Short linear cationic antimicrobial peptides: screening， optimizing， and prediction.

The problem of pathogenic antibiotic-resistant bacteria such as Staphylococcus aureus and Pseudomonas aeruginosa is worsening， demonstrating the urgent need for new therapeutics that are effective against multidrug-resistant bacteria. One potential class of substances is cationic antimicrobial peptides. More than 1000 natural occurring peptides have been described so far. These peptides are short (less than 50 amino acids long)， cationic， amphiphilic， demonstrate different three-dimensional structures， and appear to have different modes of action. A new screening assay was developed to characterize and optimize short antimicrobial peptides. This assay is based on peptides synthesized on cellulose， combined with a bacterium， where a luminescence gene cassette was introduced. With help of this method tens of thousands of peptides can be screened per year. Information gained by this high-throughput screening can be used in quantitative structure-activity relationships (QSAR) analysis. QSAR analysis attempts to correlate chemical structure to measurement of biological activity using statistical methods. QSAR modeling of antimicrobial peptides to date has been based on predicting differences between peptides that are highly similar. The studies have largely addressed differences in lactoferricin and protegrin derivatives or similar de novo peptides. The mathematical models used to relate the QSAR descriptors to biological activity have been linear models such as principle component analysis or multivariate linear regression. However， with the development of high-throughput peptide synthesis and an antibacterial activity assay， the numbers of peptides and sequence diversity able to be studied have increased dramatically. Also， "inductive" QSAR descriptors have been recently developed to accurately distinguish active from inactive drug-like activity in small compounds. "Inductive" QSAR in combination with more complex mathematical modeling algorithms such as artificial neural networks (ANNs) may yield powerful new methods for in silico identification of novel antimicrobial peptides.