ArticlePloS one2015
Analysis and prediction of the critical regions of antimicrobial peptides based on conditional random fields.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 39 citations in OpenAlex.
- AMPActiPred: A three-stage framework for predicting antibacterial peptides and activity levels with deep forest.Protein science : a publication of the Protein Society · 2024Article
- Peptidome Profiling ofAntibiotics (Basel, Switzerland) · 2024Article
- AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria.Antibiotics (Basel, Switzerland) · 2024Article
- Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning.Antibiotics (Basel, Switzerland) · 2022Review
- Identification and Functional Characterization of Peptides With Antimicrobial Activity From the Syphilis Spirochete,Frontiers in microbiology · 2022Article
- Antimicrobial Peptides: An Update on Classifications and Databases.International journal of molecular sciences · 2021Review
- Peptide profiling in cow urine reveals molecular signature of physiology-driven pathways and in-silico predicted bioactive properties.Scientific reports · 2021Article
- Article
- Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms.International journal of molecular sciences · 2020Article
- Article
- De Novo Design and In Vitro Testing of Antimicrobial Peptides against Gram-Negative Bacteria.Pharmaceuticals (Basel, Switzerland) · 2019Article
- dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data.Nucleic acids research · 2019Article
- Encodings and models for antimicrobial peptide classification for multi-resistant pathogens.BioData mining · 2019Review
- Large-Scale Analysis of Antimicrobial Activities in Relation to Amphipathicity and Charge Reveals Novel Characterization of Antimicrobial Peptides.Molecules (Basel, Switzerland) · 2017Article
- Antimicrobial Protein Candidates from the Thermophilic Geobacillus sp. Strain ZGt-1: Production, Proteomics, and Bioinformatics Analysis.International journal of molecular sciences · 2016Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew's correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion.
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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.