DNA Binding to Gold Nanoparticles through the Prism of Molecular

May 22, 2019 - Our data also show that under the conditions employed in this study cytosine is ... These regularities were confirmed in a series of bi...
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Article Cite This: Langmuir 2019, 35, 7916−7928

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DNA Binding to Gold Nanoparticles through the Prism of Molecular Selection: Sequence−Affinity Relation Pavel Vorobjev,†,‡,§ Anna Epanchintseva,†,§ Alexander Lomzov,†,‡,§ Aleksey Tupikin,† Marsel Kabilov,† Inna Pyshnaya,† and Dmitrii Pyshnyi*,†,‡ †

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Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, 8 Lavrentiev Avenue, Novosibirsk 630090, Russia ‡ Novosibirsk State University, 2, Pirogova Street, Novosibirsk 630090, Russia S Supporting Information *

ABSTRACT: Native DNA strongly adsorbs to citrate-coated gold nanoparticles (AuNPs). The resulting composites (DNA/AuNPs) are valuable materials in many fields, especially in biomedicine. For this reason, the process of adsorption is a focus for intensive research. In this work, DNA adsorption to gold nanoparticles was studied using a molecular selection procedure followed by high-throughput DNA sequencing. The chemically synthesized DNA library containing a central N26 randomized fragment was sieved through four cycles of adsorption to AuNPs in a tree-like selection-amplification scheme (SELEX (Selective Evolution of Ligands by EXponential enrichment)). The frequencies of occurrence of specific oligomeric DNA motifs, k-mers (k = 1−6), in the initial and selected pools were calculated. Distribution of secondary structures in the pools was analyzed. A large set of diverse A, T, and G enriched k-mers undergo a pronounced positive selection, and these sequences demonstrate faster and strong binding to the AuNPs. For facile binding, such structural motifs should be located in the loop regions of weak intramolecular complexeshairpins with imperfect stem, or other portion of the structure, which is unpaired under selection conditions. Our data also show that, under the conditions employed in this study, cytosine is significantly depleted during the selection process, although guanine remains unchanged. These regularities were confirmed in a series of binding experiments with a set of synthetic DNA oligonucleotides. The detailed analysis of DNA binding to AuNPs shows that the sequence specificity of this interaction is low due to its nature, although the presence and the number of specific structural motifs in DNA affect both the rate of formation and the strength of the formed noncovalent associates with AuNPs.

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INTRODUCTION DNA-coated gold nanoparticles (AuNPs) represent an excellent material for a variety of biomedical applications. AuNPs are widely used to create bioanalytical and diagnostic as well as therapeutic and theranostic tools.1−4 DNA can be attached to AuNPs either covalently or noncovalently.5,6 Covalent attachment is very effective and easily controllable but involves costly chemical manipulations with DNA. Otherwise, the efficiency of noncovalent binding can also be very high, and the resulting DNA/AuNPs or RNA/AuNPs complexes are stable enough during storage and handling.7−11 Therefore, it is not surprising that the adsorption of native DNA on the AuNPs surface attracts growing attention. The structure and surface of nanoparticles have been well studied by spectroscopic methods and in silico modeling.12−14 Widely used gold crystals are rigid and strictly periodic, with typically two types of crystallographic faces: (111) and (100).15,16 While the regularity of single-stranded DNA (ssDNA) differs significantly, it is restricted by the molecular structure of the sugar−phosphate backbone and the base stacking interactions. Four nucleobases are irregularly posi© 2019 American Chemical Society

tioned throughout the DNA strand, although the points of attachment for all bases along the chain are positioned regularly. When studying the adsorption of DNA onto gold nanoparticles, one should expect an interaction of one regular and one quasi-regular structure. Nucleobases are considered as the points of the direct interaction of DNA with the AuNP surface. Because of the significant difference in the structure between the nucleobases, they interact with AuNPs differently, and DNA adsorption onto AuNPs is influenced by its nucleotide composition. The study of the thermal desorption of nucleobases from gold films revealed a decrease in desorption temperature in the following order: G > A > C > T.17 The observed order is in good agreement with the results obtained by direct measurements of oligonucleotides’ binding strength to the aqueous Au(111) interface: G ≈ A > T ≈ C.12 However, various other orders of affinities are also published. For instance, the analysis with Fourier transform infrared Received: March 5, 2019 Revised: April 21, 2019 Published: May 22, 2019 7916

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Langmuir spectroscopy revealed the following order: A > C ≥ G > T.18 In a surface plasmon resonance study based on the nucleosidedriven agglomeration of AuNPs, the order was G ≈ C > A > T, while the DNA hybridization-based technique resulted in the A > C > G > T order of adsorption strength.19,20 Among a large variety of sizes, shapes, and surface chemistries, 15 nm spherical citrate-coated AuNPs represent a well-balanced proportion of the ease in synthesis, high stability, and lability of coating. They can bear higher useful loads than 5−10 nm particles; the stability of 30−50 nm AuNPs is significantly lower. Citrate coating provides significant stability to the particle but is labile enough to allow the facile adsorption of various biomolecules to AuNPs.1,7,21 The interaction of DNA with gold and especially with gold nanoparticles was studied in various media. All these efforts resulted in a clear understanding that ssDNA, not doublestranded DNA (dsDNA), binds very strongly to AuNPs in a broad range of conditions.21 In acidic conditions or at high ionic strength, the best binders for AuNPs are oligo- and polydA sequences.22,23 In our previous work, we have studied the binding of oligodeoxynucleotides with 15 nm AuNPs in a slightly acidic, low ionic strength citrate solution.7 These conditions are formed in an AuNPs suspension during the classical procedure of citrate reduction of tetrachloroaurate, which is widely used for the synthesis of AuNPs. Under these “right after synthesis” conditions, a moderate rise in the temperature allows the fast, strong, and dense adsorption of oligonucleotides. Under these conditions, the oligoT sequences bind to DNA even slightly better than oligo(dA), while oligo(dC) sequences bind weaker.7 The length of the oligonucleotide influences the binding affinity and dissociation kinetics of DNA from AuNPs under various conditions.7,24 The overall differences in the AuNP binding affinity for different DNA sequences were 2 to 3 orders of magnitude in terms of KD,7 which reflects the details of the mechanism of the DNA−AuNP interaction. For a deeper understanding of the features of DNA binding to the colloidal gold nanoparticles, various broad screening methods or molecular evolution-based techniques could be employed. Currently, there are successful examples of using the SELEX approach to determine oligonucleotide motifs with enhanced affinity to various inorganic nanoparticles, for example, ZnO or calcium phosphate.25−27 In the present work, we used the SELEX procedure combined with high-throughput sequencing to select, from the randomized ssDNA pool, a set of sequences that readily bind to freshly prepared citrate-coated AuNPs. After several cycles of selection-amplification, we have acquired a series of ssDNA pools enriched with better AuNP binders. The analysis of these sequences provides some keys to understanding the DNA adsorption on AuNPs.

