The first 17 amino acid residues of Huntingtin protein (Nt17 of htt) are thought to play an important role in the protein's function; Nt17 is one of two membrane binding domains in htt. In this study the binding ability of Nt17 peptide with vesicles comprised of two subclasses of phospholipids is studied using electrospray ionization -mass spectrometry (ESI-MS) and molecular dynamics (MD) simulations. Overall, the peptide is shown to have a greater propensity to interact with vesicles of phosphatidylcholine (PC) rather than phosphatidylethanolamine (PE) lipids. Mass spectra show an increase in lipid-bound peptide adducts where the ordering of the number of such specie is 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) > 1-palmitoyl-2-oleoylglycero-3-phosphocholine (POPC) > 1-palmitoyl-2-oleoyl-sn-glycero-3 phosphoethanolamine (POPE). MD simulations suggest that the compactness of the bilayer plays a role in governing peptide interactions. The peptide shows greater disruption of the DOPC bilayer order at the surface and interacts with the hydrophobic tails of lipid molecules via hydrophobic residues. Conversely, the POPE vesicle remains ordered and lipids display transient interactions with the peptide through the formation of hydrogen bonds with hydrophilic residues. The POPC system displays intermediate behavior with regard to the degree of peptide-membrane interaction. Finally, the simulations suggest a helix stabilizing effect resulting from the interactions between hydrophobic residues and the lipid tails of the DOPC bilayer. K E Y W O R D S peptide interactions, native MS, molecular dynamics simulations, Huntingtin, Protein aggregation

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This paper presents a Laplacian-based image filtering method. Using a local noise estimator function in an energy functional minimizing scheme we show that Laplacian that has been known as an edge detection function can be used for noise removal applications. The algorithm can be implemented on a 3x3 window and easily tuned by number of iterations. Image denoising is simplified to the reduction of the pixels value with their related Laplacian value weighted by local noise estimator. The only parameter which controls smoothness is the number of iterations. Noise reduction quality of the introduced method is evaluated and compared with some classic algorithms like Wiener and Total Variation based filters for Gaussian noise. And also the method compared with the state-of-the-art method BM3D for some images. The algorithm appears to be easy, fast and comparable with many classic denoising algorithms for Gaussian noise.