Application of 'inductive' QSAR descriptors for quantification of antibacterial activity of cationic polypeptides.
On the basis of the inductive QSAR descriptors we have created a neural network-based solution enabling quantification of antibacterial activity in the series of 101 synthetic cationic polypeptides (CAMEL-s). The developed QSAR model allowed 80% correct categorical classification of antibacterial potencies of the CAMEL-s both in the training and the validation sets. The accuracy of the activity predictions demonstrates that a narrow set of 3D sensitive 'inductive' descriptors can adequately describe the aspects of intra- and intermolecular interactions that are relevant for antibacterial activity of the cationic polypeptides. The developed approach can be further expanded for the larger sets of biologically active peptides and can serve as a useful quantitative tool for rational antibiotic design and discovery.
Cherkasov
Artem
A
Division of Infectious Diseases, Faculty of Medicine, University of British Columbia, 2733 Heather Street, Vancouver, BC, V5Z 3J5, Canada. artc@interchange.ubc.ca
Jankovic
Bojana
B
D000595:Amino Acid Sequence; D000900:Anti-Bacterial Agents / Q000737:chemistry* / Q000494:pharmacology; D023181:Antimicrobial Cationic Peptides / Q000737:chemistry* / Q000494:pharmacology; D003198:Computer Simulation; D015195:Drug Design; D006090:Gram-Negative Bacteria / Q000187:drug effects*; D006094:Gram-Positive Bacteria / Q000187:drug effects*; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D008826:Microbial Sensitivity Tests; D008956:Models, Chemical; D008969:Molecular Sequence Data; D021281:Quantitative Structure-Activity Relationship; D055672:Static Electricity
D000900:Anti-Bacterial Agents; D023181:Antimicrobial Cationic Peptides