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Oligonucleotides were purified by reversed-phase high-performance liquid chromatography (HPLC) on an Agilent 1200 Series using a Zorbax 5 μm Eclipse-XDB-C18 80 Å column (150 × 4.6 mm2). The random DNA pool was purified by denaturing gel electrophoresis. Silver nanoparticles (0.46 nM, 40 nm in diameter) were kindly provided by Dr. I. D. Ivanov, Institute of Molecular Biology and Biophysics, SB RAS. Water was purified by a Simplicity 185 water system (Millipore) and had a resistivity of 18.2 MΩ·cm at 25 °C.

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METHODS

Preparation of Citrate-Coated AuNPs. AuNPs were prepared using a citrate reduction procedure.19 The size and monodispersity of nanoparticles were determined by transmission electron microscopy (15 ± 1 nm) and dynamic light scattering (DLS) (17.3 ± 2.1 nm).28 A typical suspension of AuNPs exhibited a characteristic surface plasmon band at 520 nm. The concentration of the particles was (3.7 ± 0.5) × 10−9 M, as calculated from the absorbance at 520 nm using the extinction coefficient of (8.78 ± 0.06) × 108 M−1 cm−1.29 (Figures S1−S3, Supporting Information). Selection. The initial randomized DNA library consisted of two constant 20-mers flanking a random 26-meric DNA sequence: 5′CCTGGATCCTTCTTCCCACT-N26-CGCGCGAGGTAGAATTCGAA-3′. During the first round, the initial DNA library (1 μL, 0.54 mM) was added to the suspension of AuNPs (1 mL, 6.8 nM). Ten identical probes were incubated for 1 h at 25 °C with constant stirring (1400 rpm). Excess DNA was removed by centrifugation for 15 min at 13,200 rpm at 25 °C. The sediment was resuspended in 1 mL of wash buffer (2 mM Tris-HCl, 1 mM NaCl, pH 7.5) and centrifuged; the step was repeated six times. To elute the DNA, 0.5 mL of aqueous ammonia (25%) was added to the sediment, and the resulting solution was incubated for 7 min at 60 °C and centrifuged; the step was repeated four times. All supernatants after the elution were combined and concentrated on the CentriVap (Labconco), and DNA was precipitated from the sodium acetate solution by adding a 3-fold volume excess of ethanol. The recovery of the fluorescein-labeled T26 oligonucleotide adsorbed and eluted using the same procedure was 104 ± 6%. For details, see Table S1, Supporting Information. For the subsequent rounds, 100 pmol of DNA and 10 pmol of AuNPs were incubated, washed, and eluted as described above. This scheme is referred further in this text as a standard incubation (S). For the polyadenylate competition rounds (A), 10 pmol of AuNPs prior to selection was incubated with 1.2 mg of polyadenylate for 1 h at 25 °C and constant stirring (1400 rpm) and washed twice with the wash buffer. After that, the polyA-coated AuNPs were incubated with DNA in a standard way. For electrophoretic selection (E), 20- or 100-fold excess of DNA from the previous pool was incubated with 100 nM AuNPs in a total volume of 5 μL. Prior to electrophoresis, glycerol (5 μL) was added, then the samples were loaded onto the 0.8% agarose gel in 25 mM Tris, 250 mM glycine, pH 8.3 and run for 30 min at 5 V/cm. The fastest band was cut from the gel, and the DNA was extracted using the QIAquick Gel Extraction Kit (Qiagen). Prism 5, GraphPad Software was used for nonlinear regression of the experimental data. Polymerase Chain Reaction (PCR). DNA after each selection step was amplified on the MasterCycler (Eppendorf) in a reaction mixture containing 0.25 mM of each dNTP, 2 μM of each primer, 50 mM Tris-HCl, 2.5 mM MgCl2, pH 8.8, and 2.5 U of Taq polymerase in a total volume of 50 μL per probe. In most cases, 23 cycles were repeated as follows: 94 °C for 7 s, 61.7 °C for 13 s, and 72 °C for 33 s. Primers with the following sequences were used: 5′CCTGGATCCTTCTTCCCACT and 5′-Biotin-TTCGAATTCTACCTCGCGCG. DNA strand separation after PCR was performed using Solulink streptavidin-agarose (TriLink) according to the manufacturer’s instructions. For PCR selection (PCR-label on the selection scheme), the standard incubation mixture was prepared, washed, and centrifuged. The DNA-loaded AuNPs (1 μL, 27 nM) were added to the PCR

EXPERIMENTAL SECTION

Chemicals. Tetrachloroauric acid (HAuCl4·3H2O) was purchased from Aurat, Russia. Sodium citrate dihydrate and magnesium chloride were from Sigma-Aldrich, USA. Tris, glycine, sodium chloride, and sodium acetate were from Amresco. γ[32P]-ATP, T4 polynucleotide kinase (EC 2.7.1.78), Taq polymerase (EC 2.7.7.7), and deoxynucleoside triphosphates (dNTPs) were from Biosan, Russia. Polyadenylic acid (potassium salt) was from Reanal, Hungary. All oligodeoxynucleotides, including a randomized DNA library, were synthesized on an ASM-800 instrument (Biosset, Russia) by the solid-phase phosphoramidite protocol using phosphoramidites from ChemGenes. 7917

