# ProToxin, a Predictor of Protein Toxicity

## TL;DR

ProToxin is a new machine learning tool that accurately predicts protein toxicity from sequence data, outperforming existing methods.

## Contribution

A novel and improved machine learning-based predictor for identifying protein toxins from sequences.

## Key findings

- ProToxin achieved an accuracy of 0.906 on a blind test dataset.
- The Matthews correlation coefficient and overall performance measure were both 0.796.
- ProToxin outperformed existing state-of-the-art toxin prediction tools.

## Abstract

Toxins are naturally poisonous small compounds, peptides and proteins that are produced in all three kingdoms of life. Venoms are animal toxins and can contain even hundreds of different compounds. Numerous approaches have been used to detect toxins, including prediction methods. We developed a novel machine learning-based predictor for detecting protein toxins from their sequences. The gradient boosting method was trained on carefully selected training data. Initially, we tested 2614 features, which were reduced to 88 after a comprehensive feature selection procedure. Out of the four tested algorithms, XGBoost was chosen to train the final predictor. Comparison to available predictors indicated that ProToxin showed significant improvement compared to state-of-the-art predictors. On a blind test dataset, the accuracy was 0.906, the Matthews correlation coefficient was 0.796, and the overall performance measure was 0.796. ProToxin is a fast and efficient method and is freely available. It can be used for small and large numbers of sequences.