CAMP: a useful resource for research on antimicrobial peptides

Abstract

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.

Cytokeratins mediate epithelial innate defense through their antimicrobial properties

“…ExPASy proteomic tools were used to predict peptide secondary structures, transmembrane helices, amphipathicity (hydrophobic moment), and net charges (18-21) for 8 variants of K6A (Table 2). Notably, none of the peptides tested showed sequence homology with any known mammalian antimicrobial peptides (22), but they all exhibited a predominance of glycine repeats and a common sequence, GGLSSVGGGS. Variants of the originally identified 17-mer fragment with an additional N-and/or C-terminal residue(s) (19-mer and two 18-mer peptides) were predicted to be favorable for antimicrobial activity based on predicted coil structure, potential for transmembrane helices, a hydrophobic face, and cationic charge.…”

“…Since glycine residues were a common feature of naturally occurring KDAMPs (Table 2), and dominance of glycine has been identified in some nonmammalian antimicrobial peptides (22,30), the contribution of 2 glycine residues to the bactericidal activity of synthetic KDAMPs was investigated. Two glycine residues (G-2 and G-8) of 10-mer peptide (50% glycine content) were substituted with alanine to interrupt consecutive glycine stretches.…”

New trends in peptide-based anti-biofilm strategies: a review of recent achievements and bioinformatic approaches

“…Some authors have used predictive data mining to assess the antimicrobial potential of new peptides. For example the AMPer method (Fjell et al 2007) recognizes individual classes of AMPs (such as defensins, cathelicidins and cecropins) and discovers novel AMP candidates based on HMMs fed on publicly available data; the BACTIBASE method (Hammami et al 2007 uses HMMs to produce bacteriocin profiles for each known family and the sequence analysis tool HMMER to provide statistical descriptions of family consensus sequences in order to support sequence-based searches on the bacterial families producing bacteriocins; the AntiBP and AntiBP2 methods (Lata et al 2007(Lata et al , 2010) predict antibacterial peptides applying ANN, QM and SVM models to the analysis of the N and C terminal residues of proteins; the CAMP method (Thomas et al 2010) uses RF, DA and SVM models to predict the antimicrobial activity of peptide sequences; the BA-GEL2 method (de Jong et al 2010) combines HMMs and simple decision rules in the prediction of bacteriocin sub-classes; the DAMPD method (Sundararajan et al 2011) predicts AMPs based on SVMs that can classify peptides into one of 27 AMP families in catalogue; and the tool of Wang et al (2011b) integrates protein BLAST (BLASTP) and a feature selection method based on mRMR and IFS models to select the optimal features for the prediction of AMPs vs non-AMPs. In addition, Juretic´et al (2009,2011) have been addressing the correlation between the physical characteristics of natural AMPs and high selectivity to generate potential peptide antibiotics not homologous to any existing natural or synthetic AMPs.…”

“…A comprehensive database on AMPs with information on their activity would facilitate the study of peptide potential, enabling and promoting sequence-specificity and sequence-activity studies (Hammami et al 2009;Thomas et al 2010). Although many AMPs are now well characterized, much information is still missing or scattered over scientific literature, ie its collection and analysis is troublesome and implies time consuming manual curation.…”

Antimicrobial peptides from the skin of the Asian frog, Odorrana jingdongensis: De novo sequencing and analysis of tandem mass spectrometry data

“…The Antimicrobial Peptide Database (APD, updated in June 2012, http://aps.unmc.edu/AP/) has collected 1992 AMPs with the validated antimicrobial activities, and the majority of them contain less than 100 amino acid residues [2]. In Collection of Anti-Microbial Peptides database (CAMP, updated on 29th April 2010, http://www.bicnirrh.res.in/antimicrobial/), there are 2867 validated AMPs and 1153 predicted AMPs with antimicrobial sequences [4]. Many AMPs have been identified from skin [5], brain [6] and stomach [7] of Anurans (frogs and toads).…”