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Langmuir mixture (the PCR conditions are described above), resulting in a total of 25 probes, 50 μL in volume each. Amplicon Deep Sequencing. About 1 μg of the PCR product was ligated with adapters from NEBNext Multiplex Oligos using NEBNext Ultra DNA Library Prep Kit for Illumina (NEB). Libraries were sequenced on Miseq using a 2× 300 bp paired-end sequencing kit (Illumina) in SB RAS Genomics Core Facility (ICBFM SB RAS, Novosibirsk, Russia). Raw sequences were analyzed with UPARSE pipeline using the Usearch v10.0.240.30 The UPARSE pipeline included the merging of paired reads, read quality filtering, length trimming, merging of identical reads, removing chimeras, and clustering using the UNOISE algorithm.31 The table with the mono- or dinucleotides frequencies of occurrence was normalized by autoscaling and used for principal component analysis (PCA) by Python’s scikit-learn package.32 EMSA Binding Isotherms. DNA (at least 10× concentrated against the final concentration) was added to the suspension of AuNPs to a 5 μL final volume and a 0.1 μM final AuNP concentration and incubated for 30 min at 56 °C. Before loading onto the 0.8% agarose gel, 1 μL of 50% glycerol was added as a loading solution. Electrophoresis was carried out for 30 min at 5 V/cm in 25 mM Tris, 250 mM glycine, pH 8.3. EMSA Binding Kinetics. A 200-fold excess of DNA (at least 50× concentrated against the final concentration) was added to the suspension of AuNPs to a 135 μL final volume and a 3.7 nM final AuNP concentration, incubated for the necessary time interval at 25 °C, and concenrated to 5 μL . Before loading onto the 0.8% agarose gel, 1 μL of 50% glycerol was added as a loading solution. Electrophoresis was carried out for 30 min at 5 V/cm in 25 mM Tris, 250 mM glycine, pH 8.3. The integral optical density (IOD) in the band with the highest electrophoretic mobility was plotted against time, and the half-time of the reaction (t1/2) was obtained by nonlinear regression using the equation Y = Ymax * (1 − exp(−ln2*X/t1/2)), where Y is the IOD, and X is the reaction time. 5′-End-[32P]-Labeling of DNA. Oligonucleotides (100 nmol) were 5′-[32P]-labeled in a solution (10 μL) containing 50 mM TrisHCl, pH 7.6, 10 mM MgCl2, 5 mM dithiothreitol, 50 μM (0.1 mCi) γ[32P]-ATP, and 10 U of T4 polynucleotide kinase for 1−2 h at 37 °C. The reaction mixture was subjected to flash chromatography on a Waters C18 125 Å 55−105 μm resin. The activity of 32P-containing specimens was counted in water using a liquid scintillation counter (Tri-Carb 2800TR, PerkinElmer). An Equilibrium Dissociation Constant (Binding Curve). [32P]-labeled DNA (up to 0.05 nM final concentration) was added to AuNPs in a 1.4 mL final volume (with final AuNP concentration ranging from 0.05 to 40 nM), incubated for 30 min at 56 °C, and centrifuged at 13,200 rpm. DNA low-bind tubes (Eppendorf) were used in this protocol. The radioactivity of supernatants was measured, and the concentration of the unbound oligonucleotide was determined. KD values were determined by nonlinear fitting using the equation Y = BmaxX/(KD + X), where X is the total concentration of AuNPs, Y is the bound fraction of oligonucleotide, and Bmax is the number of binding sites.33 All binding probes were analyzed at least in triplicate. The standard deviation is shown on the graphs by error bars. The statistical analysis of k-mer occurrence within the pool of DNA sequences and the secondary structure analysis data were performed using a custom Python scripts developed by M.R.L. and A.A.L., respectively.

steps: binding of a library (DNA) with the target (AuNPs), isolation of the complex, elution, and amplification of bound DNA, and second strand removal.34 Generally, 5−15 repeats of all steps are required to obtain the pools significantly enriched with high-affinity sequences. However, DNA binds to AuNPs with very high affinity. Even the weakest complexes of oligonucleotides with AuNPs possess dissociation constants (KD) of about 10 nM.7 Therefore, we were not interested in finding a “super-binder” sequence but in revealing how the DNA sequence drives the process of affinity increase and what are the common characteristics of DNA sequences that bind to AuNPs better than others. Our binding experiments were carried out at 25 °C, typically for 1 h, to select sequences that bind to AuNPs faster than others (high ka). According to our previous data, these conditions are nonequilibrium; equilibrium can be achieved within 1 h only at temperatures higher than 56 °C.7 Multiple washing steps after each binding were applied to elute sequences with low affinity (high kd). During the initial selection round, we incubated 4.3 nmol of oligonucleotide library (2.6 × 1015 sequences) with 55 pmol of AuNPs (3.3 × 1013 nanoparticles). The bound DNA was eluted from the AuNPs and PCR-amplified. We used various methods for binding and separating unbound DNA from AuNPs (Figure 1). In the standard selection scheme (index “S”

Figure 1. Outline of selection ways. L0−L7 are the names of the sequenced DNA pools. R0−R4 are the rounds of selection. Pools N1−N5 were not sequenced. Indexes S, A, E, and PCR correspond to the standard, polyA, electrophoresis, and PCR selection schemes, respectively.

