CAMP: a useful resource for research on antimicrobial peptides

By S. Thomas, Shreyas D. Karnik, Ram Shankar Barai, V. Jayaraman, Susan Idicula-Thomas. Nucleic Acids Res., 2009

437 citations recorded by the source corpus.

Antimicrobial peptides (AMPs) are gaining popularity as better substitute to antibiotics. These peptides are shown to be active against several bacteria, fungi, viruses, protozoa and cancerous cells. Understanding the role of primary structure of AMPs in their specificity and activity is essential for their rational design as drugs. Collection of Anti-Microbial Peptides (CAMP) is a free online database that has been developed for advancement of the present understanding on antimicrobial peptides. It is manually curated and currently holds 3782 antimicrobial sequences. These sequences are divided into experimentally validated (patents and non-patents: 2766) and predicted (1016) datasets based on their reference literature. Information like source organism, activity (MIC values), reference literature, target and non-target organisms of AMPs are captured in the database. The experimentally validated dataset has been further used to develop prediction tools for AMPs based on the machine learning algorithms like Random Forests (RF), Support Vector Machines (SVM) and Discriminant Analysis (DA). The prediction models gave accuracies of 93.2% (RF), 91.5% (SVM) and 87.5% (DA) on the test datasets. The prediction and sequence analysis tools, including BLAST, are integrated in the database. CAMP will be a useful database for study of sequence-activity and -specificity relationships in AMPs. CAMP is freely available at http://www.bicnirrh.res.in/antimicrobial.

Subfields: Microbiology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a7ed7982dde9d7ebcb63cf8832db55edea9f2c9a

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Citations in the corpus

Antimicrobial peptides: key components of the innate immune system (2012, background): “Since then, more than 1400 AMPs have been isolated from bacteria, insects and other invertebrates, amphibians, birds, fishes, mammals, and plants (Wang and Wang, 2004; Wang et al., 2009a; Thomas et al., 2010).”
iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. (2013, methodology): “Based on the experimentally validated data in CAMP, these authors further used various machine-learning algorithms such as Random Forests (RF), Support Vector Machines (SVM) and Discriminant Analysis (DA) to identify AMPs [11].”
LAMP: A Database Linking Antimicrobial Peptides (2013, background): “There are 3,706 links to the CAMP and 1,972 links to the APD.”
Peptides: A Package for Data Mining of Antimicrobial Peptides (2015, methodology): “Additionally to these four properties there are other descriptors that used in conjunction can provide useful information in the classification and design of antimicrobial peptides (Boman, 2003; Wang et al., 2009; Thomas et al., 2010; Piotto et al., 2012).”
Strategies and molecular tools to fight antimicrobial resistance: resistome, transcriptome, and antimicrobial peptides (2013, methodology): “…sequence, definition of protein biological activity, taxonomy of the source organism, target organisms—indicating the MIC (minimum inhibitory concentration), hemolytic activity of the peptide and links to external databases such as SwissProt, PDB, PubMed and the NCBI Taxonomy (Thomas et al., 2010).”
MilkAMP: a comprehensive database of antimicrobial peptides of dairy origin (2014, background): “…of these list peptides from various sources (Antimicrobial Peptide Database (APD) (Wang et al. 2009), Collection of Antimicrobial Peptides (CAMP) (Thomas et al. 2010), DAMPD (Seshadri Sundararajan et al. 2012), YADAMP (Piotto et al. 2012), etc.) while others are dedicated to a specific category…”
Designing antimicrobial peptides: form follows function (2011, methodology): “The Antimicrobial Peptides Database (APD) 75 , and later the updated APD2 (REF.”