ArticleCommunications biology2024
PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- HD-AIP: a heterogeneous dual-stream alignment-free framework for anti-inflammatory peptide prediction based on language models and CT-Net.Bioinformatics (Oxford, England) · 2026Article
- A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.PLoS computational biology · 2026Article
- GDA-Pred: Generative AI-Driven Data Augmentation for Improved Prediction of IL-6 and IL-13-Inducing Peptides.International journal of molecular sciences · 2026Article
- Integrated Genome Mining, Bacterial Co-Culture Activation, and Peptidomic Analyses Identify Antimicrobial Peptide Candidates from South American Bacteria.Antibiotics (Basel, Switzerland) · 2026Article
- Discovery of potent low-toxicity antimicrobial peptides through diffusion modeling.Nature communications · 2026Article
- NeuroPred-GMC: a dual-branch deep learning architecture for neuropeptide prediction based on gated dilated convolutional network and multi-scale convolutional network.Journal of computer-aided molecular design · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- Spider Venom-Derived Peptide Exhibits Dual Anti-Inflammatory and Antioxidative Activities in LPS-Stimulated BEAS-2B Cells.Antioxidants (Basel, Switzerland) · 2025Article
- Artificial intelligence-curated repository of gene-encoded natural diverse components from herbal medicines.Innovation (Cambridge (Mass.)) · 2025Article
- Antimicrobial peptide prediction based on contrastive learning and gated convolutional neural network.Scientific reports · 2025Article
- Omics-based profiling and therapeutic potential of natural components in pan-Shennongjia medicinal herbs.Chinese medicine · 2025Article
- eCBT-I dialogue system: a comparative evaluation of large language models and adaptation strategies for insomnia treatment.Journal of translational medicine · 2025Article
- Unveiling the Bioactive Potential of the Invasive JellyfishBiomolecules · 2025Article
- SimPep and OP-AND: A deep learning framework and curated database for predicting osteogenic peptides.PLoS computational biology · 2025Article
- Advancing therapeutic vaccines for chronic hepatitis B: Integrating reverse vaccinology and immunoinformatics.World journal of hepatology · 2025Review
- NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides.BMC biology · 2025Article
- Synthesis and Evaluation of Aquatic Antimicrobial Peptides Derived from Marine Metagenomes Using a High-Throughput Screening Approach.Marine drugs · 2025Article
- PLM-ATG: Identification of Autophagy Proteins by Integrating Protein Language Model Embeddings with PSSM-Based Features.Molecules (Basel, Switzerland) · 2025Article
- Leveraging large language models for peptide antibiotic design.Cell reports. Physical science · 2025Article
- ToxiPep: Peptide toxicity prediction via fusion of context-aware representation and atomic-level graph.Computational and structural biotechnology journal · 2025Article
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3 authors.
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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 characterize the residue representations generated by a pre-trained protein language model. After training and testing, PepNet demonstrates great superiority over other leading AIP and AMP predictors and shows strong interpretability of its learned peptide representations. A user-friendly web server for PepNet is freely available at http://liulab.top/PepNet/server .
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