An increasing number of reported cases of drug resistant Staphylococcus aureus and Pseudomonas aeruginosa, demonstrate the urgent need for new therapeutics that are effective against such and other multi-drug resistant bacteria. Antimicrobial peptides have for two decades now been looked upon as interesting leads for development of new therapeutics combating these drug resistant microbes.
High-throughput screening of peptide libraries have generated large amounts of information on peptide activities. However, scientists still struggle with explaining the specific peptide motifs resulting in antimicrobial activity. Consequently, the majority of peptides put into clinical trials have failed at some point, underlining the importance of a thorough peptide optimization.
An important tool in peptide design and optimization is quantitative structure-activity relationship (QSAR) analysis, correlating chemical parameters with biological activities of the peptide, using statistical methods. In this review we will discuss two different in silico strategies of computer-aided antibacterial peptide design, a linear correlation model build as an extension of traditional principal component analysis (PCA) and a non-linear artificial neural network model. Studies on structurally diverse peptides, have concluded that the PCA derived model are able to guide the antibacterial peptide design in a meaningful way, however requiring rather a high homology between the peptides in the test-set and the in silico library, to ensure a successful prediction. In contrast, the neural network model, though significantly less explored in relation to antimicrobial peptide design, has proven extremely promising, demonstrating impressive prediction success and ranking of random peptide libraries correlating well with measured activities.