Visualizing energy landscapes with metric disconnectivity graphs

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Visualizing energy landscapes with metric disconnectivity graphs. / Smeeton, Lewis C.; Oakley, Mark T.; Johnston, Roy L.

In: Journal of Computational Chemistry, Vol. 35, No. 20, 30.07.2014, p. 1481-1490.

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@article{2c6ab6352bf04796b52858282d5460fe,
title = "Visualizing energy landscapes with metric disconnectivity graphs",
abstract = "The visualization of multidimensional energy landscapes is important, providing insight into the kinetics and thermodynamics of a system, as well the range of structures a system can adopt. It is, however, highly nontrivial, with the number of dimensions required for a faithful reproduction of the landscape far higher than can be represented in two or three dimensions. Metric disconnectivity graphs provide a possible solution, incorporating the landscape connectivity information present in disconnectivity graphs with structural information in the form of a metric. In this study, we present a new software package, PyConnect, which is capable of producing both disconnectivity graphs and metric disconnectivity graphs in two or three dimensions. We present as a test case the analysis of the 69-bead BLN coarse-grained model protein and show that, by choosing appropriate order parameters, metric disconnectivity graphs can resolve correlations between structural features on the energy landscape with the landscapes energetic and kinetic properties.",
keywords = "collective variables, protein, coarse-grained models, software, Python",
author = "Smeeton, {Lewis C.} and Oakley, {Mark T.} and Johnston, {Roy L.}",
note = "Manuscript Accepted: 14 APR 2014",
year = "2014",
month = jul,
day = "30",
doi = "10.1002/jcc.23643",
language = "English",
volume = "35",
pages = "1481--1490",
journal = "Journal of Computational Chemistry",
issn = "0192-8651",
publisher = "Wiley",
number = "20",

}

RIS

TY - JOUR

T1 - Visualizing energy landscapes with metric disconnectivity graphs

AU - Smeeton, Lewis C.

AU - Oakley, Mark T.

AU - Johnston, Roy L.

N1 - Manuscript Accepted: 14 APR 2014

PY - 2014/7/30

Y1 - 2014/7/30

N2 - The visualization of multidimensional energy landscapes is important, providing insight into the kinetics and thermodynamics of a system, as well the range of structures a system can adopt. It is, however, highly nontrivial, with the number of dimensions required for a faithful reproduction of the landscape far higher than can be represented in two or three dimensions. Metric disconnectivity graphs provide a possible solution, incorporating the landscape connectivity information present in disconnectivity graphs with structural information in the form of a metric. In this study, we present a new software package, PyConnect, which is capable of producing both disconnectivity graphs and metric disconnectivity graphs in two or three dimensions. We present as a test case the analysis of the 69-bead BLN coarse-grained model protein and show that, by choosing appropriate order parameters, metric disconnectivity graphs can resolve correlations between structural features on the energy landscape with the landscapes energetic and kinetic properties.

AB - The visualization of multidimensional energy landscapes is important, providing insight into the kinetics and thermodynamics of a system, as well the range of structures a system can adopt. It is, however, highly nontrivial, with the number of dimensions required for a faithful reproduction of the landscape far higher than can be represented in two or three dimensions. Metric disconnectivity graphs provide a possible solution, incorporating the landscape connectivity information present in disconnectivity graphs with structural information in the form of a metric. In this study, we present a new software package, PyConnect, which is capable of producing both disconnectivity graphs and metric disconnectivity graphs in two or three dimensions. We present as a test case the analysis of the 69-bead BLN coarse-grained model protein and show that, by choosing appropriate order parameters, metric disconnectivity graphs can resolve correlations between structural features on the energy landscape with the landscapes energetic and kinetic properties.

KW - collective variables

KW - protein

KW - coarse-grained models

KW - software

KW - Python

U2 - 10.1002/jcc.23643

DO - 10.1002/jcc.23643

M3 - Article

C2 - 24866379

VL - 35

SP - 1481

EP - 1490

JO - Journal of Computational Chemistry

JF - Journal of Computational Chemistry

SN - 0192-8651

IS - 20

ER -