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The number of isolated drug-resistant pathogenic microbes has increased drastically over the past decades, demonstrating an urgent need for new therapeutic interventions. Antimicrobial peptides have for a long time been looked upon as an interesting template for drug optimization. However, the process of optimizing peptide antimicrobial activity and specificity, using large peptide libraries is both tedious and expensive. Here, we describe the construction of a mathematical model for prediction, prior to synthesis, of peptide antibacterial activity toward Pseudomonas aeruginosa. By use of novel descriptors quantifying the contact energy between neighboring amino acids in addition to a set of inductive and conventional quantitative structure-activity relationship descriptors, we are able to model the peptides antibacterial activity. Cross-correlation and optimization of the implemented descriptor values have enabled us to build a model (Bac2a- #2) that was able to correctly predict the activity of 84% of the tested peptides, within a twofold deviation window of the corresponding IC50 values, measured earlier. The predictive power, is an average of 10 submodels, each predicting the activity of 20 randomly excluded peptides, with a predictive success of 16.7 +/- 1.6 peptides. The model has also been proven significantly more accurate than a simpler model (Bac2a- #1), where the inductive and conventional quantitative structure-activity relationship descriptors were excluded.

The readily available N-Boc-protected delta-amino alpha,beta-unsaturated gamma-keto ester 1 was diastereoselectively reduced to the corresponding alcohols 2 and 3, using boron- and aluminum-based reducing reagents. Reduction reactions were successful and resulted in anti/syn ratios of alcohols of >95:5 (80% yield), using LiAlH(O-t-Bu)(3) in EtOH at -78 degrees C under chelation control, and 5:95 (98% yield), using NB-Enantride in THF at -78 degrees C under Felkin-Anh control.

A peptide L5 (PAWRKAFRWAWRMLKKAA), derived from the N-terminal alpha-helical region of bovine lactoferrin (LFB 14-31), that is highly active against several tumour cell lines was reported earlier. In this study, a number of L5 analogues were designed in order to investigate how subsequent replacements of the aromatic amino acids in L5 with three amino acids representing different structural parameters influenced antitumour activity and tumour cell specificity relative to normal human cells. The Trp residues were substituted by Lys, Ile or Ala, while the Phe residue was substituted with Ala. The resulting peptides were investigated for their activity against prokaryotic cells, four tumour cell lines, human lung fibroblasts and human erythrocytes. Most of the peptides were highly active against both E. coli and S. aureus. The peptides were more active against the tumour cell lines than against normal eukaryotic cells but the activity against normal fibroblasts varied more among the peptides than did their antitumour activities. The results revealed that aromatic residues located opposite the cationic sector in L5 were more critical for antitumour activity than were aromatic residues located adjacent to the cationic sector. The biological responses for the peptides against tumour cell lines, fibroblasts, S. aureus (but not E. coli), were highly correlated with the amino acid descriptors used in our QSAR model. The result obtained from the QSAR study identified specific structural features that were important for lytic activity and membrane specificity. Certain structural properties in positions 3, 9 and 11 were shown to be important for antitumour activity, while additional structural properties in position 7 were found to be important with respect to tumour cell specificity. This information may offer a possibility for de novo design of an antitumour peptide with an improved therapeutic index.