engleski

Design principles for peptide antibiotics

Huge increase of antibiotic usage in the average human lifespan, the ever-increasing prevalence of antibiotic resistance and almost empty antibiotic pipelines of the pharmaceutical industry, created a great need for new antibiotics. Antimicrobial peptides (AMP) are evolutionary ancient weapons. Every single multicellular organism examined up to day posses AMPs which they use to fend off bacteria, fungi, protozoa and even viruses. AMPs target “ microbial Achilles heel” , a design features of microbial membranes that are specific for prokaryotic cells. To design novel AMP antibiotics we must learn to appreciate the design principles that natural AMPs can teach us. In this work we use sequence attributes such as peptide preference to enter membrane, its maximal hydrophobic moment and amino acid frequencies to estimate peptide therapeutic index TI. In an attempt to beat the nature and to create even more efficient antibiotics we constructed the data base of 37 non-homologous (less than 70% pair wise identity) AMPs with known therapeutic index. That data set was used for defining sequence moments, a tool which can distinguish excellent from average peptide antibiotics (the cosine of the angle between two moment vectors leads to the D1 one-parameter linear model for the TI prediction). We also constructed the first version of the point mutator – an algorithm which predicts the best point mutation for a significant increase of the therapeutic index The present work is only the first step in attempts to discover sequence features which can distinguish peptide antibiotics from peptide toxins. Even so, it opens doors to proteome scanning or to peptide design with a goal to find novel peptide antibiotics different from all known antibiotics. Much additional work remains to be done in the field of molecular modeling in order to correlate peptide therapeutic potential with its propensity to form parallel or antiparallel dimers in membrane environment.

antimicrobial peptides ; peptide antibiotics ; therapeutic index ; selectivity prediction

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