Persistent topology and metastable state in conformational dynamics.
The large amount of molecular dynamics simulation data produced by modern computational models brings big opportunities and challenges to researchers. Clustering algorithms play an important role in understanding biomolecular kinetics from the simulation data, especially under the Markov state model...
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doaj-c0f56c7786ee47f199fba0fdacef37722020-11-25T01:14:10ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0184e5869910.1371/journal.pone.0058699Persistent topology and metastable state in conformational dynamics.Huang-Wei ChangSergio BacalladoVijay S PandeGunnar E CarlssonThe large amount of molecular dynamics simulation data produced by modern computational models brings big opportunities and challenges to researchers. Clustering algorithms play an important role in understanding biomolecular kinetics from the simulation data, especially under the Markov state model framework. However, the ruggedness of the free energy landscape in a biomolecular system makes common clustering algorithms very sensitive to perturbations of the data. Here, we introduce a data-exploratory tool which provides an overview of the clustering structure under different parameters. The proposed Multi-Persistent Clustering analysis combines insights from recent studies on the dynamics of systems with dominant metastable states with the concept of multi-dimensional persistence in computational topology. We propose to explore the clustering structure of the data based on its persistence on scale and density. The analysis provides a systematic way to discover clusters that are robust to perturbations of the data. The dominant states of the system can be chosen with confidence. For the clusters on the borderline, the user can choose to do more simulation or make a decision based on their structural characteristics. Furthermore, our multi-resolution analysis gives users information about the relative potential of the clusters and their hierarchical relationship. The effectiveness of the proposed method is illustrated in three biomolecules: alanine dipeptide, Villin headpiece, and the FiP35 WW domain.http://europepmc.org/articles/PMC3614941?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huang-Wei Chang Sergio Bacallado Vijay S Pande Gunnar E Carlsson |
spellingShingle |
Huang-Wei Chang Sergio Bacallado Vijay S Pande Gunnar E Carlsson Persistent topology and metastable state in conformational dynamics. PLoS ONE |
author_facet |
Huang-Wei Chang Sergio Bacallado Vijay S Pande Gunnar E Carlsson |
author_sort |
Huang-Wei Chang |
title |
Persistent topology and metastable state in conformational dynamics. |
title_short |
Persistent topology and metastable state in conformational dynamics. |
title_full |
Persistent topology and metastable state in conformational dynamics. |
title_fullStr |
Persistent topology and metastable state in conformational dynamics. |
title_full_unstemmed |
Persistent topology and metastable state in conformational dynamics. |
title_sort |
persistent topology and metastable state in conformational dynamics. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2013-01-01 |
description |
The large amount of molecular dynamics simulation data produced by modern computational models brings big opportunities and challenges to researchers. Clustering algorithms play an important role in understanding biomolecular kinetics from the simulation data, especially under the Markov state model framework. However, the ruggedness of the free energy landscape in a biomolecular system makes common clustering algorithms very sensitive to perturbations of the data. Here, we introduce a data-exploratory tool which provides an overview of the clustering structure under different parameters. The proposed Multi-Persistent Clustering analysis combines insights from recent studies on the dynamics of systems with dominant metastable states with the concept of multi-dimensional persistence in computational topology. We propose to explore the clustering structure of the data based on its persistence on scale and density. The analysis provides a systematic way to discover clusters that are robust to perturbations of the data. The dominant states of the system can be chosen with confidence. For the clusters on the borderline, the user can choose to do more simulation or make a decision based on their structural characteristics. Furthermore, our multi-resolution analysis gives users information about the relative potential of the clusters and their hierarchical relationship. The effectiveness of the proposed method is illustrated in three biomolecules: alanine dipeptide, Villin headpiece, and the FiP35 WW domain. |
url |
http://europepmc.org/articles/PMC3614941?pdf=render |
work_keys_str_mv |
AT huangweichang persistenttopologyandmetastablestateinconformationaldynamics AT sergiobacallado persistenttopologyandmetastablestateinconformationaldynamics AT vijayspande persistenttopologyandmetastablestateinconformationaldynamics AT gunnarecarlsson persistenttopologyandmetastablestateinconformationaldynamics |
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1725158314448257024 |