A learning algorithm to discover soluble vesicle-binding helical peptides.
Membrane peptide folding studies require peptides that bind to lipid vesicles while remaining water-soluble. Currently available peptides are either artificial designs, or they have membrane-disrupting antimicrobial or venomous activity. As a first step to derive new soluble membrane-binding peptides from naturally occurring membrane proteins, we trained a learning algorithm on several water-soluble and insoluble helical peptides by comparing its predictions with experimental solubility and fluorescence vesicle binding assays. The algorithm yielded an easily computed score S to discover soluble peptides in databases of transmembrane helical proteins. To validate the algorithm, we selected four helices based on a good S score. Experiments showed that all four are soluble at >25 microM, and that three bind to vesicles. We illustrate with an example that the vesicle binding of such peptides can be temperature-tuned. Finally, we predict four additional peptides that should be water-soluble and able to bind to lipid vesicles.
Denos
Sharlene
S
Center for Biophysics and Computational Biology, University of Illinois, Urbana-Champaign, 600 South Mathews Avenue, Urbana, Illinois 61801, USA.
Gotkowski
Eric
E
Gruebele
Martin
M
D000465:Algorithms; D002942:Circular Dichroism; D006801:Humans; D008051:Lipid Bilayers / Q000737:chemistry* / Q000378:metabolism*; D008563:Membrane Lipids / Q000737:chemistry* / Q000378:metabolism*; D008565:Membrane Proteins / Q000737:chemistry / Q000378:metabolism; D008566:Membranes / Q000737:chemistry / Q000378:metabolism; D010446:Peptide Fragments / Q000737:chemistry* / Q000378:metabolism*
D008051:Lipid Bilayers; D008563:Membrane Lipids; D008565:Membrane Proteins; D010446:Peptide Fragments