The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
AMPer: a database and an automated discovery tool for antimicrobial peptides
Open accessAbstract
Motivation: Increasing antibiotics resistance in human pathogens represents a pressing public health issue worldwide for which novel antibiotic therapies based on antimicrobial peptides (AMPs) may offer one possible solution. In the current study, we utilized publicly available data on AMPs to construct hidden Markov models (HMMs) that enable recognition of individual classes of antimicrobials peptides (such as defensins, cathelicidins, cecropins, etc.) with up to 99% accuracy and can be used for discovering novel AMP candidates. Results: HMM models for both mature peptides and propeptides were constructed. A total of 146 models for mature peptides and 40 for propeptides have been developed for individual AMP classes. These were created by clustering and analyzing AMP sequences available in the public sources and by consequent iterative scanning of the Swiss-Prot database for previously unknown gene-coded AMPs. As a result, an additional 229 additional AMPs have been identified from Swiss-Prot, and all but 34 could be associated with known antimicrobial activities according to the literature. The final set of 1045 mature peptides and 253 propeptides have been organized into the open-source AMPer database. Availability: The developed HMM-based tools and AMP sequences can be accessed through the AMPer resource at http://www.cnbi2.com/cgi-bin/amp.pl Contact: cfjell@interchange.ubc.ca Supplementary information: Supplementary data are available at Bioinformatics online.
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Cites
Antimicrobial peptides: pore formers or metabolic inhibitors in bacteria?
Brogden, K. A. (2005) · Nature Reviews Microbiology · doi:10.1038/nrmicro1098 · Cited by 5,829
Cationic peptides: effectors in innate immunity and novel antimicrobials
Hancock, R. E. (2001) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(01)00092-5 · Cited by 978
Self-citationMultidimensional signatures in antimicrobial peptides
Yount, N. Y., & Yeaman, M. R. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0401567101 · Cited by 402
Lindenbaum, M., Perkins, E., Csonka, E., Fleming, E., Garcia, L., Greene, A., Gung, L., Hadlaczky, G., Lee, E., Leung, J., et al. (2004) · Nucleic Acids Research · doi:10.1093/nar/gnh169 · Cited by 137
Cited by
APD2: the updated antimicrobial peptide database and its application in peptide design.
Wang, G., Li, X., & Wang, Z. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn823 · Cited by 941
CAMP R3 : a database on sequences, structures and signatures of antimicrobial peptides: Table 1.
Waghu, F. H., Barai, R. S., Gurung, P., & Idicula-Thomas, S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1051 · Cited by 715
Mooney, C., Haslam, N. J., Pollastri, G., & Shields, D. C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045012 · Cited by 615
Deep learning improves antimicrobial peptide recognition
Veltri, D., Kamath, U., & Shehu, A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty179 · Cited by 593
Identification of antimicrobial peptides from the human gut microbiome using deep learning
Ma, Y., Guo, Z., Xia, B., Zhang, Y., Liu, X., Yu, Y., Tang, N., Tong, X., Wang, M., Ye, X., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01226-0 · Cited by 490
Metadata
- Authors
- Fjell, C. D., Hancock, R. E. W., & Cherkasov, A.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-03-06
- Volume
- 23
- Issue
- 9
- Pages
- 1148-1155
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2128495797
- Cited by
- 230
- References
- 4
- FWCI
- 8.46
- Type
- journal article
- Language
- en
- Open access
- bronze
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