Development of a Synthetic Biosensor for Chemical Exchange MRI Utilizing In Silico Optimized Peptides.
Adam J FillionAlexander R BriccoHarvey D LeeDavid KorenchanChristian T FarrarAssaf A GiladPublished in: bioRxiv : the preprint server for biology (2023)
Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) has been identified as a novel alternative to classical diagnostic imaging. Over the last several decades, many studies have been conducted to determine possible CEST agents, such as endogenously expressed compounds or proteins, that can be utilized to produce contrast with minimally invasive procedures and reduced or non-existent levels of toxicity. In recent years there has been an increased interest in the generation of genetically engineered CEST contrast agents, typically based on existing proteins with CEST contrast or modified to produce CEST contrast. We have developed an in-silico method for the evolution of peptide sequences to optimize CEST contrast and showed that these peptides could be combined to create de novo biosensors for CEST MRI. A single protein, superCESTide 2.0, was designed to be 198 amino acids. SuperCESTide 2.0 was expressed in E. coli and purified with size-exclusion chromatography. The magnetic transfer ratio asymmetry (MTR asym ) generated by superCESTide 2.0 was comparable to levels seen in previous CEST reporters, such as protamine sulfate (salmon protamine, SP), Poly-L-Lysine (PLL), and human protamine (hPRM1). This data shows that novel peptides with sequences optimized in silico for CEST contrast that utilizes a more comprehensive range of amino acids can still produce contrast when assembled into protein units expressed in complex living environments.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- amino acid
- magnetic resonance
- computed tomography
- minimally invasive
- diffusion weighted imaging
- molecular docking
- endothelial cells
- mass spectrometry
- escherichia coli
- big data
- oxidative stress
- photodynamic therapy
- protein protein
- high performance liquid chromatography
- genetic diversity
- binding protein
- liquid chromatography
- machine learning