pts1-prediction-tool 1.0.2

Tool for predicting a PTS1 (peroxisomal targeting sequence 1) in a amino acid sequence.

pts1-prediction-tool

This project offers a classification-algorithm for predicting the peroxisomal targeting signal 1 (PTS1) in a given amino acid sequence (primary structure of a protein).

Actual version: 1.0.2

Installation

pip install pts1-prediction-tool

PyPi: https://pypi.org/project/pts1-prediction-tool/

Userguide:

Simple example for using the pts1-prediction-tool in your application.

from pts1_prediction_tool.pts1_prediction import PTS1_Predictor

#Instantiates the svm and creates the prediction model
predictor = PTS1_Predictor()

aminoacid_sequence = "MMMMMKLSKMLLLSLSKLSKLSKLSKL"

# Checks a amino acid sequence for an existing PTS1 
result = predictor.check_for_pts1(aminoacid_sequence)

print(result.isPeroxisomal)

Run tests with:
pipenv shell
python -m unittest tests/pts1_prediction_tests.py

Algorithm:

The used classification-algorithm is a support vector machine (svm, sklearn.svm.SVC) from https://scikit-learn.org/.
This machine learning algorithm was trained to predict the PTS1 in a amino acid sequence (aa_sequence) with a dataset of 514 PTS1/peroxisomal and 11.337 not peroxisomal aa_sequences. The peroxisomal dataset was generated out of 2324 peroxisomal aa_sequences, which were filtered for the c-terminal-pts1-tripeptide
(S, A, C, P, H, T, N, Q, E, G, V) / (K, R, H, Q, D, N, S, M) / (L, F, I, M, Y)*

For the training of the svm, the last 14 C-terminal amino acids of the sequence are used. The optimal parameters for the svm and the used c-terminal length were determined by 5-fold-cross validation. For this the perxosiomal and not peroxisomal trainingsets were merged and separated into 80 % training-sets and 20 % validation-sets
The final svm has the following statistical average quantities:

• Specifity = 1.0
• Sensitivity = 0.86
• Precision = 0.98

Learning data for the svm

The aa_sequences for the learning-sets are downloaded from UniProt (https://www.uniprot.org/), 23.07.2020.

An example application will be published on my website, https://olis-lab.de/
@Copyright 2021, Oliver Koch

Sources:

1. Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
2. https://scikit-learn.org/, 11.04.2021
3. https://www.uniprot.org/), 23.07.2020
4. https://biopython.org/, 11.04.2021