Visualizing energy landscapes with metric disconnectivity graphs

Lewis C. Smeeton, Mark T. Oakley, Roy L. Johnston

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
278 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1481-1490
JournalJournal of Computational Chemistry
Issue number20
Early online date28 May 2014
Publication statusPublished - 30 Jul 2014

Bibliographical note

Manuscript Accepted: 14 APR 2014


  • collective variables
  • protein
  • coarse-grained models
  • software
  • Python


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