ArticleScientific reports2017
Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 199 papers.
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Who cites it
199 citing papers in PubMed.
- RK-14: A Novel Antimicrobial Peptide Targeting Multidrug-Resistant Bacteria and Catheter-Associated Biofilms.Probiotics and antimicrobial proteins · 2026Article
- Integrated Genome Mining, Bacterial Co-Culture Activation, and Peptidomic Analyses Identify Antimicrobial Peptide Candidates from South American Bacteria.Antibiotics (Basel, Switzerland) · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- Plant-associated fungi co-opt ancient antimicrobials for host manipulation.Science advances · 2026Article
- SPlant biotechnology journal · 2026Article
- The Extreme Environment Microbiome Catalog (EEMC): a global resource for microbial diversity and antimicrobial discovery.Nature communications · 2026Article
- CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters.Nature communications · 2026Article
- AMP-CapsNet: a multi-view feature fusion approach for antimicrobial peptide prediction using capsule networks.Genomics & informatics · 2026Article
- ANIA: an inception-attention network for predicting minimum inhibitory concentration of antimicrobial peptides.Briefings in bioinformatics · 2026Article
- Human histone fragments display antibacterial properties againstFrontiers in immunology · 2026Article
- Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation.Frontiers in molecular biosciences · 2026Review
- The impact of biologically relevant negative samples on machine learning-based B-cell epitope prediction for Influenza A.Bioinformatics advances · 2026Article
- Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures.Antibiotics (Basel, Switzerland) · 2026Article
- In Silico Identification of Antiviral Peptides as Potential Leads Against Sudan Ebolavirus VP-40.BioMed research international · 2026Article
- A Novel Type I Crustin Isoform from Scylla olivacea and its Antibacterial Potential: Membrane Depolarization, Disruption, and Induction of Reactive Oxygen Species as Modes of Action.Probiotics and antimicrobial proteins · 2025Article
- Tetrapeptide from microbial coculture exhibiting a unique algicidal mechanism against Alexandrium fundyense.Scientific reports · 2025Article
- Antimicrobial peptide prediction based on contrastive learning and gated convolutional neural network.Scientific reports · 2025Article
- A generative artificial intelligence approach for the discovery of antimicrobial peptides against multidrug-resistant bacteria.Nature microbiology · 2025Article
- BBATProt: a framework predicting biological function with enhanced feature extraction via interpretable deep learning.Briefings in bioinformatics · 2025Article
- Targeted Modification of the Antimicrobial Peptide DGL13K Reveals a Naturally Optimized Sequence for Topical Applications.Microorganisms · 2025Article
139 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server iAMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/. The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs.
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