| Author: | Sean Seyler |
|---|---|
| Year: | 2015 |
| License: | GNU Public Licence, version 3 (or higher) |
| Copyright: | © 2015 Sean Seyler |
| Citation: | SL Seyler, A Kumar, MF Thorpe, O Beckstein. Path Similarity Analysis: a Method for Quantifying Macromolecular Pathways. ArXiv e-prints (2015). arXiv:1505.04807 [q-bio.QM] |
Path Similarity Analysis (PSA) comprises a computational framework designed to enhance the quantitative comparison of macromolecular transition paths [Seyler2015]. This tutorial provides two examples to demonstrate a simple comparison, using PSA, of closed to open adenylate kinase (AdK) transition paths generated by a selection of various algorithms [Seyler2014]. Hierarchical clustering is used as a simple, but powerful approach to exploratory data analysis by construction of a heat map-dendrogram representation of the quantitative comparison.
PSA is based on measuring the geometric similarity of transition paths in configuration space using the Hausdorff and Fréchet path metrics. PSA takes advantage of MDAnalysis [Michaud-Agrawal2011] to provide a seamless interface to the Python and NumPy arrays, and a mechanism for performing path comparisons using arbitrary atom selections. MDAnalysis also provides a format-agnostic framework for reading simulation trajectories, allowing rapid comparison of many different computational methods. More information about the method can be found in [Seyler2015].
This tutorial demonstrates a straightforward application of PSA to a set of transitions of the enzyme adenylate kinase (AdK) generated by a selection of methods (for more background on this particular example see [Seyler2014]). Two example scripts are provided: a short version shows how to perform similarity analysis on a set of trajectories that have been pre-processed for proper (frame-by-frame) structural alignment; a full version additionally demonstrates, using the PSA framework, how an alignment procedure would be performed prior to similarity analysis.
Analysis is performed with the psa_short.py or psa_full.py scripts,
which automatically read trajectories from the methods directory into a
PSA object, perform trajectory alignment (in the case of psa_full.py),
generate discrete Fréchet distance matrices, and produce a heat map-dendrogram
plot representing the distance matrix after Ward hierarchical clustering.
The scripts can be run directly using
python psa_short.py
or
python psa_full.py
The user can also try adjusting settings at the top of each file to change the:
- path metric (default: discrete Fréchet [
discrete_frechet]) - linkage algorithm for hierarchical clustering (default:
Ward) - name and location of the plot (default:
df_ward_psa-[short/full].pdf)
These examples serve as a sufficient basis for understanding PSA's framework. Some other techniques and analyses using PSA are described in [Seyler2015].
If you have questions or problems using the package then ask on the MDAnalysis user mailing list: http://groups.google.com/group/mdnalysis-discussion
If you want to write your own code using PSA then use the MDAnalysis.analysis.psa
module, which is part of MDAnalysis (since release 0.10.0) and have a look at
the documentation of the PSA module.
| [Michaud-Agrawal2011] | N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein. MDAnalysis: A toolkit for the analysis of molecular dynamics simulations. J Comp Chem, 32:2319-2327, 2011. doi:10.1002/jcc.21787. http://www.mdanalysis.org |
| [Seyler2014] | (1, 2) S.L. Seyler and O. Beckstein, Sampling large conformational transitions: adenylate kinase as a testing ground. Mol Simul 40:855–877, 2014. doi:10.1080/08927022.2014.919497 |
| [Seyler2015] | (1, 2, 3) S.L. Seyler, A. Kumar, M.F. Thorpe, and O. Beckstein, Path Similarity Analysis: a Method for Quantifying Macromolecular Pathways. arXiv:1505.04807 [q-bio.QM], 2015 |