Protein and peptide engineering for chemical exchange saturation transfer imaging in the age of synthetic biology

Lanthanide-Based Probes for Imaging Detection of Enzyme Activities by NIR Luminescence, T1- and ParaCEST MRI

Applying a single molecular probe to monitor enzymatic activities in multiple, complementary imaging modalities is highly desirable to ascertain detection and to avoid the complexity associated with the use of agents of different chemical entities. We demonstrate here the versatility of lanthanide (Ln3+) complexes with respect to their optical and magnetic properties and their potential for enzymatic detection in NIR luminescence, CEST and T1 MR imaging, controlled by the nature of the Ln3+ ion, while using a unique chelator. Based on X-ray structural, photophysical, and solution NMR investigations of a family of Ln3+ DO3A-pyridine model complexes, we could rationalize the luminescence (Eu3+, Yb3+), CEST (Yb3+) and relaxation (Gd3+) properties and their variations between carbamate and amine derivatives. This allowed the design ofL n L G a l 5 ${{{\bf L n L}}_{{\bf G a l}}^{5}}$ probes which undergo enzyme-mediated changes detectable in NIR luminescence, CEST and T1-weighted MRI, respectively governed by variations in their absorption energy, in their exchanging proton pool and in their size, thus relaxation efficacy. We demonstrate that these properties can be exploited for the visualization of β-galactosidase activity in phantom samples by different imaging modalities: NIR optical imaging, CEST and T1-weighted MRI.

Computational peptide discovery with a genetic programming approach

The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POETRegex , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

Development of a synthetic biosensor for chemical exchange MRI utilizing in silico optimized peptides

Chemical exchange saturation transfer (CEST) 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, was designed to be 198 amino acids. SuperCESTide was expressed in E. coli and purified with size exclusion chromatography. The magnetic transfer ratio asymmetry generated by superCESTide was comparable to levels seen in previous CEST reporters, such as protamine sulfate (salmon protamine) and human protamine. These data show that novel peptides with sequences optimized in silico for CEST contrast that utilize a more comprehensive range of amino acids can still produce contrast when assembled into protein units expressed in complex living environments.

The relayed nuclear Overhauser effect in magnetization transfer and chemical exchange saturation transfer MRI

Magnetic resonance (MR) is a powerful technique for noninvasively probing molecular species in vivo but suffers from low signal sensitivity. Saturation transfer (ST) MRI approaches, including chemical exchange saturation transfer (CEST) and conventional magnetization transfer contrast (MTC), allow imaging of low-concentration molecular components with enhanced sensitivity using indirect detection via the abundant water proton pool. Several recent studies have shown the utility of chemical exchange relayed nuclear Overhauser effect (rNOE) contrast originating from nonexchangeable carbon-bound protons in mobile macromolecules in solution. In this review, we describe the mechanisms leading to the occurrence of rNOE-based signals in the water saturation spectrum (Z-spectrum), including those from mobile and immobile molecular sources and from molecular binding. While it is becoming clear that MTC is mainly an rNOE-based signal, we continue to use the classical MTC nomenclature to separate it from the rNOE signals of mobile macromolecules, which we will refer to as rNOEs. Some emerging applications of the use of rNOEs for probing macromolecular solution components such as proteins and carbohydrates in vivo or studying the binding of small substrates are discussed.

A Genetic Programming Approach to Engineering MRI Reporter Genes

Here we develop a mechanism of protein optimization using a computational approach known as "genetic programming". We developed an algorithm called Protein Optimization Engineering Tool (POET). Starting from a small library of literature values, the use of this tool allowed us to develop proteins that produce four times more MRI contrast than what was previously state-of-the-art. Interestingly, many of the peptides produced using POET were dramatically different with respect to their sequence and chemical environment than existing CEST producing peptides, and challenge prior understandings of how those peptides function. While existing algorithms for protein engineering rely on divergent evolution, POET relies on convergent evolution and consequently allows discovery of peptides with completely different sequences that perform the same function with as good or even better efficiency. Thus, this novel approach can be expanded beyond developing imaging agents and can be used widely in protein engineering.

Development of a Synthetic Biosensor for Chemical Exchange MRI Utilizing In Silico Optimized Peptides

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.

Preparation of Hydrothermal Carbon Quantum Dots as a Contrast Amplifying Technique for the diaCEST MRI Contrast Agents

The discovery of exogenous contrast agents (CAs) is one of the key factors behind the success and widespread acceptability of MRI as an imaging tool. To the long list of CAs, the newest addition is the chemical exchange saturation transfer (CEST)-based CAs. Among them, the diaCEST CAs are the safer metal-free option constituted by a large pool of organic and macromolecules, but the tradeoff comes in terms of smaller natural offset. Another major challenge for the CEST CAs is that they need to operate in the tens of millimolar concentration range to produce any meaningful contrast. The quest for high efficiency diaCEST agents has led to a number of strategies such as use of hydrogen bonding, use of equivalent protons, and use of diatropic ring current. Here, we present carbon quantum dot formation using hydrothermal treatment as a new strategy to amplify diaCEST contrast efficiency. We show that while the well-known analgesic drug lidocaine hydrochloride when repurposed as a diaCEST CA produces no contrast at the physiological pH and temperature, the carbon dots prepared from it elevate the physiological contrast to a sizable 11%. Also, the maximum efficiency at an acidic pH gets amplified by a factor of 2 to 46%. The study showed that the enhancement in CEST efficiency is reproducible and the pH response of these carbon dots is tunable through variation in synthesis conditions such as temperature, duration, and precursor concentration.

Using genetic programming to predict and optimize protein function

Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and especially machine learning approaches have been recruited to assist Directed Evolution, showing promising results. In this article, we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality. As a proof-of-concept, we use peptides that generate MRI contrast detected by the Chemical Exchange Saturation Transfer contrast mechanism. The evolutionary methods used in POET are described, and the performance of POET in different epochs of our experiments with Chemical Exchange Saturation Transfer contrast are studied. Our results indicate that a computational modeling tool like POET can help to find peptides with 400% better functionality than used before.