computational investigation. This is because many important quantities (e.g., free energy differences) depend on the relative populations of different conformers; insufficient convergence translates into systematic errors. Here we present a simple method to self-consistently assess the convergence of a simulation. Standard clustering methods first generate a set of reference structures to any desired precision. The trajectory is then classified by proximity to the reference structures, yielding a one-dimensional histogram of structurally distinct populations. Comparing ensembles of different trajectories (or different parts of the same trajectory) built with the same reference structures provides a sensitive, quantitative measure of convergence. Please note: this is a preliminary manuscript, and should be read as such. Comments are most welcome, especially regarding pertinent prior work.
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"abstract": "Assessing the convergence of a biomolecular simulation is an essential part\nof any computational investigation. This is because many important quantities\n(e.g., free energy differences) depend on the relative populations of different\nconformers; insufficient convergence translates into systematic errors. Here we\npresent a simple method to self-consistently assess the convergence of a\nsimulation. Standard clustering methods first generate a set of reference\nstructures to any desired precision. The trajectory is then classified by\nproximity to the reference structures, yielding a one-dimensional histogram of\nstructurally distinct populations. Comparing ensembles of different\ntrajectories (or different parts of the same trajectory) built with the same\nreference structures provides a sensitive, quantitative measure of convergence.\nPlease note: this is a preliminary manuscript, and should be read as such.\nComments are most welcome, especially regarding pertinent prior work.",
"arxiv_id": "physics/0601104",
"authors": [
"Edward Lyman",
"Daniel M. Zuckerman"
],
"categories": [
"physics.bio-ph",
"physics.chem-ph"
],
"doi": "10.1529/biophysj.106.082941",
"title": "Ensemble based convergence assessment of biomolecular trajectories",
"url": "https://arxiv.org/abs/physics/0601104"
},
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