# PSPI: A deep learning approach for prokaryotic small protein identification

## TL;DR

This paper introduces PSPI, a deep learning tool for identifying small proteins in prokaryotes, which outperforms existing methods in speed and accuracy.

## Contribution

The novel contribution is a deep learning approach specifically designed for prokaryotic small protein identification with improved performance.

## Key findings

- PSPI demonstrated high accuracy in predicting both generalized and human metagenome-specific prokaryotic SPs.
- PSPI outperformed existing tools in precision, sensitivity, and specificity for both prokaryotic and eukaryotic SPs.
- The use of (n, k)-mers significantly enhanced PSPI's performance, indicating the importance of short linear motifs in SPs.

## Abstract

Small Proteins (SPs) are pivotal in various cellular functions such as immunity, defense, and communication. Despite their significance, identifying them is still in its infancy. Existing computational tools are tailored to specific eukaryotic species, leaving only a few options for SP identification in prokaryotes. In addition, these existing tools still have suboptimal performance in SP identification. To fill this gap, we introduce PSPI, a deep learning-based approach designed specifically for predicting prokaryotic SPs. We showed that PSPI had a high accuracy in predicting generalized sets of prokaryotic SPs and sets specific to the human metagenome. Compared with three existing tools, PSPI was faster and showed greater precision, sensitivity, and specificity not only for prokaryotic SPs but also for eukaryotic ones. We also observed that the incorporation of (n, k)-mers greatly enhances the performance of PSPI, suggesting that many SPs may contain short linear motifs. The PSPI tool, which is freely available at https://www.cs.ucf.edu/∼xiaoman/tools/PSPI/, will be useful for studying SPs as a tool for identifying prokaryotic SPs and it can be trained to identify other types of SPs as well.