ArticleBriefings in bioinformatics2021
Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 56 citations in OpenAlex.
- MBPBERT: A Large Language Model for Metal-Binding Peptide Discovery.Interdisciplinary sciences, computational life sciences · 2026Article
- BiToxNet: a deep learning framework integrating multimodal features for accurate identification of neurotoxic peptides and proteins.BMC biology · 2026Article
- Enhancing the Feature Representation of Protein Sequence Descriptors in Protein-Protein Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- MFE-ACVP: anti-coronavirus peptide prediction based on multimodal feature extraction and ensemble learning.Briefings in bioinformatics · 2025Article
- Advancing the Accuracy of Anti-MRSA Peptide Prediction Through Integrating Multi-Source Protein Language Models.Interdisciplinary sciences, computational life sciences · 2025Article
- Antimicrobial Peptides: Mechanism, Expressions, and Optimization Strategies.Probiotics and antimicrobial proteins · 2025Review
- iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.Current medicinal chemistry · 2025Article
- EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.Current medicinal chemistry · 2025Article
- ACVPICPred: Inhibitory activity prediction of anti-coronavirus peptides based on artificial neural network.Computational and structural biotechnology journal · 2024Article
- Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.Interdisciplinary sciences, computational life sciences · 2024Article
- Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.Interdisciplinary sciences, computational life sciences · 2024Article
- Antiprotozoal peptide prediction using machine learning with effective feature selection techniques.Heliyon · 2024Article
- Innovative Alignment-Based Method for Antiviral Peptide Prediction.Antibiotics (Basel, Switzerland) · 2024Article
- Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides.Molecular diversity · 2024Review
- AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features.Scientific reports · 2024Article
- FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization.Briefings in bioinformatics · 2024Article
- Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide.Current medicinal chemistry · 2024Review
- Multi-label classification and features investigation of antimicrobial peptides with various functional classes.iScience · 2023Article
- Analysis, Modeling, and Target-Specific Predictions of Linear Peptides Inhibiting Virus Entry.ACS omega · 2023Article
- Physicochemical and sequence determinants of antiviral peptides.Biologia futura · 2023Article
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the current worldwide outbreaks of the SARS-CoV-2, it is urgently needed to develop effective therapeutic agents for inhibiting the pathogens or treating the related diseases. Antimicrobial peptides (AMP) with functional activity against coronavirus could be a considerable solution, yet there is no research for identifying anti-coronavirus (anti-CoV) peptides with the computational approach. In this study, we first investigated the physiochemical and compositional properties of the collected anti-CoV peptides by comparing against three other negative sets: antivirus peptides without anti-CoV function (antivirus), regular AMP without antivirus functions (non-AVP) and peptides without antimicrobial functions (non-AMP). Then, we established classifiers for identifying anti-CoV peptides between different negative sets based on random forest. Imbalanced learning strategies were adopted due to the severe class-imbalance within the datasets. The geometric mean of the sensitivity and specificity (GMean) under the identification from antivirus, non-AVP and non-AMP reaches 83.07%, 85.51% and 98.82%, respectively. Then, to pursue identifying anti-CoV peptides from broad-spectrum peptides, we designed a double-stages classifier based on the collected datasets. In the first stage, the classifier characterizes AMPs from regular peptides. It achieves an area under the receiver operating curve (AUCROC) value of 97.31%. The second stage is to identify the anti-CoV peptides between the combined negatives of other AMPs. Here, the GMean of evaluation on the independent test set is 79.42%. The proposed approach is considered as an applicable scheme for assisting the development of novel anti-CoV peptides. The datasets and source codes used in this study are available at https://github.com/poncey/PreAntiCoV.
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