Computation meets experiment: identification of highly efficient fibrillating peptides.

Self-assembling peptides are of huge interest for biological, medical and nanotechnological applications. The enormous chemical variety that is available from the 20 amino acids offers potentially unlimited peptide sequences, but it is currently an issue to predict their supramolecular behavior in a reliable and cheap way. Herein we report a computational method to screen and forecast the aqueous self-assembly propensity of amyloidogenic pentapeptides. This method was found also as an interesting tool to predict peptide crystallinity, which may be of interest for the development of peptide based drugs.