ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding.

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 i

SUBMITTER: Fu H 

PROVIDER: S-EPMC7455913 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature