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There has been an exponential increase in the design of synthetic antimicrobial peptides (AMPs) for its use as novel antibiotics. Synthetic AMPs are substantially enriched in residues with physicochemical properties known to be critical for antimicrobial activity; such as positive charge, hydrophobicity, and higher alpha helical propensity. The current prediction algorithms for AMPs have been developed using AMP sequences from natural sources and hence do not perform well for synthetic peptides. In this version of CAMP database, along with updating sequence information of AMPs, we have created separate prediction algorithms for natural and synthetic AMPs. CAMPR4 holds 24243 AMP sequences, 933 structures, 2143 patents and 263 AMP family signatures. In addition to the data on sequences, source organisms, target organisms, minimum inhibitory and hemolytic concentrations, CAMPR4 provides information on N and C terminal modifications and presence of unusual amino acids, as applicable. The database is integrated with tools for AMP prediction and rational design (natural and synthetic AMPs), sequence (BLAST and clustal omega), structure (VAST) and family analysis (PRATT, ScanProsite, CAMPSign). The data along with the algorithms of CAMPR4 will aid to enhance AMP research. CAMPR4 is accessible at http://camp.bicnirrh.res.in/.

Antimicrobial peptides (AMPs) are known to have family-specific sequence composition, which can be mined for discovery and design of AMPs. Here, we present CAMPR3; an update to the existing CAMP database available online at www.camp3.bicnirrh.res.in. It is a database of sequences, structures and family-specific signatures of prokaryotic and eukaryotic AMPs. Family-specific sequence signatures comprising of patterns and Hidden Markov Models were generated for 45 AMP families by analysing 1386 experimentally studied AMPs. These were further used to retrieve AMPs from online sequence databases. More than 4000 AMPs could be identified using these signatures. AMP family signatures provided in CAMPR3 can thus be used to accelerate and expand the discovery of AMPs. CAMPR3 presently holds 10247 sequences, 757 structures and 114 family-specific signatures of AMPs. Users can avail the sequence optimization algorithm for rational design of AMPs. The database integrated with tools for AMP sequence and structure analysis will be a valuable resource for family-based studies on AMPs. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Antimicrobial peptides (AMPs) are gaining importance as anti-infective agents. Here we describe the updated Collection of Antimicrobial Peptide (CAMP) database, available online at http://www.camp.bicnirrh.res.in/. The 3D structures of peptides are known to influence antimicrobial activity. Although there exists databases of AMPs, information on structures of AMPs is limited in these databases. CAMP is manually curated and currently holds 6756 sequences and 682 3D structures of AMPs. Sequence and structure analysis tools have been incorporated to enhance the usefulness of the database.

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.