Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model

The increasing rate in antibiotic-resistant bacterial strains has become an imperative health issue. Thus, pharmaceutical industries have focussed their efforts to find new potent, non-toxic compounds to treat bacterial infections. Antimicrobial peptides (AMPs) are promising candidates in the fight against antibiotic-resistant pathogens due to their low toxicity, broad range of activity and unspecific mechanism of action. In this context, bioinformatics' strategies can inspire the design of new peptide leads with enhanced activity. Here, we describe an artificial neural network approach, based on the AMP's physicochemical characteristics, that is able not only to identify active peptides but also to assess its antimicrobial potency. The physicochemical properties considered are directly derived from the peptide sequence and comprise a complete set of parameters that accurately describe AMPs. Most interesting, the results obtained dovetail with a model for the AMP's mechanism of action that takes into account new concepts such as peptide aggregation. Moreover, this classification system displays high accuracy and is well correlated with the experimentally reported data. All together, these results suggest that the physicochemical properties of AMPs determine its action. In addition, we conclude that sequence derived parameters are enough to characterize antimicrobial peptides

Functional Synergy between Antimicrobial Peptoids and Peptides against Gram-Negative Bacteria ▿ †

Antimicrobial peptides (AMPs) are integral components of innate immunity and are typically found in combinations in which they can synergize for broader-spectrum or more potent activity. Previously, we reported peptoid mimics of AMPs with potent and selective antimicrobial activity. Using checkerboard assays, we demonstrate that peptoids and AMPs can interact synergistically, with fractional inhibitory concentration indices as low as 0.16. These results strongly suggest that antimicrobial peptoids and peptides are functionally and mechanistically analogous

An engineered innate immune defense protects grapevines from Pierce disease

We postulated that a synergistic combination of two innate immune functions, pathogen surface recognition and lysis, in a protein chimera would lead to a robust class of engineered antimicrobial therapeutics for protection against pathogens. In support of our hypothesis, we have engineered such a chimera to protect against the Gram-negative Xylella fastidiosa (Xf), which causes diseases in multiple plants of economic importance. Here we report the design and delivery of this chimera to target the Xf subspecies fastidiosa (Xff), which causes Pierce disease in grapevines and poses a great threat to the wine-growing regions of California. One domain of this chimera is an elastase that recognizes and cleaves MopB, a conserved outer membrane protein of Xff. The second domain is a lytic peptide, cecropin B, which targets conserved lipid moieties and creates pores in the Xff outer membrane. A flexible linker joins the recognition and lysis domains, thereby ensuring correct folding of the individual domains and synergistic combination of their functions. The chimera transgene is fused with an amino-terminal signal sequence to facilitate delivery of the chimera to the plant xylem, the site of Xff colonization. We demonstrate that the protein chimera expressed in the xylem is able to directly target Xff, suppress its growth, and significantly decrease the leaf scorching and xylem clogging commonly associated with Pierce disease in grapevines. We believe that similar strategies involving protein chimeras can be developed to protect against many diseases caused by human and plant pathogens

Association studies of the copy-number variable ß-defensin cluster on 8p23.1 in adenocarcinoma and chronic pancreatitis

<p>Abstract</p> <p>Background</p> <p>Human ß-defensins are a family of antimicrobial peptides located at the mucosal surface. Both sequence multi-site variations (MSV) and copy-number variants (CNV) of the defensin-encoding genes are associated with increased risk for various diseases, including cancer and inflammatory conditions such as psoriasis and acute pancreatitis. In a case–control study, we investigated the association between MSV in <it>DEFB104</it> as well as defensin gene (DEF) cluster copy number (CN), and pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP).</p> <p>Results</p> <p>Two groups of PDAC (N=70) and CP (N=60) patients were compared to matched healthy control groups CARLA1 (N=232) and CARLA2 (N=160), respectively. Four <it>DEFB104</it> MSV were haplotyped by PCR, cloning and sequencing. DEF cluster CN was determined by multiplex ligation-dependent probe amplification.</p> <p>Neither the PDAC nor the CP cohorts show significant differences in the <it>DEFB104</it> haplotype distribution compared to the respective control groups CARLA1 and CARLA2, respectively.</p> <p>The diploid DEF cluster CN exhibit a significantly different distribution between PDAC and CARLA1 (Fisher’s exact test P=0.027), but not between CP and CARLA2 (P=0.867).</p> <p>Conclusion</p> <p>Different DEF cluster b CN distribution between PDAC patients and healthy controls indicate a potential protective effect of higher CNs against the disease.</p