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Pyshifts

Pyshifts: A Pymol Plugin for Chemical Shift-Based Analysis of Biomolecular Ensembles

Prerequisite

Quick Start

Install Dependencies

git clone https://github.com/atfrank/PyShifts.git
cd PyShifts
source setup.sh

The following commands will invoke a pymol window

pymol

Adding Pyshifts to PyMOL

In PyMOL window, go to Plugin -> Plugin manager -> Install new plugin, choose Pyshifts.py file in your local Pyshifts folder, and click OK on the next step. You will then see a pop-up message Plugin "PyShiftsPlugin" has been installed.

Using Pyshifts

Open PyMOL

manually set evironmental variable and then run PyMOL:
export PYSHIFTS_PATH=~/Downloads/PyShifts
export PATH=$PYSHIFTS_PATH:$PATH
export BME=~/Downloads/PyShifts/BME
export PYTHONPATH=$BME:$PATH

export LARMORD_BIN=~/Downloads/PyShifts/LarmorD_New/bin
export PATH=$LARMORD_BIN:$PATH
export LARMORCA_BIN=~/Downloads/PyShifts/LARMORCA/bin
export PATH=$LARMORCA_BIN:$PATH

conda activate pyshifts
pymol

or

source evironmental variables from bashrc and then run PyMOL
source ~/.bashrc

conda activate pyshifts
pymol

Load Object

Load the object to be analyzed in PyMOL, e.g. 2KOC_test.pdb provided in test/ folder, by typing load test/2KOC_test.pdb in pymol command line or dragging the file into PyMOL window.

Run Pyshifts

Run Pyshifts through Plugin -> Legacy Plugins -> Pyshifts.

Analysis in Pyshifts

  • Change the PyMOL selection/ object entry to the name of your target object, e.g. 2KOC_test and clink on Run.

  • Go to the second tab Error Analysis, and click on Compare shifts.

  • Click on Error table or CS table to save results.

Pyshifts Tabs

Pyshifts has four tabs and one Exit button. The first tab Options include basic options for Pyshifts, the second tab Error Analysis performs chemical shift comparison, displays table results and provides options in different ways of sorting.

The third tab Advanced Options contains functionality for parameter tuning. There are options on chemical shift error offset, accuracy, PyMOL rendering setting as well as machine learning clustering parameters.

Publications

  • Pyshifts(In press) : Jingru Xie, Kexin Zhang and Aaron T. Frank. "PyShifts: A PyMOL Plugin for Chemical Shift-Based Analysis of Biomolecular Ensembles." The Journal of Physical Chemistry B 118.42 (2014): 12168-12175.

  • LarmorD: Frank, Aaron T., Sean M. Law, and Charles L. Brooks III. "A simple and fast approach for predicting 1H and 13C chemical shifts: toward chemical shift-guided simulations of RNA." The Journal of Chemical Information and Modeling (2020).

  • LarmorC⍺: Frank, Aaron T., et al. "Predicting Protein Backbone Chemical Shifts From Cα Coordinates: Extracting High Resolution Experimental Observables from Low Resolution Models." Journal of chemical theory and computation 11.1 (2014): 325-331.

Copyright Notice

The PyMOL Plugin source code in this file is copyrighted, but you can freely use and copy it as long as you don't change or remove any of the copyright notices. This PyMOL Plugin is Copyright (C) 2016 by Jingru Xie , Kexin Zhang and Aaron T. Frank All Rights Reserved

Permission to use, copy, modify, distribute, and distribute modified versions of this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both the copyright notice and this permission notice appear in supporting documentation, and that the name(s) of the author(s) not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission.

THE AUTHOR(S) DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR(S) BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

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Pyshifts: A Pymol Plugin for Chemical Shift-Based Analysis of Biomolecular Ensembles

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