ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding
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RESEARCH ARTICLE
Open Access
ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding Haoyi Fu1 , Zicheng Cao2 , Mingyuan Li1 and Shunfang Wang1*
Abstract Background: Antimicrobial resistance is one of our most serious health threats. Antimicrobial peptides (AMPs), effecter molecules of innate immune system, can defend host organisms against microbes and most have shown a lowered likelihood for bacteria to form resistance compared to many conventional drugs. Thus, AMPs are gaining popularity as better substitute to antibiotics. To aid researchers in novel AMPs discovery, we design computational approaches to screen promising candidates. Results: In this work, we design a deep learning model that can learn amino acid embedding patterns, automatically extract sequence features, and fuse heterogeneous information. Results show that the proposed model outperforms state-of-the-art methods on recognition of AMPs. By visualizing data in some layers of the model, we overcome the black-box nature of deep learning, explain the working mechanism of the model, and find some import motifs in sequences. Conclusions: ACEP model can capture similarity between amino acids, calculate attention scores for different parts of a peptide sequence in order to spot important parts that significantly contribute to final predictions, and automatically fuse a variety of heterogeneous information or features. For high-throughput AMPs recognition, open source software and datasets are made freely available at https://github.com/Fuhaoyi/ACEP. Keywords: Antimicrobial resistance, Antimicrobial peptide, Deep learning, Feature fusion, Visualization
Background Antimicrobial resistance is one of our most serious health threats. Infections from resistant bacteria are now too common, and some pathogens have even become resistant to the multiple types of antibiotics [1]. Natural antimicrobials, known as host defense peptides or antimicrobial peptides (AMPs), defend host organisms against microbes, and most have shown a lowered likelihood for bacteria to form resistance compared to many conventional drugs [2]. AMPs have been demonstrated to kill Gram-negative and Gram-positive bacteria, enveloped *Correspondence: [email protected] School of Information Science and Engineering, Yunnan University, 650500 Kunming, China Full list of author information is available at the end of the article 1
viruses, fungi and even transformed or cancerous cells; thus, AMPs are considered as potential novel antimicrobial compounds [3]. Unlike the majority of conventional antibiotics, AMPs frequently destabilize biological membranes, form transmembrane channels and may also have the ability to enhance immunity by functioning as immunomodulators [4]. Over the last few decades, several AMPs have successfully been approved as drugs by FDA, which has prompted an interest in these AMPs. To aid researchers in novel AMP discovery, a variety of computational approaches are proposed for AMP recognition. Many incorporate machi
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