in the corresponding pool names), a 10-fold excess of the DNA pool from the previous selection round was incubated with AuNPs for 1 h at 25 °C. The excess DNA was removed by centrifugation, and the particles were washed several times. In the polyA competition scheme (index “A”), a 10-fold excess (over AuNPs) of the DNA pool from the previous selection round was incubated with polyA-coated AuNPs. In the electrophoretic selection scheme (index “E”), a 20-fold excess of the DNA pool from the previous selection round was incubated with concentrated AuNPs (100 nM) and loaded onto an agarose gel. The fastest migrating band was cut out, and the DNA was eluted. In the “PCR” scheme, the DNA/ AuNPs were subjected to polymerase chain reaction after the standard incubation to increase the competition between the best binders. The outline of selection ways is shown in Figure 1. During the selection process, all pools were tested for binding efficiency using the procedure suggested in our previous work.7 At a constant concentration of AuNPs, the better adsorbing DNA binds to AuNPs at lower DNA concentrations. Binding of some threshold number of DNA strands to AuNPs adds a negative charge, which is sufficient to provide the maximal mobility during electrophoretic separa-

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RESULTS AND DISCUSSION Selection of DNA for Binding to AuNP. In this work, we employed the molecular selection procedure generally referred to as SELEX (Selective Evolution of Ligands by EXponential enrichment). A typical SELEX protocol requires the preparation of a randomized nucleic acid pool at the start of the selection. Generally, all DNA sequences of the pool contain a randomized core sequence flanked by constant sequences for binding of PCR primers. The protocol includes the following 7918

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Langmuir

We observe a significant difference between the two pools. Namely, pool N4 selected from pool L4 binds to AuNPs with lower affinity: the intensity of the fastest band is very low, and it is observed only in the last lane. In contrast, the isotherm for pool N5 does not significantly differ from that of parental pool L4. This suggests the difference in sequence specificity of DNA binding to gold and silver nanoparticles and can be the subject for further study. Pools L0−L7 were subjected to high-throughput sequencing using MiSeq (Illumina). The libraries L0, L3, L4, L6, and L7 were sequenced with the coverage of more than 3 × 105 row reads resulting in no less than 1 × 105 final unique N26 sequences. Pools L1, L2, and L5 were sequenced in the test regime with low coverage, but the information from these pools does not significantly differ from the other pools. Additionally, we have in silico generated a stochastic pool N containing 1.5 × 105 unique 26-meric sequences. The data for all pools are presented in the Supporting Information. We found that the initial DNA library and enriched pools after a short, up to four rounds, selection consisted of unique sequences only. Very rarely, the duplicates or triplicates of sequences were found. However, we anticipated that the majority of heterogeneous sequences bind to AuNPs with high affinity because of the multiple-point character of interaction, which is probably the reason for the low enrichment of pools with sequence repeats. The detailed information on the sequenced pools is provided in the Supporting Information. The absence of highly conserved sequences leads to the need to analyze a larger number of short motives of the DNA structure. First, we analyzed the nucleotide composition of all libraries to figure out the general difference between the initial library and the enriched pools. It was required to evaluate the randomness of the initial library and check whether it meaningfully differs from the enriched pools. We used the principal component analysis (PCA) for visualization of the DNA pools in k-mer (sequences consist of k nucleotides) dimensions. The method allows eliciting principal components (typical motifs or groups of motifs of nucleic acids sequences) that, with a certain degree of confidence, underlie the difference between the libraries under comparison. The comparison of the total frequencies of occurrence of certain k-mers, for instance, mono- or dinucleotides, in all sequenced libraries

tion. We compared the binding isotherms for DNA sequences of equal length. The smaller the DNA concentration corresponding to the lane where the fastest band is present, the higher the affinity of this DNA to AuNPs. Figure 2 shows the example of isothermal binding of the initial, intermediate, and final pools with AuNPs. The complete

Figure 2. Electropherograms of isothermal binding for the (A) initial DNA pool L0, (B) intermediate enriched pool N1, and (C) final enriched pool L6. All 0.8% agarose gels contain probes of ssDNA incubated with AuNPs (100 nM), and the concentration of the DNA is shown at each lane. “S” marks the start position, and “H” marks the band with the highest electrophoretic mobility.

set of isotherms superimposed on the selection scheme is presented in Figure S4, Supporting Information. At least two ways of selection generated: after the third and fourth rounds of selection, the ssDNA pools with better binding affinity to AuNPs as compared with other pools. Notably, all ssDNA pools possessed better affinity to AuNPs compared to the initial randomized pool. It was also of interest to test the specificity of selected DNA pools binding to AuNPs. To this point, we incubated pool L4 (Figure S4) with silver nanoparticles (AgNPs). The DNA pool bound to AgNPs (N4) and the nonbound pool (N5) after PCR amplification were compared using EMSA binding isotherms for AuNPs (Figure 3).

Figure 3. Electrophoretic comparison of AuNP binding isotherms for pool L4 and pools generated from L4 at binding to AgNPs: N4 (bound) and N5 (nonbound). The AuNP concentration was 100 nM, and the concentration of the DNA is shown at each lane.

Figure 4. Pools L0−L7 clustering based on a principal component analysis (PCA) of the frequencies of occurrence of (A) mono- and (B) dinucleotides. N is an artificial pool with equal frequencies of dinucleotides. Principal components PC1 (X axis) and PC2 (Y axis) are shown, each with the proportion of explained variance of the data. 7919

