Biomedical Engineering Reference
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iDIOL. As a consequence, we began systematically evaluating the Keq values of
DBA:diol and DBA:glucose candidates using their ARS profiles, over a range of
diol/glucose concentrations. Although it could be viewed as necessary to screen
every potential candidate DBA:diol combination to determine their response to
glucose, even a limited set of boronic acids (e.g.
n
50) incorporated into a series
of dendrimer generations as DBA constructs (e.g.
n
¼
¼
5) and evaluated against
iDIOL candidates (e.g.,
n
50) gives a formidable number (e.g.
n
¼
¼
50
5
50
12,500) of possible combinations. In order to overcome the technical and
resource challenges of such a laborious screening process, we built a binding
affinity model and database based on a three-component DBA:glucose:diol inter-
action model [
73
]. Establishing a foundation based on an affinity model database
was critical to furthering our efforts toward designing a system whose function
relies on the affinities of the sensing system components. These derived Keq values
were used to identify lead DBA and iDIOL candidates. By comparing Keq values,
we were able to estimate how sensitively each DBA would respond to glucose and
identify components that would best fit a sensing system designed to detect glucose
over the physiological range. Not only did this approach significantly limit the
number of DBA:diol candidate combinations that would need to be screened, but it
also quantified and allowed us to directly compare binding between each DBA:
glucose and DBA:diol pair.
Experimental Keq values of DBA:diol and DBA:glucose combinations were
generated utilizing the three-component competitive assay developed by
Springsteen and Wang [
73
]. Using ARS as the fluorescent reporter, the association
constant between each respective DBA:glucose and DBA:diol pair was determined.
Within this system there are two competing equilibria, the first between the
candidate DBA and the ARS reporter and the second between the candidate DBA
and glucose or saccharide mimic diol. Fluorescence intensity changes, as they relate
to the formation and perturbation of each equilibria, were used to calculate the Keq
of glucose and the diol relative to the DBA [
73
]. These data were ranked according
to the magnitude of the Keq (Fig.
8
) to facilitate selection of DBA(s) for use as
competition signaling components and diol(s) for immobilization as iDIOL binding
environments.
Keq values of each DBA:diol and DBA:glucose combination were used to
generate a scatter plot of the interaction data (Fig.
8
), which illustrates the wide
range of relative affinities encompassed in our DBA and saccharide mimic libraries.
Based on the location of a representative data point on the interaction graph, the
relative affinity of glucose versus each diol for that DBA can be easily compared.
For example, if a data interaction point is located along the 1:1 line, as depicted in
Fig.
8
, this indicates that the relative binding affinity of the candidate DBA for
glucose is similar to the binding affinity of the same DBA for the diol of the DBA:
diol pair. Additionally, if a data interaction point is located along the 2:1 line, the
binding strength of the candidate DBA for glucose is approximately twice the
binding strength of the same DBA for the diol. This may signify that a data
interaction point on the 2:1 line represents a DBA:diol that is more sensitive to
glucose than a DBA:diol pair on the 1:1 line. Depending on how the binding affinity
¼
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