The therapeutic, antibiotic potential of antimicrobial peptides can be prohibitively diminished because of the cytotoxicity and hemolytic profiles they exhibit. Quantifying and predicting antimicrobial peptide toxicity against host cells is thus an important goal of AMP related research. In this work, we present quantitative structure activity relationships for toxicity of protegrin-like antimicrobial peptides against human cells (epithelial and red blood cells) based on physicochemical properties, such as interaction energies and radius of gyration, calculated from molecular dynamics simulations of the peptides in aqueous solvent. The hypothesis is that physicochemical properties of peptides, as manifest by their structure and interactions in a solvent and as captured by atomistic simulations, are responsible for their toxicity against human cells. Protegrins are β-hairpin peptides with high activity against a wide variety of microbial species, but in their native state are toxic to human cells. Sixty peptides with experimentally determined toxicities were used to develop the models. We test the resulting relationships to determine their ability to predict the toxicity of several protegrin-like peptides. The developed QSARs provide insight into the mechanism of cytotoxic action of antimicrobial peptides. In a subsequent blind test, the QSAR correctly ranked four of five protegrin analogues newly synthesized and tested for toxicity.
Bibliographical noteFunding Information:
Benjamin Schuster was supported by a University of Minnesota Bioinformatics Summer Institute internship, NSF award EEC-0234112. This work was supported by a grant from NIH (GM 070989). Computational support from the Minnesota Supercomputing Institute (MSI) is gratefully acknowledged. This work was also partially supported by National Computational Science Alliance under MCA04T033 and utilized the marvel cluster at the Pittsburgh Supercomputing Center.
- Antimicrobial peptides
- Molecular dynamics simulation
- Quantitative structure activity relationship (QSAR)
- Toxicity prediction