Peptides that self-assemble into nanostructures are of tremendous interest for biological, medical, photonic and nanotechnological applications. The enormous sequence space that is available from 20 amino acids probably harbours many interesting candidates, but it is currently not possible to predict supramolecular behaviour from sequence alone. Here, we demonstrate computational tools to screen for the aqueous self-assembly propensity in all of the 8,000 possible tripeptides and evaluate these by comparison with known examples. We applied filters to select for candidates that simultaneously optimize the apparently contradicting requirements of aggregation propensity and hydrophilicity, which resulted in a set of design rules for self-assembling sequences. A number of peptides were subsequently synthesized and characterized, including the first reported tripeptides that are able to form a hydrogel at neutral pH. These tools, which enable the peptide sequence space to be searched for supramolecular properties, enable minimalistic peptide nanotechnology to deliver on its promise.
We report on a simple carbohydrate amphiphile able to self-assemble into nanofibers upon enzymatic dephosphorylation. The self-assembly can be triggered by alkaline phosphatase (ALP) in solution or in situ by the ALP produced by osteosarcoma cell line, SaOs2. In the latter case, assembly and localized gelation occurs mainly on the cell surface. The gelation of the pericellular environment induces a reduction of the SaOs2 metabolic activity at an initial stage (≤7 h) that results in cell death at longer exposure periods (≥24 h). We show that this effect depends on the phosphatase concentration, and thus, it is cell-selective with prechondrocytes ATDC5 (that express ∼15-20 times lower ALP activity compared to SaOs2) not being affected at concentrations ≤1 mM. These results demonstrate that simple carbohydrate derivatives can be used in an antiosteosarcoma strategy with limited impact on the surrounding healthy cells/tissues.
Here, we use a closed-loop discovery and optimization approach for searching the peptide sequence space. Combining an evolutionary algorithm with machine learning and in vitro assay allowed for rapid development of new antimicrobial peptides.
Peptide co-assembly is of interest for the development of functional supramolecular biomaterials. Herein, computational simulations were combined with experimental validation to aid the design and understanding of cooperative co-assembly of a structure-forming tripeptide (FFD) and a functional copper-binding tripeptide (GHK) leading to hydrogel formation in response to complexation with copper ions.
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