Prediction of general antibacterial activity is a tool for predicting the antimicrobial
potential of only linear peptides active against some bacterial strain.
It is based on the machine learning algorithm and uses the Moon and Fleming
scale (Moon C. P., Fleming K. G., Proc. Natl. Acad. Sci. U. S. A. 2011, 108 (25), 10174-10177),
and the following physicochemical characteristics of peptides: Hydrophobic moment, Charge density
and depth-dependent potential (for details, see Vishnepolsky B. and Pirtskhalava
M. Prediction of Linear Cationic Antimicrobial Peptides Based on Characteristics
Responsible for Their Interaction with the Membranes J. Chem. Inf. Model. 2014, 54, 1512−1523., PubMed).
The peptide should consist of 20 canonical amino acids, and its length should not exceed 100 amino
acids.
Paste sequence(s) in FASTA format (the peptide sequence can contain the '+' sign to the end in case of C-terminal amidation):