Binding patterns of homo-peptides on bare magnetic nanoparticles: insights into environmental dependence

Abstract

Magnetic nanoparticles (MNP) are intensively investigated for applications in nanomedicine, catalysis and biotechnology, where their interaction with peptides and proteins plays an important role. However, the characterisation of the interaction of individual amino acids with MNP remains challenging. Here, we classify the affinity of 20 amino acid homo-hexamers to unmodified iron oxide nanoparticles using peptide arrays in a variety of conditions as a basis to identify and rationally design selectively binding peptides. The choice of buffer system is shown to strongly influence the availability of peptide binding sites on the MNP surface. We find that under certain buffer conditions peptides of different charges can bind the MNP and that the relative strength of the interactions can be modulated by changing the buffer. We further present a model for the competition between the buffer and the MNP's electrostatically binding to the adsorption sites. Thereby, we demonstrate that the charge distribution on the surface can be used to correlate the binding of positively and negatively charged peptides to the MNP. This analysis enables us to engineer the binding of MNP on peptides and contribute to better understand the bio-nano interactions, a step towards the design of affinity tags for advanced biomaterials.Magnetic nanoparticles (MNP) are widely used for the purification of nucleic acids and other biological molecules [1][2][3][4] . MNP are also employed in the immobilisation of enzymes 5,6 , for biomedical applications such as drug delivery and hyperthermia in cancer treatment and as contrast agents for magnetic resonance imaging 7,8 . Their superparamagnetic behaviour allows for their manipulation by an external magnetic field to easily accumulate MNP in a desired area 9 . In addition, MNP have also spiked interest in the field of catalytic reaction engineering 10 . For most applications, MNP have to be functionalised to allow for a selective binding of the target molecule. This is presently achieved by attaching metal-ion chelating molecules, e.g. nitrilotriacetic acid, to the MNP surface, which then bind His-tagged biomolecules 11,12 . Drawbacks of this method are the leakage of toxic metal ions and instability of the surface functionalisation 13,14 . Alternative surface modifications for protein adsorption on magnetic particles are glutathione, streptavidin, biotin or protein A, all of which lead to high costs running in the thousands of Euros per gram 15 . Use of bare superparamagnetic iron oxides thus offers decisive advantages for industrial applications mainly due to the easy, rapid and low-cost synthesis and the absence of degradable functional surface groups. We undertake here the first systematic study of peptide-MNP interactions of bare MNP to ultimately develop peptides that can be genetically engineered into proteins as tags and strongly bind to the nonfunctionalised MNP. The key to the design of high-affinity peptide tags lies in an in-depth understanding of surface-peptide recognition patterns 16 ....

Bare Iron Oxide Nanoparticles for Magnetic Harvesting of Microalgae: From Interaction Behavior to Process Realization

“…That the impact of ionic strength on separation depends on the salt selected was demonstrated by Procházková, who showed that phosphates have a strong effect and lead to a decreasing separation efficiency, whereas other anions did not [ 40 ]. We have also illustrated the high affinity of phosphate or citrate ions for the BION surface in previous work on adsorption of peptides and proteins [ 57 ], which explains the lower adsorption of other substances to the surface. Further adhesion experiments should deepen these findings.…”

“…Then, positively charged groups may meet negatively charged groups across the whole pH range, due to the complexity of the components present and the various dissociation constants (the so-called Lewis acid-base properties). Therefore, even when repulsion is expected from the zeta potential values (netto charge) of nanoparticles and algae, the patchwork model for charge distribution on the surface makes interaction possible, as recently presented by us [ 57 ]. Furthermore, the zeta potential of the particles depends strongly on the environmental conditions [ 40 , 57 ].…”

“…Therefore, even when repulsion is expected from the zeta potential values (netto charge) of nanoparticles and algae, the patchwork model for charge distribution on the surface makes interaction possible, as recently presented by us [ 57 ]. Furthermore, the zeta potential of the particles depends strongly on the environmental conditions [ 40 , 57 ]. Note that the zeta potential measurement, although a fast, easy, and widely used method for gaining surface charge information, is not completely appropriate for microalgae systems due to the complex medium present, as explained by Lim et al [ 31 ].…”

Design of Interactions Between Nanomaterials and Proteins: A Highly Affine Peptide Tag to Bare Iron Oxide Nanoparticles for Magnetic Protein Separation

“…In previous studies we were able to show, that a binding of amino acids to bare iron oxide nanoparticles (BIONs), at ambient conditions close to pH 7, is mainly through the carboxy group and that peptides based on glutamate residues demonstrate a high affinity to BIONs . Moreover, a transfer to larger, more complex structures where this sequence can be used as affinity tag is possible.…”

Iron Oxide Nanoparticles: Multiwall Carbon Nanotube Composite Materials for Batch or Chromatographic Biomolecule Separation

“…The differences of affinity and binding capacity can be explained by strong electrostatic interactions between lysine and negatively charged materials such as cCNTs compared to the interactions with amphiphilic iron oxides [ 62 ]. However, iron oxide nanoparticles are also usually negatively charged as they get complexed by phosphate ions in PBS buffer [ 63 ]. l -lysine was chosen since this amino acid is much easier to detect with the TNBSA method compared to other amino acids [ 49 ].…”