# AEPMA: peptide–microbe association prediction based on autoevolutionary heterogeneous graph learning

## TL;DR

This paper introduces AEPMA, a computational framework that predicts which antimicrobial peptides target specific microbes, helping to develop new antibiotics more efficiently.

## Contribution

AEPMA introduces a novel autoevolutionary heterogeneous graph learning framework for targeted prediction of peptide–microbe associations.

## Key findings

- AEPMA outperforms five state-of-the-art methods on peptide–microbe and drug–microbe datasets.
- The model identifies two novel peptides targeting Staphylococcus aureus and Escherichia coli.

## Abstract

The inappropriate use of antibiotics has precipitated the emergence of multidrug-resistant bacteria, prompting significant interest in antimicrobial peptides (AMPs) as potential alternatives to traditional antibiotics. Given the prohibitive costs and time-consuming nature of biological experiments, computational methods provide an efficient alternative for the development of AMP-based drugs. However, existing computational studies primarily focus on identifying AMPs with antimicrobial activity, lacking a targeted identification of AMPs against specific microbial species. To address this gap, we propose a peptide–microbe association (PMA) prediction framework, termed AEPMA, which is constructed based on an autoevolutionary heterogeneous graph. Within AEPMA, we construct an innovative peptide-microbe-disease network (PMDHAN). Furthermore, we design an autoevolutionary information aggregation mechanism that facilitates the representation learning of the heterogeneous graph. This model automatically aggregates semantic information within the heterogeneous network while thoroughly accounting for the spatiotemporal dependencies and heterogeneous interactions in the PMDHAN. Experiments conducted on one peptide-microbe and three drug–microbe association datasets demonstrate that the performance of AEPMA outperforms five state-of-the-art methods, demonstrating its robust modeling capability and exceptional generalization ability. In addition, this study identifies a novel anti-Staphylococcus aureus peptide and an anti-Escherichia coli peptide, thereby contributing valuable information for the development of antimicrobial drugs and strategies for mitigating antibiotic resistance.