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Langmuir revealed a meaningful difference between the enriched pools (L1−L7) and the initial DNA pool (L0). The frequency data for these pools were visualized using the principal component analysis (Figure 4). The nonselected pools (L0, N) are well discriminated from all other pools in a two-dimensional space of the first two principal components (87 and 99% explained variance to mononucleotides (Figure 4A) and dinucleotides (Figure 4B), respectively). We can see a prominent depletion of G, C-rich dimers and accumulation of A, T-rich motifs in all pools with better affinity to AuNPs as compared to L0. The changes in the G-content are difficult to describe as a single trend: it is depleted in GC- and CG-motifs but accumulated as TG- and GT-motifs. The most evident tendency is the depletion of C nucleotide and enrichment of T within the selected pool of sequences. There is a principal difference in the frequencies of di- and trinucleotides in a sequence of the 26-mer central region for all enriched pools compared to the initial library. At the level of mononucleotides, we also observed changes in the nucleotide composition in random sequence during the selection process. Quantitatively, the content of C decreased, while the T content increased by 2−3%. The content of purines remains nearly unchanged (variations within 1% range). At first glance, these changes could not substantiate a noticeable selection for binding to AuNPs. Nevertheless, the results presented in Figure 2 and Figure S4 demonstrate the opposite with high evidence. The selection leads to the rising of DNA affinities to AuNPs, and the increase in the number of rounds within any evolution branch results in observable enhancement of the trend. For more detailed analysis of the changes in the sequences of the central 26-mer region during the selection process, we compared the frequencies for all k-mers (k = 1−6) in all sequenced pools. The percentage of occurrence of each k-mer in pool L0 was subtracted from the corresponding value for enriched pools L1−L7. The result is referenced as Dkx, where k = 1−6, the length of k-mer in nucleotides, and x is the sequential number of an enriched pool. The maximum value of D1x does not exceed 2.5%, which corresponds to 12% of the abundance in the initial library. This value is negative for C and indicates its depletion from the analyzed region. The increase is observed for A (1.8%, 9% of initial abundance) and T (0.7%, 3% of initial abundance). The average D1x for G is slightly negative (−0.3%), that is, 2% of the initial abundance. For the higher k values, we observed more pronounced changes, which comply with the PCA data. As expected, the D2x values are lower, in the range of −1.6% to +1.6%. However, the relative changes are more prominent and show specific sequence changes during the selection. Figure 5 shows the D2x values for each dimer in each pool relative to the occurrence in the initial library. We observed a significant redistribution of dinucleotide frequencies. The most negatively selected dinucleotides were CC, CG, and GC, while TC, AA, and GG were deselected to a less extent. On the contrary, the positive selection was observed for dinucleotides TA, TG, AT, and GT as well as TT, CA, AG, and GA; the latter four were selected to a smaller extent. Dimers CT and AC were neutral to the selection process. The changes in the relative abundances of dimers show the following potential trends:

Figure 5. Changes of dinucleotide occurrences (D2) for pools L3, L4, L6, and L7 relative to L0. Dinucleotide sequences are shown from the 5′ to 3′ end.

2. The sequences containing only G, T, and A, mainly in PyPu or PuPy heterodimers (where Py is a pyrimidine and Pu is a purine), are accumulated in the pools that readily bind to AuNPs. 3. Dimers CT and AC are neither selected nor deselected. We can hypothesize the existence of a large set of longer kmers that are positively selected for the affinity to AuNPs. To estimate the highest applicable k, we calculated the frequencies of occurrence for all oligomers up to k = 7. Since not all possible 7-mers were found in all pools, we limited the k value by 6. All 6-mers were present in pools L0−L7, except L2 and L5, where only 2 and 7 hexamers from the complete sets missed. For all enriched pools, all D6x values were calculated. We compared the values of D6x for each hexamer as a percentage of its abundance in L0. A distribution of D66 typical for all pools is shown in Figure 6.

Figure 6. Typical ranged values of differences (D6 value, blue) and relations (red) of hexamer occurrences in pools L6 and L0.

The analysis revealed a significant sequence specificity of the selection. On the “x” axis, all 4096 sequences of hexamers are ranged by the D66 value. We can see that most of the hexamers were subjected to selection, positive or negative. If we assume the value of D66 to be ±0.5% of the initial abundance as a limit of the selection, only 74 of all 4096 hexamers remain neutral to the selection. The remaining 4022 hexamers have significantly changed their abundance, for instance, in L6 as compared to L0. Additionally, both positive and negative selections divide the pool of selectable hexamers into two fractions of similar size. A pronounced accumulation or depletion of over 20% is observed for 829 and 872 hexamers. On average, for all accumulated motifs, the abundance is increased by 21.4%, while for all depleted motifs, it is decreased by 22.2%. The absolute leader of the positive selection for L6 was the hexamer

1. G, C-rich sequences seem to be excluded from the pools with high affinity to AuNPs. 7920

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Langmuir ATGTTG, while the most depleted one was CGCGCG. One can see a significant homology between the sequences with close D6x values. Heatmaps offer an informative option to compare D6x for all 4096 hexamers (Figure 7) and reveal the sequence−affinity

Figure 8. Correlation of the frequency of occurrence values of hexamers (ν6) and their perfectly matched sequences (ν′6) calculated for the N26 fragments within initial L0 (red), fourth round L6 (blue), and random N (yellow) pools. The center (marked by a red cross) corresponds to the most probable value of the frequency of occurrence (100%/4096) for any 6-mers within the infinite random library.

differs from the theoretical random library because of the deviations from the randomness at the levels of chemical synthesis, enzymatic transition to the dsDNA form, and preparation of the sample for sequencing. As a result of the selection, the enriched pools (e.g., L6) are even more distant from the randomness than the initial library. The frequencies of occurrence for each hexamer ν6 and its complement ν′6 were calculated. Their ratio ν6/ν′6 is varied very low for pool N (Figure 9); this value for L0 is, on average, twice higher. For

Figure 7. Typical heatmap for all complete set of 6-mers (D66 values are compared). The horizontal axis is 5′(top)/3′(bottom) 5′-terminal trimers, and the vertical axis is 5′(left)/3′(right) 3′-terminal trimers of hexanucleotide.

