PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model.

ABSTRACT: Identifying anti-inflammatory peptides (AIPs) and antimicrobial peptides (AMPs) is crucial for the discovery of innovative and effective peptide-based therapies targeting inflammation and microbial infections. However, accurate identification of AIPs and AMPs remains a computational challenge mainly due to limited utilization of peptide sequence information. Here, we propose PepNet, an interpretable neural network for predicting both AIPs and AMPs by applying a pre-trained protein language model to fully utilize the peptide sequence information. It first captures the information of residue arrangements and physicochemical properties using a residual dilated convolution block, and then seizes the function-related diverse information by introducing a residual Transformer block to characteriz

SUBMITTER: Han J 

PROVIDER: S-EPMC11438969 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature