pHisPred

A bioinformatic tool for identifying protein histidine phosphorylation sites

Installation

The following software should be installed in your cluster or computer before running the pHisPred.py.

•     Python (>= 3.7), https://www.python.org/downloads/.
  

•     The scikit-learn module, http://scikit-learn.org/stable/install.html.
  

In most use cases the best way to install Python and scikit-learn package on your system is by using Anaconda(https://www.continuum.io), which is an easy-to-install free Python distirbution and includes more than 400 of the most popular Python packages. Anaconda includes installers(https://www.continuum.io/downloads) for Windows, OS X, and Linux.

Usage for predicting pHis sites

Command:
	python pHisPred.py -f input.fa -o output -t euka
Options:
	-h, --help      show this help message and exit
	-f input files, --file=input files
            	        enter proteins in .fasta (.fa) format.
	-o output files, --out=output files
            	        assign your output file.
	-t class type, --type=class type
            	        eukaryotes: euka (default); prokaryotes: proka.
	-w window size, --window=window size
            	        specific the window size used for extracting the peptides around histidine sites.
	-m trained model, --model=trained model
            	        specific your own trained model.

Usage for building classification model

Command:
	python train_model.py -p pHis.fa -n non_pHis.fa -o model.tsv -t proka
Options:
	-h, --help          show this help message and exit
	-p positive samples, --positive=positive samples
            	            enter peptides around pHis sites in .fasta (.fa) format.
	-n negative samples, --negative=negative samples
            	            enter peptides around non-pHis sites in .fasta (.fa) format.
	-o output files, --out=output files
            	            assign your output file.
	-t class type, --type=class type
            	            eukaryotes: euka (default); prokaryotes: proka.

Examples

• Predicting pHis sites with protein sequences or peptide sequences centered by His site

Commands:
	python pHisPred.py -f examples/euka_proteins.fa -o euka_result.txt -t euka
	python pHisPred.py -f examples/proka_proteins.fa -o proka_result.txt -t proka
	python pHisPred.py -f examples/negatve.fa -o euka_result.txt -t euka

Note: The length of peptide sequence should be 31 and 25 respectively for euka and proka.

• Predicting pHis sites with the model built on your own data

Commands:
	python train_model.py -p exampels/positive.fa -n examples/negative.fa -o euka_model.tsv -t euka
	python pHisPred.py -f examples/euka_proteins.fa -o euka_result.txt -t euka -m euka_model.tsv

Note: For training model, the input files should be peptide sequences centered by His site.
      The length of peptide sequence is better set to 31 and 25 respectively for euka and proka; if not, please use the parameter -w to specific the length.

Author

pHisPred is developed by Jian Zhao (zhao_doctor@hotmail.com). For questions and comments, please contact Jian or submit an issue on github.