*** Multipipsa ** Python wrapper for                         ***
*** Protein Interaction Property Similarity Analysis - PIPSA *** 

Date:      April, 2019
Authors:   Stefan Richter, Neil Bruce, Rebecca Wade
Copyright: HITS gGmbH, Schloss-Wolfsbrunnenweg 35c, 69118 Heidelberg 
           www.h-its.org
Contact:   mcmsoft@h-its.org
License:   EUPL (https://eupl.eu/1.2/en) 
           according to the LICENSE.EUPL file included in the distribution

THE LICENSE IS ACCEPTED BY USING THE SOFTWARE

The original PIPSA code is included in this distribution in the 
multipipsa/data/pipsa/ directory. This distribution has its own 
Copyright, License and Authorship information. By using this software,
you also agree to the license given in the multipipsa/data/pipsa/LICENSE
file.

Citation:  By using this software, you agree to cite the following references 
when publishing results obtained by the software:

Any application of PIPSA should cite the original papers:

Blomberg, N., Gabdoulline,R.R., Nilges, M., Wade,R.C. Classification of protein
sequences by homology modeling and quantitative analysis of electrostatic 
similarity. Proteins (1999) 37, 379-387.
Wade,R.C., Gabdoulline,R.R., De Rienzo, F.  
Protein Interaction Property Similarity
Analysis Intl. J. Quant. Chem. (2001) 83, 122-127.

Any application of webPIPSA should also cite:

Richter S, Wenzel A, Stein M, Gabdoulline RR, Wade RC. 
webPIPSA: a web server for the comparison of protein interaction properties. 
Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):
W276-80. doi: 10.1093/nar/gkn181.

Any application of PIPSA for comparative analysis of the binding properties of 
user-defined groups of proteins using "multipipsa", should also cite:

Tong R, Wade RC, Bruce NJ. Comparative electrostatic analysis of adenylyl
cyclase for isoform dependent regulation properties. Proteins. 2016
Dec;84(12):1844-1858. doi: 10.1002/prot.25167.



This distribution of PIPSA 4.0 contains the standard PIPSA (ver. 3.2) code for 
running a PIPSA similiarity analysis on the whole protein skin or on selected 
parts of the protein skin (selected spheres, cones or cylinders) and in 
addition  has a python module for running several PIPSA runs on a single 
protein and comparing these with statistical measures.

The standard PIPSA distribution is located in the "pipsa" subdirectory. It is 
distributed with all the documentation, which is also found online:
https://projects.h-its.org/mcmsoft/pipsa/latest/usrguide.html
This version of PIPSA can also be run online using a webserver:
http://pipsa.h-its.org

In addition, this PIPSA distribution allows the user to run PIPSA within python
and to do a PIPSA analysis of the whole protein skin or on spheres centered on 
many points selected by the user, eg. all C-alpha carbon atoms. An image of the 
clustering is produced for each region analysed. Also one can compare clusters 
to a given reference cluster and write a PDB file with the scores in the 
B-Factor column of the PDB file.

The method described in [1] can also be run using the python modules. It 
reproduces the Fig. 5 of the reference. 
This method allows the comparative analysis of the molecular electrostatic 
potentials of two preselected groups of protein isoforms in order to obtain  
information on binding properties. The scores defined in [1] are written to 
the B-Factor column of the PDB file and can be visualized with any molecular 
viewer capable of coloring by B-Factor.

An example of how to use the python module is given in 
multipipsa/testMultipipsa.py and in the main method of the module file: 
multipipsa/multipipsa.py.
This script runs for approximately 15-20 minutes on a workstation.

A description of how to install PIPSA is given in the "pipsa" subdirectory.
The python module installation is described in the INSTALL file.

[1] Tong, Wade and Bruce, Proteins 2016; 84:1844-1858
