ArticleInternational journal of molecular sciences2020
Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
33 citing papers in PubMed, 75 citations in OpenAlex.
- Review
- Nutritional Intervention with Antimicrobial Peptides Improves Growth Performance, Muscle Quality, Antioxidant Capacity, and Immune Function of Crucian Carp (Animals : an open access journal from MDPI · 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
- Antimicrobial Peptide Databases as the Guiding Resource in New Antimicrobial Agent Identification via Computational Methods.Molecules (Basel, Switzerland) · 2025Article
- Effects of Antimicrobial Peptides on Antioxidant Properties, Non-specific Immune Response and Gut Microbes of Tsinling Lenok Trout (Brachymystax lenok tsinlingensis).Biochemical genetics · 2025Article
- Bacterial diseases in Siniperca chuatsi: status and therapeutic strategies.Veterinary research communications · 2024Review
- Expression and Immune Response Profiles in Nile Tilapia (International journal of molecular sciences · 2024Article
- Identification of Multifunctional Putative Bioactive Peptides in the Insect Model Red Palm Weevil (Biomolecules · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Heterologous Production of Antimicrobial Peptides: Notes to Consider.The protein journal · 2024Article
- Peptidome Profiling ofAntibiotics (Basel, Switzerland) · 2024Article
- Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models.International journal of molecular sciences · 2023Article
- In Silico Prospecting for Novel Bioactive Peptides from Seafoods: A Case Study on Pacific Oyster (Molecules (Basel, Switzerland) · 2023Article
- Geometric deep learning as a potential tool for antimicrobial peptide prediction.Frontiers in bioinformatics · 2023Review
- Antibacterial and Anti-Inflammatory Properties of Peptide KN-17.Microorganisms · 2022Article
- Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning.Antibiotics (Basel, Switzerland) · 2022Review
- Review
- Metal-Ion Interactions with Dodecapeptide Fragments of Human Cationic Antimicrobial Protein LL-37 [hCAP(134-170)].The journal of physical chemistry. B · 2022Article
- Peptide therapeutics in the management of metastatic cancers.RSC advances · 2022Review
- A review on antimicrobial peptides databases and the computational tools.Database : the journal of biological databases and curation · 2022Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Because of the rapid development of multidrug resistance, conventional antibiotics cannot kill pathogenic bacteria efficiently. New antibiotic treatments such as antimicrobial peptides (AMPs) can provide a possible solution to the antibiotic-resistance crisis. However, the identification of AMPs using experimental methods is expensive and time-consuming. Meanwhile, few studies use amino acid compositions (AACs) and physicochemical properties with different sequence lengths against different organisms to predict AMPs. Therefore, the major purpose of this study is to identify AMPs on seven categories of organisms, including amphibians, humans, fish, insects, plants, bacteria, and mammals. According to the one-rule attribute evaluation, the selected features were used to construct the predictive models based on the random forest algorithm. Compared to the accuracies of iAMP-2L (a web-server for identifying AMPs and their functional types), ADAM (a database of AMP), and MLAMP (a multi-label AMP classifier), the proposed method yielded higher than 92% in predicting AMPs on each category. Additionally, the sensitivities of the proposed models in the prediction of AMPs of seven organisms were higher than that of all other tools. Furthermore, several physicochemical properties (charge, hydrophobicity, polarity, polarizability, secondary structure, normalized van der Waals volume, and solvent accessibility) of AMPs were investigated according to their sequence lengths. As a result, the proposed method is a practical means to complement the existing tools in the characterization and identification of AMPs in different organisms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.