ArticlePloS one2012
CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Preliminary Identification and Characterization of Antimicrobial Peptides from the Skin ofBiology · 2026Article
- CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters.Nature communications · 2026Article
- Multi-feature fusion for gene prediction and functional peptide identification.Frontiers in microbiology · 2026Article
- A Comprehensive Overview of Antimicrobial Peptides: Broad-Spectrum Activity, Computational Approaches, and Applications.Antibiotics (Basel, Switzerland) · 2025Review
- AEPMA: peptide-microbe association prediction based on autoevolutionary heterogeneous graph learning.Briefings in bioinformatics · 2025Article
- BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides forJournal of chemical information and modeling · 2025Article
- Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model.Science advances · 2025Article
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- Article
- Novel technologies uncover novel 'anti'-microbial peptides inPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Machine learning tools for peptide bioactivity evaluation - Implications for cell culture media optimization and the broader cultivated meat industry.Current research in food science · 2024Article
- AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria.Frontiers in microbiology · 2024Article
- Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models.International journal of molecular sciences · 2023Article
- Antimicrobial Peptides: Challenging Journey to the Pharmaceutical, Biomedical, and Cosmeceutical Use.International journal of molecular sciences · 2023Review
- A parametric approach for molecular encodings using multilevel atomic neighborhoods applied to peptide classification.NAR genomics and bioinformatics · 2023Article
- Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning.Antibiotics (Basel, Switzerland) · 2022Review
- The acyclotide ribe 31 from Rinorea bengalensis has selective cytotoxicity and potent insecticidal properties in Drosophila.The Journal of biological chemistry · 2022Article
- Antimicrobial potential of a ponericin-like peptide isolated from Bombyx mori L. hemolymph in response to Pseudomonas aeruginosa infection.Scientific reports · 2022Article
- An Efficient Evaluation System Accelerates α-Helical Antimicrobial Peptide Discovery and Its Application to Global Human Genome Mining.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at <http://sourceforge.net/projects/csamppred/> and runs on any Linux machine.
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