ArticleBriefings in bioinformatics2023
iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- PepPharmaHub: a cloud-based platform integrating multimodel language architectures with curated data resources for therapeutic peptide discovery.BMC biology · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network.Scientific reports · 2026Article
- Prediction of minimum inhibitory concentration of antibacterial peptides using geometric graph networks and dynamic loss functions.Briefings in bioinformatics · 2026Article
- A dual diffusion model-based representation learning framework for antimicrobial peptides classification.Bioinformatics (Oxford, England) · 2026Article
- Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation.Frontiers in molecular biosciences · 2026Review
- Article
- DeepPlantAllergy: deep learning for explainable prediction of allergenicity in plant proteins.Briefings in bioinformatics · 2025Article
- BBATProt: a framework predicting biological function with enhanced feature extraction via interpretable deep learning.Briefings in bioinformatics · 2025Article
- In Silico Identification and Molecular Characterization ofAntibiotics (Basel, Switzerland) · 2025Article
- Article
- Pepxml: ESM2-based extreme multilabel classification of pathogen-targeted antimicrobial peptides.Briefings in bioinformatics · 2025Article
- Article
- AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.Accounts of chemical research · 2025Review
- BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides forJournal of chemical information and modeling · 2025Article
- Leveraging large language models for peptide antibiotic design.Cell reports. Physical science · 2025Article
- UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides.BMC bioinformatics · 2025Article
- sRNAdeep: a novel tool for bacterial sRNA prediction based on DistilBERT encoding mode and deep learning algorithms.BMC genomics · 2024Article
- dsAMP and dsAMPGAN: Deep Learning Networks for Antimicrobial Peptides Recognition and Generation.Antibiotics (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.
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