5.5 Results and discussion
5.7.5 Competition biosensor-surface plasmon resonance
The same streptavidin-coated sensor chip previously described was also used for competition SPR analyses. Samples containing unlabeled hairpin DNA sequences (5’-
CGAATTGAATTCGGCTCTCCGAATTCAATTCG-3’, and 5’- CGATATGATATCGGCTCTCCGATATCATATCG-3’) were added to a constant concentration of DB2277 and flowed over the chip surface. The added DNA sequences in solution compete for binding to DB2277, which results in the decrease of RUobs. Solutions were prepared in 50 mM
Tris-HCl buffer with constant 100 nM DB2277 compound. Competing DNA concentrations ranging 0 nM – 2.5 µM were injected over the sensor chip at a flow rate of 100 µL∙min-1 until steady-state responses were reached. Experimental buffer (i.e. no DB2277) was flowed over the chip surface to dissociate bound DB2277 from DNA and competing DNA. The sensor chip surface was regenerated with 0.5 M NaCl for 30 s and rinsed with three injections of experimental buffer to produce a stable baseline for the next cycle. For chip regeneration, no detectable differences were observed in the baseline stabilization among 10 mM glycine, 1 M NaCl, and 0.5 M NaCl solutions. A lower salt concentration of 0.5 M NaCl was used for competition SPR analyses to ensure a constant RU of immobilized 5’-end labeled biotinylated DNA was maintained, meaning none of the immobilized DNA dissociated during regeneration.
To determine the solution dissociation constant (KS) of the competing DNA, a one-site
binding model was used to determine KS of the competing DNA with DB2277 in solution by the
following equation: (5.2) 𝑅𝑈𝑜𝑏𝑠 ={([𝐿]𝑇)−0.5(𝐾𝑆+[𝐿]𝑇+[𝐷]𝑇)−√(𝐾𝑆+[𝐿]𝑇+[𝐷]𝑇 2)−4([𝐿] 𝑇∙[𝐷]𝑇)}×𝑅𝑈𝑚𝑎𝑥 {([𝐿]𝑇)−0.5(𝐾𝑆+[𝐿]𝑇+[𝐷]𝑇)−√(𝐾𝑆+[𝐿]𝑇+[𝐷]𝑇2)−4([𝐿]𝑇∙[𝐷]𝑇)}+𝐾𝐷
The RUobs was plotted against total competing DNA concentration to determine dissociation
constants of the competitor DNA (KS) in solution. Equation 5.2 was derived specifically for
determined. It substitutes a conventional one-site binding model with a quadratic formula [40] which includes total concentrations of DB2277 ligand and competing DNA in solution as [L]T and
[D]T, respectively. The KD in Equation 5.2 is an averaged equilibrium dissociation constant of K1
and K2 values (i.e. K12) for DB2277 binding with AAAAGCTTTT, AATTGCAATT or
ATATGCATAT previously determined by Equations 5.1b or 5.1c. Here, [L]T, [D]T , RUmax and
KD are used as fitting parameters to determine KS using KalediaGraph. Dissociation constants for
AATTGAATT using competition SPR are compared to values obtained direct-binding SPR and are reported in Table 5.1.
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6 CONCLUSIONS
In this dissertation, recognition of the DNA minor groove by small molecules was investigated. The use of electrospray ionization mass spectrometry was the primary tool used to identify ligand-DNA interactions. In addition to the findings by electrospray ionization mass spectrometry (ESI-MS), other biophysical methods were used to validate our findings.
We first introduced the development of a novel technique using ESI-MS to observe competitive binding of well-characterized compounds with various DNA sequences. We highlighted the many advantages competition ESI-MS has to offer such as the rapid and convenient sample analysis, as well as the small amounts of sample required. More importantly, competition ESI-MS allowed easy identification of stoichiometry, cooperativity, and a direct comparison of relative binding affinities. In theory, any number of sequences and ligands can be simultaneously analyzed as long as the moleculer weights of each species and their potential complexes are discernable. With one sample, lots of information can be gathered all the while reducing reagents used and time spent cleaning between sample runs. More importantly, it is not limited to DNA and can be applied to other biomacromoleculear interactions.
Our competition ESI-MS method was next applied to investigations of mixed DNA sequences with small molecules. Many important features were discovered using systematic variations of a test compound and its target binding site. Specificity and cooperativity of the test compound against several analogues determined the parent compound was optimum for binding with the target sequence. One such analogues showed unexpected binding with both target and mixed-site reference sequences. Several mutant sequences displayed unusual binding patterns strictly based on sequence such that a simple reversal of two bases would result in complete loss
of binding of the parent compound. Overall, the consensus binding site remained the preferred sequence and further illustrated the importance of base pair sequence in minor groove recognition. In the next project, several diamidines were test for selectivity among a set mixed-site sequences. Recognition groups within the compounds, such as hydrogen bond acceptors and donors, and flexibility of the compound were strategically modified to determine the effects on minor groove binding. Competition ESI-MS, in combination with other biophysical methods, confirmed the strong and selective recognition of a diamidine for a single G-containing sequence. The information gathered provided additional information for the rational design of more specific compounds to target longer mixed-site sequences.
Finally, ESI-MS was used to identify additional binding interactions of a well- characterized compound with new mixed-site sequences. Competition ESI-MS initially provided information such as stoichiometry and cooperativity. More detailed studies using SPR showed direct correlation for binding with results obtained by ESI-MS. MD simulations unveiled a distinct pattern in the DNA microstructure for several sequences which later explained the intrinsic binding behavior of our test compound. Complementary evidence from both experimental and computional methods provided a rationale for the sequence-dependent behavior of the compound binding in the minor groove.
The common denominator in each of these projects is the application of competition ESI-