correlations. A pronounced clusterization of 6-meric motifs by the types of shorter sequences is observed. For example, almost all hexamers containing motifs ATG, TGT, and GTA at the 5′ or 3′ ends are accumulated (positively selected red regions in Figure 7). On the contrary, the GCG or CGC motifs at the 5′ or 3′ ends decrease the affinity to AuNPs for complete hexamers (green regions in Figure 7). The hexamers with similar nucleotide compositions show comparable efficiency of the selection. Notably, the heatmaps for all sequenced pools look similar, including low-coverage test pools L1, L2, and L5 (see the Supporting Information). This implies that regardless of the selection algorithm, the nucleotide sequence of DNA and possibly its secondary structure are the main factors that influence the affinity to AuNPs. Based on the data on the abundances of hexamer sequences and their perfect complements, we compared the enriched pools with pool L0 and artificial random pool N. The data in Figure 8 show an anticorrelation between the percentage of a certain hexamer in pool L6, ν6, and the corresponding value for its complement. The initial DNA pool and all enriched pools significantly differ from the artificial library of 150,000 unique random 26-nucleotide sequences (N26) generated in silico. In the latter case, the distribution of dots on the graph illustrates the value of “white noise” (standard deviation, ∼9 × 10−4%) at operation with the pools of implemented abundance level. In theory, with an equal frequency of occurrence for all hexamers, their percentage must be 0.0244% (1/4096). The initial library significantly

Figure 9. Ranged values of the relation of occurrences perfectly matched 6-mers (ν6 and ν′6) within L0 (blue), L6 (red), and random N (green) pools.

L6, this value is ∼1.2 times higher than for L0. Consequently, the asymmetry in the occurrences of mutual complements points that the initial library should contain nonstructured or poorly structured fragments that potentially bind better to AuNPs. The selection process increases this asymmetry. This supports the proposition that poorly structured strands bind to AuNPs more readily in comparison to dsDNA or DNA structures of the higher order.21,35,36 We can expect that such asymmetry can be realized in the core 26-mer as a nonuniform arrangement of various motifs relative to the 5′ or 3′ ends. Indeed, the analysis of the occurrences of nucleotides (1-mer) at positions 1−25 in a core 26-mer revealed a significant difference between the initial and enriched pools. The Variation Analysis of the Positional Occurrence of k-mer Motifs during the Selection. In pool L0, the occurrence of each nucleotide does not depend on its position in the core sequence (Figure 10, top). The standard deviation (σ) for the frequency of occurrence of each nucleotide at positions 1−25 does not exceed 0.04% and is 0.02% on average, whereas an average frequency of occurrence of each nucleotide at a certain position is 1%. In all enriched pools, we observed a similar redistribution of nucleotides throughout the 7921

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Figure 10. Heatmaps of nucleotide position in N26 sequence distributions for pools L0 and L6 (red-green scale) and their difference (red-blue scale). The bottom string shows the average values of differences at each position.

Figure 11. Difference of the occurrence frequencies of all dimers at positions 1−24 of N26 for pool L6 relative to L0 after clusterization.

result of the selection. We noticed that the dimers from various groups were depleting or accumulating at different positions in a similar manner. Using the AutoSOME program package, we revealed three clusters of dimers with similar positional profiles at the selection.37,38 The first cluster contains dimers positively changing the occurrence in a 5′ region (positions #1−10), the second cluster contains dimers negatively changing the occurrence in nearly all positions (#6−24), and the third cluster contains dimers in a 3′ region (#17−24). Such heatmapping easily visualizes the depletion/accumulation for any k-mer in any specific region (e.g., see the trimer and hexamer analyses in the Supporting Information). It is necessary to note that we can identify those regions within the core 26-mer where the main selection events occur. We can also detect the trends for these events. In the 5′ region, AG, GA, AC, and CA are positively selected, with all other dinucleotides being selected weakly, except TT, which is strongly depleted. On the other side, in the 3′ region, the dimers AT, TA, GT, TG, and TT are accumulated; CT and TC to a less extent. Dimers GC, CG, CC, and GG are negatively selected throughout the core 26-mer, with GC and CG depleted even more in the 3′ region. Thus, we observed a pronounced trend to the enrichment of boundary regions of the randomized core sequence with some specific blocks against other motifs. At the same time, the central region of the

core 26-mer. Heatmaps show (Figure 10) that the content of G, C, and A smoothly decreases toward the 3′ end, whereas the content of T increases in the same direction. For the frequencies of occurrence of all nucleotides at positions 1− 25 in an enriched pool L6, σ is equal to 0.11%, and the maximum value is 0.16%. It is essential that the level of the redistribution of nucleotides increases in the course of the selection. For intermediate pools L3 and L4, the level of variation at the positions is higher than in the initial library and final pools L6 and L7. Red-blue heatmaps demonstrate the difference between selected L6 and initial L0 pools in the frequencies for each nucleotide at a specific position within the N26 fragment. Blue color shows the depletion of a nucleotide, and red color corresponds to its accumulation. White color shows the low selection level. The observed difference is statistically significant; the values vary in the range from −0.45 to 0.35%, whereas in the random pool N, the standard deviation for the frequency of occurrence at a specific position is 0.004%. Differential data (L6−L0) on the positional occurrences of dimers produced even more impressive heatmap (Figure 11). Similar to the case of mononucleotides, in the initial library, σ was 0.01 on average for each dinucleotide at a specific position (0.26%). In the enriched pools, we observe a significant increase in σ to 0.026−0.044%. This reflects a significant redistribution of dinucleotides throughout the core 26-mer as a 7922

