Long short-term memory-based deep learning model for the discovery of antimicrobial peptides targeting <i>Mycobacterium tuberculosis</i>.

ABSTRACT:

Motivation

Tuberculosis, caused by Mycobacterium tuberculosis, remains a global health challenge driven by rising antibiotic resistance. Antimicrobial peptides offer a promising alternative due to membrane-disruptive activity and low resistance potential, yet the scarcity of TB-specific AMP data constrains targeted development. We present a reproducible deep learning protocol that integrates long short-term memory networks with transfer learning to classify and generate TB-active peptides.

Results

Classifiers were pretrained on a large corpus of general AMPs and fine-tuned on curated TB-specific sequences using frozen encoder and full backpropagation strategies. We benchmarked four model variants [unidirectional and bidirectional long short-term memories (LSTMs), with

SUBMITTER: Wang L 

PROVIDER: S-EPMC12603352 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature