Alexander Robert Bricco1, Illiya Miralavy1, Shaowei Bo2, Or Perlman3, Christian Farrar3, Mike McMahon2, Wolfgang Banzhaf1, and Assaf Gilad1
1Michigan State University, East Lansing, MI, United States, 2Johns Hopkins University, Baltimore, MD, United States, 3Harvard Medical School, Boston, MA, United States
Synopsis
Reporter genes for MRI allow for the non-invasive
observation and evaluation of gene and cell therapies which are being rapidly
developed. Traditional methods of gene optimization failed to improve reporter
gene quality. We developed and employed a novel machine learning program called
the Protein Optimizing Evolving Tool (POET). Using POET we have discovered
peptides which provide more contrast than prior efforts and also display
properties that challenge prior knowledge on the functions of these genes.
Introduction
Reporter genes for Magnetic Resonance Imaging (MRI) would
allow genes to be measured in a non-invasive and safe manner, paving the way
for translational use in human subjects. One of the best mechanisms for generating
such a reporter gene is Chemical Exchange Saturation Transfer (CEST), which
generates contrast without addition of outside metals and allows for the
contrast mechanism to be controlled. Previous CEST reporter genes1,2can
be further optimized via protein evolution but have proven difficult to improve
using typical protein optimization engineering methods. We hypothesized that
these limitations could be overcome via application of machine learning.
To achieve this goal, we utilized Genetic Programming (GP) which
is a field in computer science studying algorithms inspired by biological
evolution. GP is a powerful technique that evolves models to difficult tasks
and has been used in the past to in array of biological studies.
In this study we sought to develop contrast which was
generated at two different saturation frequencies. The first experiments were
used to optimize contrast at 3.6ppm downfield from water, since this is the
saturation frequency currently used in research due to the plentiful amines
present in amino acids. Later experiments were used to optimize contrast at 5.0
ppm downfield from water, since there is less background saturation at this frequency,
and it demonstrates the ability for POET to develop a novel function less well
understood in research.Methods
We have thusfar performed 10 generations of development for
peptides with a CEST peak at 3.6 ppm, and 6 generations of development for
peptides with a CEST peak at 5.0 ppm. For the 3.6 ppm peptides we started with
training data obtained from prior literature values of 12 amino acid long
peptides. For the 5 ppm peptides we used the 5ppm data gathered from the first four
generations of the 3.6 evolution.
In each generation of development, POET was used to predict
10 peptides, which were then synthesized by a vendor. We dissolved the peptides
to a concentration of 5 mg/mL in PBS and were imaged on 7T preclinical MRI at
37 C, TR 10 s, Tsat 4s, Bsat 4.7 μT with conditions as
described before3. The experimental results
would then be added into the training data and the genetic program would then
develop predictive models and predict proteins based on the new information
learnable from the increase in data. Exchange rate (Kex) was determined using QUEST4.
Each sample was scanned alongside a sample of Poly-L-Lysine
(PLL), which served as a control and standard. PLL was chosen due to its
relation to the Lysine Rich Protein (LRP) which is the current state of the art
CEST reporter gene1.Results
The first peptides developed using POET were mostly insoluble,
with only one peptide which was able to dissolve and produce contrast. The next
two Later generations showed steadily increased contrast. Later generations
level out in terms of median and maximum contrast, however, Across all
generation, 42 peptides were discovered that produce greater contrast than PLL
(figure 1). More importantly, several peptides that produce contrast that is
substantially higher than PLL were identified. When the exchange rate (Kex)
– the hallmark of a good CEST agent) was measure using the QUEST method, some
selected peptides show Kex as high as 822 Hz which is ~2 fold of PLL
(Kex=479).
Next, we tested if POET can be used to develop a new
function which is generating contrast at 5 ppm. The advantage of contrast at 5
ppm is that there is a very low background contrast from physiological molecules
in comparison to the conventional contrast at 1.8 or 3.6 ppm. The first
peptides developed for 5ppm showed greater contrast on average than the
training data. Later generations have shown less contrast than the first
generation (figure 2). Remarkably, throughout 6 generations, several peptides
were identified that can produce contrast that is up to 80% of the contrast that
PLL produces at 3.6 ppm. while this evolution experiment is still in progress,
our data point out that POET can be used to evolve new function. Discussion
This mechanism has shown the ability to discover new
peptides that have challenged the current understanding about what chemical
properties allow MRI contrast to be generated. Although the training data, and
prior research was focused on peptides that were both positively charged and
basic, POET had led to the discovery of CEST agents that are neutral in charge
and pI but produce more contrast than PLL.Conclusion
The contrast of the peptides generated by POET produce
substantially more contrast than PLL after a few generations of using the
program. Meanwhile, selected peptides developed using POET, that showed superior
contrast can be assembled into a new MRI
reporter gene improving its sensitivity over previous generations of reporters.Acknowledgements
This research was made possible by financial support from NIH/NIBIB: R01 EB031008; R01EB030565; R01EB031936; P41-EB024495References
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