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stable hairpin as a product. Notably, the loop of the hairpin is located at positions #32−48. Thus, the 5′ region of the random core provides the formation of the stem, whereas the 3′ region forms the hairpin loop. Surprisingly, for all pools, the 5′ constant region (corresponding to Pr1) remains substantially single-stranded, that is, it does not take part in any intramolecular interactions within the components of pools and does not influence the selection of a random core (Figure 12; also see Figure S5). Thermodynamically conditioned distribution of nucleotides in the secondary structures is similar for other pools (initial L0, final L6 and L7, and artificial N) (Figure S6). As pool N demonstrates the same distribution, we can conclude that the secondary structures of this type in all pools originated from the nucleotide sequence of primers, probably with a high content of G, C nucleotides in the 3′ constant region. This provides high thermal stability of the secondary structures that involve the 5′ region of this primer sequence fragment. The 3′ fraction of this constant region is A, T-rich and is unable to form stable hairpin structures. Analysis of the stem structures revealed that they typically contain mismatches, extrahelical nucleotides, or loops. On average, in the initial pool, the enriched pool L6 or N has ∼5.5 unpaired nucleotides per 24 nt in the stem. During the selection process, the pools are enriched with stems of decreased stability. Free energy distributions for pools L7 and L6 are very close, both with the maximum on −11 kcal/mol, whereas L0 distribution is shifted to −12.5 kcal/mol. Moreover, the L6 and L7 pools have narrower distributions compared to L0, indicating the fact of the selection itself and its direction toward the weakening of stems (Figure S6). We also analyzed the occurrences of all dinucleotides in the stem or loop regions. The rate of the selection in the stem region is very low, with the standard deviation σ for the frequency of occurrence of the dinucleotide in the stem without considering positions at 0.15% for both L0 and L6. Nevertheless, some moderate changes were observed. The most significant accumulation took place for GA, CT, AT, and TA, while CC and GG were depleted (Figure 13). The

randomized core demonstrates maximum positive selectability (position #13). This trend was observed for most dimers. Whereas the maximum positive selection is observed for the central region, the 5′ and 3′ end regions are also enriched to some extent. Here, we observed the accumulation of sequences specific to the complementary motifs like 5′-AC/GT-3′ and 5′CA/TG-3′ and, to a less extent, 5′-AG/CT-3′ and 5′-GA/TC3′. The central region is significantly depleted of C, leaving a substantial variability of sequences based on A, G, and T. These trends can indicate the existence of inter- or intramolecular structures in the DNA binding sequences. However, taking into account the low ionic strength and low concentrations of all components, the formation of intramolecular hairpin-like structures is far more likely. Hairpin Structures in Pools and Their Influence on Selection. To check this statement, three DNA pools (initial L0, enriched L6, and artificial pool N) were analyzed for the presence of hairpin structures. The Mirinho package was employed, which allows analyzing sequences for the presence of perfect and imperfect hairpin structures computationally effective.39 At the first step, only the core 26-mer was sought for hairpin structures based on at least six nucleotide pairs: G/ C, A/T, and G/T wobble pairs. Hairpins containing short side loops or bulges were also considered. This algorithm is specific to the physicochemical characterization of the secondary structures in RNA and thus is unable to compute the DNA structures with sufficient accuracy. Instead, it identifies the complementary regions and estimates the relative stability (maximum expectance) of the secondary structure formation. Most sequences in the L0 and L6 pools, as well as in the artificial random pool N, form hairpin-like structures fixed with at least six complementary or wobble pairs. All pools in this study consist of 66-mer sequences including the flanking constant primer-binding regions (Figure 12). The

Figure 12. Probability distribution for the beginning of the stem, the beginning of the loop, and the end of the stem at the position in the complete 66-mer sequence for the L6 pool. The structure at the top is an average hairpin structure.

first constant region corresponds to the sequence of the direct primer (Pr1); the second is a complement for the reversed primer (Pr2). These constant regions are comparable in length with the core fragment and can also participate in the intramolecular interactions. Hairpin structures with a stem length of up to 20 base pairs (bp) and a single-stranded loop from 3 to 20 nucleotides in size were computed within the 66mer sequences. The most stable secondary structures involved the 3′ constant region. On average, in each pool, most of the positions in this region (#48−60) are involved in stable secondary structures. Otherwise, positions #61−66 were very rarely involved (Figure 12). These structures can be lost after PCR and selection because of self-priming, which gives a long

Figure 13. Changes in the frequencies of dinucleotide occurrence in pools L6 and L7 compared to L0 in the (A) stem and (B) loop region of hairpins. 7923

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A-rich sequences and possibly some A, T, C-rich variants can also be among the sequences that readily bind to AuNPs. Experimental Validation of the Revealed Regularities. To prove our suggestions, we synthesized a series of 26mers with different nucleotide sequences. They were rated using the D66 values (difference in the occurrence of composing 6-mer motifs between L6 and L0). For each 6mer, the D66 values were normalized to the frequency of occurrence in pool L0, and the sum of these values (Σ, or selection rating) for each 26-mer was calculated. All 26-mers in the sequenced pools can be rated by the value of the selection rating. We assumed that the leaders of these ratings would bind to AuNPs more readily than other sequences. We compared the AuNP binding rates for all synthesized oligonucleotides using agarose gel electrophoresis, suggesting that the rate of accumulation of the AuNPs fraction with the highest mobility depends on the association rate of the oligonucleotide with the AuNPs (Figure 14). We quantitated

