journal article·2020-04Open access

Endosomal escape of protein nanoparticles engineered through humanized histidine-rich peptides

Hèctor López-Laguna, Rafael Cubarsi, Ugutz Unzueta, Ramón Mangues, Esther Vázquez, Antonio Villaverde

Science China Materials· Volume 63· Issue 4

Abstract

Poly-histidine peptides such as H6 (HHHHHH) are used in protein biotechnologies as purification tags, protein-assembling agents and endosomal-escape entities. The pleiotropic properties of such peptides make them appealing to design protein-based smart materials or nanoparticles for imaging or drug delivery to be produced in form of recombinant proteins. However, the clinical applicability of H6-tagged proteins is restricted by the potential immunogenicity of these segments. In this study, we have explored several humanized histidine-rich peptides in tumor-targeted modular proteins, which can specifically bind and be internalized by the target cells through the tumoral marker CXCR4. We were particularly interested in exploring how protein purification, self-assembling and endosomal escape perform in proteins containing the variant histidine-rich tags. Among the tested candidates, the peptide H5E (HEHEHEHEH) is promising as a good promoter of endosomal escape of the associated full-length protein upon endosomal internalization. The numerical modelling of cell penetration and endosomal escape of the tested proteins has revealed a negative relationship between the amount of protein internalized into target cells and the efficiency of cytoplasmic release. This fact demonstrates that the His-mediated, proton sponge-based endosomal escape saturates at moderate amounts of internalized protein, a fact that might be critical for the design of protein materials for cytosolic molecular delivery.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: RNA Interference and Gene Delivery · Advanced biosensing and bioanalysis techniques · Immunotherapy and Immune Responses

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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