CAMP

CAMP provides a curated database of antimicrobial peptide (AMP) sequences and predictive models to support analysis of sequence–activity relationships and discovery of therapeutic AMPs.

Key Features:

• Comprehensive dataset: Contains 3,782 antimicrobial sequences divided into 2,766 experimentally validated sequences (including patented and non‑patented AMPs) and 1,016 predicted sequences.
• Detailed annotations: Entries include source organism, activity data such as Minimum Inhibitory Concentration (MIC) values, reference literature, and information on target and non‑target organisms.
• Integrated prediction tools: Predictive models use machine learning algorithms with reported accuracies of Random Forests (RF) 93.2%, Support Vector Machines (SVM) 91.5%, and Discriminant Analysis (DA) 87.5%.
• Sequence analysis tools: Includes sequence similarity search functionality such as BLAST for comparative analyses.

Scientific Applications:

• Structure–activity analysis: Investigating the structural determinants of AMP activity and specificity.
• Rational design: Supporting rational design and optimization of new antimicrobial peptides for therapeutic use.
• Comparative efficacy studies: Enabling comparative studies on AMP efficacy against different pathogens.
• Cancer research: Facilitating exploration of AMPs' potential applications in cancer therapy.

Methodology:

Data are manually curated and sequences are divided into experimentally validated and predicted sets; predictive models integrate machine learning algorithms including Random Forests, SVM, and Discriminant Analysis to forecast AMP activity.