Characterization Of Blood–Brain Barrier Crossing And Tumor Homing Peptides By Molecular Dynamics Simulations

Abstract

Introduction

The new frontier of tumor diagnosis and treatment relies on the development of delivery strategies capable of allowing the specific targeting of the diagnostic agents/chemotherapeutics, avoiding side effects. In the case of brain tumors, achieving this goal is made more difficult by the presence of the blood–brain barrier (BBB). Peptides have been revealed as excellent candidates for both BBB crossing and specific cancer homing. Nanoparticles (NPs), functionalized with BBB crossing and tumor homing (TH) peptides, are emerging as smart theranostic systems. However, there is still poor knowledge concerning the molecular structure and dynamical properties of these peptides, essential requirements for a suitable functionalization of the delivery systems themselves.

Methods

In this work, by means of molecular dynamics (MD) simulations, we have extensively characterized the structural and dynamical behavior of several peptides, known to be endowed of BBB crossing and TH properties.

Results

The simulations point out that, on the basis of their conformational dynamics, the peptides can be classified in two main groups: 1) peptides assuming a specific structural conformation, a feature that could be important for interacting with the molecular target but that may limit their use as functionalizing molecules and 2) highly flexible peptides whose interaction with the target may be independent of a particular structural conformation and that may represent good candidates for the functionalization of theranostic NP-based platforms.

Discussion

Such findings may be useful for the de novo designing of NP-based delivery systems.

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Keywords:

• conformational flexibility
• free energy landscapes
• essential dynamics
• peptides
• brain
• theranostic platform

Acknowledgments

This work was supported by the NANOCROSS project “Plant virus nanoparticles for blood-brain barrier crossing and medulloblastoma targeting” (IG 20314) granted by the Associazione Italiana per la Ricerca sul Cancro (AIRC) to M Mancuso. The computing resources used for this work were provided by CRESCO/ENEAGRID High Performance Computing infrastructure. The authors thank Dr. Massimo Pinto (ENEA) for his support in Python scripting and Dr. Andrea Quintiliani for English language editing and revision of the manuscript.

Disclosure

The authors report no conflicts of interest in this work