PTM-X Files

PTM-X

What is PTM-X?

PTM-X is a software to predict the cross-talk between two post-translational modification sites within a protein, by abstracting multiple features and classfying with a naive Bayes model. The features include sequence distance, structural distance, disordered region location, residue co-evolution, and modification co-evolution.

Software by Yuanhua Huang. PTM-X is also implemented as a web server [1] with frame by Ming Lu. The performance is explained in more detail in the accompanying publication [2].

Philosophy

Post-translational modification (PTM) plays an important role in regulating the functions of proteins. PTM of multiple residues on one protein may work together to determine a functional outcome, which is known as PTM cross-talk. Identification of PTM cross-talks is an emerging theme in proteomics and has elicited great interest, but their properties remain to be systematically characterized. To this end, we collected 193 PTM cross-talk pairs in 77 human proteins from the literature, and then tested location preference, and co-evolution at the residue and modification levels. We found that cross-talk events preferentially occurred among nearby PTM sites, especially in disordered protein regions; and cross-talk pairs tended to co-evolve. Given the properties of PTM cross-talk pairs, a naïve Bayes classifier integrating different features was built to predict cross-talks for pairwise combination of PTM sites.

Installation:

    PTM-X is particularly easy to install using the anaconda python distribution [3], which covers all packages you need.

    It requires Python 2.7 or later with
        Numpy, Scipy, h5py, Scikit, pylab

    If the solfware version is seperated with data, please download the data and put it in the directory ./data/ or by
    	$ mv data_path/data/* PTM-X_path/data/.

How to use PTM-X?

The current software version should be considered as beta. Still, the method is working and can be used to reproduce the result of the accompanying publication [1]. 

The PTM-X is mainly designed for Linux/Mac user by command line. For Windows user, you could simply install Cygwin [4], then you can use the command line below.

Easily getting started

1. Predict test samples:
	1.1 Write the file for test samples ./interface/test_samples.txt
		format:
		protein	residue1	PTM1	residue2	PTM2
		P04637	T387	phosphorylation	K382	acetylation
		O95786	T170	phosphorylation	K172	ubiquitination

	1.2 go to ./scr/ directory, and you will see a command file "step1.0_prediction.sh", then run it by

		$ sh step1.0_prediction.sh

		It may take a few minutes, and then you will see the prediction in th output file:
		./interface/prediction_results.txt


2. Update training samples (Note: only in case you have some new PTM cross-talk data and want to update the training set, then run this step, otherwise skip it!!):
	2.1 Update cross-talk samples ./interface/crosstalkSamples/PTM_crosstalk_Release.txt
		format:
		protein	residue1	PTM1	residue2	PTM2	relationshipe(optional)
		P04637	T387	phosphorylation	K382	acetylation
		O95786	T170	phosphorylation	K172	ubiquitination

	2.2 go to ./scr/ directory and you will see a command file "step0.1_update4training.sh", then run it by

		$ sh step0.1_update4training.sh

		Then the training set will have been updated, see the new feature files ./interface/training_positive.hdf5 and ./interface/training_negative.hdf5 


If you want to use PTM-X and encounter any issues, please contact us by email: huangyh094@gmail.com.

This software is free for academic use. Feel free to use or change it. 
But for commercial use, please contact with the author first.

References

[1] PTM-X web server: http://bioinfo.bjmu.edu.cn/ptm-x/

[2] Huang Y., Xu B., Zhou X., Li Y., Lu M., Jiang R., & Li T. Systematic Characterization and Prediction of Post-Translational Modification Cross-talk. Mol Cell Proteomics mcp.M114.037994. doi:10.1074/mcp.M114.037994

[3] Anaconda distribution for python: https://store.continuum.io/cshop/anaconda/

[4] Cygwin, Get that Linux feeling - on Windows: https://www.cygwin.com/