Investigating Antimicrobial Peptide-Membrane Interactions Using Fast Photochemical Oxidation of Peptides in Nanodiscs.

Antimicrobial peptides (AMPs) are an important part of the innate immune system and demonstrate promising applications in the fight against antibiotic-resistant infections due to their unique mechanism of targeting bacterial membranes. However, it is challenging to study the interactions of these peptides within lipid bilayers, making it difficult to understand their mechanisms of toxicity and selectivity. Here, we used fast photochemical oxidation of peptides, an irreversible footprinting technique that labels solvent accessible residues, and native charge detection-mass spectrometry to study AMP-lipid interactions with different lipid bilayer nanodiscs. We observed differences in the oxidation of two peptides, indolicidin and LL-37, in three distinct lipid environments, which reveal their affinity for lipid bilayers. Our findings suggest that indolicidin interacts with lipid head groups via a simple charge-driven mechanism, but LL-37 is more specific for Escherichia coli nanodiscs. These results provide complementary information on the potential modes of action and lipid selectivity of AMPs.

Native Mass Spectrometry Reveals the Simultaneous Binding of Lipids and Zinc to Rhodopsin.

Rhodopsin, a prototypical G-protein-coupled receptor, is responsible for scoptic vision at low-light levels. Although rhodopsin's photoactivation cascade is well understood, it remains unclear how lipid and zinc binding to the receptor are coupled. Using native mass spectrometry, we developed a novel data analysis strategy to deconvolve zinc and lipid bound to the proteoforms of rhodopsin and investigated the allosteric interaction between lipids and zinc binding. We discovered that phosphatidylcholine bound to rhodopsin with a greater affinity than phosphatidylserine or phosphatidylethanolamine, and that binding of all lipids was influenced by zinc but with different effects. In contrast, zinc binding was relatively unperturbed by lipids. Overall, these data reveal that lipid binding can be strongly and differentially influenced by metal ions.

The application of proteomic techniques to fungal protein identification and quantification.

The number of sequenced genomes has increased rapidly in the last few years, supporting a revolution in bioinformatics that has been leveraged by scientists seeking to analyze the proteomes of numerous biological systems. The primary technique employed for the identification of peptides and proteins from biological sources is mass spectrometry (MS). This analytical process is usually in the form of whole-protein analysis (termed "top-down" proteomics) or analysis of enzymatically produced peptides (known as the "bottom-up" approach). This article will focus primarily on the more common bottom-up proteomics to include topics such as sample preparation, separation strategies, MS instrumentation, data analysis, and techniques for protein quantification. Strategies for preparation of samples for proteomic analysis, as well as tools for protein and peptide separation will be discussed. A general description of common MS instruments along with tandem mass spectrometry (MS/MS) will be given. Different methodologies of sample ionization including matrix-assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) will be discussed. Data analysis methods including database search algorithms and tools for protein sequence analysis will be introduced. We will also discuss experimental strategies for MS protein quantification using stable isotope labeling techniques and fluorescent labeling. We will introduce several fungal proteomic studies to illustrate the use of these methods. This article will allow investigators to gain a working knowledge of proteomics along with some strengths and weaknesses associated with the techniques presented.

Verification of single-peptide protein identifications by the application of complementary database search algorithms.

Data produced from the MudPIT analysis of yeast (S. cerevisiae) and rice (O. sativa) were used to develop a technique to validate single-peptide protein identifications using complementary database search algorithms. This results in a considerable reduction of overall false-positive rates for protein identifications; the overall false discovery rates in yeast are reduced from near 25% to less than 1%, and the false discovery rate of yeast single-peptide protein identifications becomes negligible. This technique can be employed by laboratories utilizing a SEQUEST-based proteomic analysis platform, incorporating the XTandem algorithm as a complementary tool for verification of single-peptide protein identifications. We have achieved this using open-source software, including several data-manipulation software tools developed in our laboratory, which are freely available to download.