RotamerConvolveMD 1.0

Summary

This package analyses molecular dynamics trajectories or conformational ensembles in terms of spin-label distances as probed in double electron-electron resonance (DEER) experiments. The spin labels are fitted on trajectories and the spin label mobility is taken into account using a rotamer library.

Background

Double electron electron spin resonance (DEER) is an EPR technique for measuring distances between two spin labels that have been covalently attached to a protein. Two cysteine residues are introduced into the protein and subsequently labelled. The positions are chosen to report on the expected conformational change. A commonly used spin label is (1-oxyl-2,2,5,5-tetramethylpyrroline-3-methyl)-methanethiosulfonate (MTSL). MTSL has a linker with five rotatable bonds and is therefore very flexible. The distance distributions between the two spin labels measured by experiments are typically broad and often multi-modal. The distributions are therefore a convolution of the flexibility of the MTSL spin label and the conformational spread of the proteins in the sample. To ensure that we compared like with like we developed a method that

1. maps rotamer libraries of the MTSL spin label onto each position,

2. discards those rotamers that sterically clash with the protein (typically distances <2 Å) and

3. calculates all (weighted) distance pairs between the remaining rotamers and

4. thereby estimates a distance distribution for that structure.

The code was written in Python using the MDAnalysis library [Michaud-Agrawal2011] and a published rotamer library for MTSL [Polyhach2011]. It is available for download from the MDAnalysis website, http://mdanalysis.googlecode.com.

Our approach improves upon the existing method [Polyhach2011] by increasing computational efficiency and implementing, via the MDAnalysis library, analysis of ensembles of hundreds of structures, which allowed us to estimate distance distributions for entire simulation trajectories.

Examples of the application of this approach can be found in [Stelzl2014].

Installation

Typically, install the package with

python setup.py install --user

(see also the file INSTALL)

This will automatically install MDAnalysis and other dependencies. If problems arise, try installing MDAnalysis first (see http://mdanalysis.googlecode.com for help).

Analysis is performed with the script convolve-mtss-rotamers.py, which will have been installed in your bin directory. You might have to add the bin directory to your PATH. Consult your Python documentation for the details although often this will be ~/.local/bin (for a --user installation or /usr/local/bin for site-wide installation).

Usage

Analysis is performed with the script convolve-mtss-rotamers.py. It takes as input

• a topology or structure file (PSF, GRO, PDB, … any topology format recognized by MDAnalysis)

• a trajectory (DCD, XTC, TRR, … any format that MDAnalysis can read)

A typical invocation:

convolve-mtss-rotamers.py --resid 47 330  --histogramBins 0 80 1  --clashDistance 2.2  \
       --output "dat/peptso-xrd"  --dcdfilename "dcd/peptso-xrd-47-330" \
       peptso.gro peptso.xtc

It loads the MD trajectory from the topology peptso.gro and the trajectory peptso.xtc. The --resid pair is required and denotes the residue numbers (in the topology) to which the MTSSL spin labels would be attached. Rotamers that overlap with protein atoms as measured by an atom-atom distance smaller than the --clashDistance will be discarded and not counted in the distance calculations. For further explanations see the --help option.

For an example, see doc/example in the source distribution. The example can also be run to test the installation as reference output is provided.

Help

If you have questions or problems installing the package then ask on the MDAnalysis user mailing list: http://groups.google.com/group/mdnalysis-discussion

References