involvement of the 3′ constant region into the hairpin stem substantially changes a distribution of dimers in the doublestranded fragment. The presence of the CG and GC dimers (#47−60) in the 3′ constant region leads to a sharp decrease in the content of these dimers in the stem-involved part of the random core, thus preventing the stable duplex formation. Dimers GG and CC do not follow this rule, and the GG content in this part of the sequence is four times higher than the CC content, which indicates only a partial possibility of complementary interaction. To summarize, T-containing dimers CT, TC, TT, TG, and AT are more abundant in the enriched pools. In the loop region, a prominent selection is observed. Dimers AT, TA, GA, AG, GA, GT, and TG are accumulated, and dimers CG, GC, and CC are negatively selected. Dimers GG, CT, TC, CA, TT, AC, and AA are not subjected to either selection or deselection. The most pronounced loss is that of C, which is compensated through the accumulation of T and A. These regularities support the implications we made above during the analysis of k-mer distribution in the core 26-mer without taking the secondary structure into account. In part, the same trimers are accumulated in the enriched pools with higher affinity to AuNPs (ATG, AGT, and TGA in the stem and loop regions and TGT, TTG, and GGT in the stem region), while G, C-rich trimers in both regions are lost (Figures S7 and S8). Finally, we can conclude that a large pool of various DNA sequences survived under the conditions of the selection. These sequences should bind to AuNPs strongly (low KD) and quickly (high ka). According to our sequence-structure analysis, the main requirement is the absence of stable secondary structures that prevent nucleobases from interacting with the surface of the nanocrystals. The presence of relatively weak duplexes alongside with single-stranded fragments and hairpin-like structures apparently does not interfere with the DNA adsorption onto AuNPs. Moreover, we cannot exclude the possibility that hairpin stem formation helps to break single-stranded stacking between the nucleobases in the hairpin loop and promote the binding of such “open” site to AuNP. After the formation of this nucleation complex, additional nucleobases that were involved in weak structures can interact with gold. These nucleobases can probably be excluded even from the stem region if it is destabilized by bulges or uncanonical pairs. We cannot also exclude the possibility of another type of DNA binding to gold. The DNA strands with alternating purine-pyrimidine sequences unable to form the secondary structures apparently possess some advantages at binding to AuNPs compared to purine-purine and pyrimidine-pyrimidine enriched fragments. In homopurine sequences, all nucleobases participate in the effective intrastrand stacking. In the case of G-rich sequences, more complex spatial structures can form through H-bonding. Considering that the main molecular mechanism of nucleobases binding to gold is its parallel laying onto the metal surface, any competing base interactions will negatively influence the adsorption of DNA to the metal. These findings are consistent with the literature data.36 Double-stranded DNA has significantly lower affinity to AuNPs than ssDNA.35 We have also previously shown that a moderate rise in the reaction temperature, which decreases the stacking interaction in the single- and doublestranded states substantially, accelerates the process of DNA adsorption to AuNPs.7 Consequently, the unstructured G, T,

Figure 14. Kinetics of oligonucleotides (A) #5 and (B) #11 binding to AuNPs studied by gel electrophoresis. Time points at each lane show the time between the start of incubation and loading onto the gel for each probe. Oligonucleotide sequences are shown in Table 1.

the intensity of the highest mobility band and determined the half-time for its accumulation (saturation half-time, t1/2) as a quantitative parameter for binding kinetics. A typical nonlinear regression chart is shown in Figure S9. Data on selection ratings, saturation half-times, and equilibrium dissociation constants are summarized in Table 1. For most oligonucleotides, we found a correlation between the Σ and t1/2 values (Figure 15A). Only two oligonucleotides significantly miscorrelate and are not shown in Figure 15A. The first miscorrelated sequence is A26 (Table 1, #11) which readily binds to AuNPs, has low t1/2 and KD, but is lost during the selection (Σ = 13.09). The loss of this oligonucleotide during the selection can be attributed to its outstanding hydrophobicity, which may promote the adsorption to reaction tubes. Adenine, adenosine, and corresponding nucleotides show the highest retention times at HPLC on C 18 sorbents.40−42 Adenine is also the most hydrophobic base by its log P value defined by the partition between octanol and water.43 For the second sequence, two sets of data were obtained (Table 1, #5 and #6). We observed two distant bands during the analysis of this oligonucleotide after labeling (Figure S10). Considering the very high difference in the mobility, the upper band is likely to be an intermolecular quadruplex. The formation of this structure is very probable for oligoG sequences.44 The band with higher electrophoretic mobility (Table 1, #5) should be a single-stranded state of this oligonucleotide and has a different affinity to AuNP. This sequence is neutrally selected (Σ = −0.11) and slowly binds to AuNPs (t1/2 = 4.9 ± 0.6 h) but tightly (KD = 1.9 ± 0.1 nM). The data for #6 miscorrelate with other data probably because of the formation of higher order structure, intermolecular quadruplex, which has significantly higher total negative 7924

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Table 1. Selection Ratings (Σ), Saturation Half-Times (t1/2), and Equilibrium Binding Constants (KD) for Oligonucleotides Studied for Binding with AuNPsa #

sequence

Σ

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

CAGCGGAGTAGTGATATCATGGAGTG ATGTAGTGTTCGATGTGTTGCTGTGT TACAGATGAGGTGTTCGATTTGTATA GCGCGCGGGATGCTTCTAGATCAGTT GGTGGGGGGGTGGATACGATACGTGC GGTGGGGGGGTGGATACGATACGTGC* TGTTGTTGTTGTTGTTGTTGTTGTTG ATGATGATGATGATGATGATGATGAT CGTGCAGCTTTTGCTGCACGCACTCAGG CGTGAGACTGTGCACCTTTTGCTGCACA AAAAAAAAAAAAAAAAAAAAAAAAAA TTTTTTTTTTTTTTTTTTTTTTTTTT CCCCCCCCCCCCCCCCCCCCCCCCCC GCACGATGTAGTGATGTGATGTATCG AGGCAGGGCATCGCGCGTGAAAGGAA ATTAACGCGCGTAAGGGCCCTAGTCG GGCACAGAGGTTGAATGTTGTGTTGT CGTATCTGATAGGTATTGTGAGATCG TATCGCGAGCGGAGTAATCTAGTGAG

5.36 10.92 7.44 −1.21 −0.11 −0.11 11.98 15.65 −0.22 3.88 −13.09 −8.23 −9.45 12.03 −5.93 −5.70 9.66 7.05 −1.78

t1/2 (h)

KD (nM)

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1.0 ± 0.2

3.3 0.98 0.91 2.6 4.9 8.6 1.1 1.7 1.9 1.1 1.6 4.8 4.6 1.9 3.1 4.8 1.7 2.2 3.7

0.1 0.16 0.12 0.1 0.6 1.5 0.2 0.2 0.3 0.2 0.2 0.3 0.3 0.2 0.4 0.3 0.3 0.2 0.4

1.9 ± 0.1 5±2

6±1 1.8 ± 0.3 18 ± 3

1 ± 0.1 0.9 ± 0.1

a

The asterisk (*) denotes the possible intermolecular quadruplex form.

Figure 15. Linear correlation between the (A) Σ and t1/2 values (B) Σ and ΔA (Å2) values.

to AuNPs were noted previously.47 Taking into account that citrate-coated AuNPs reveal measurable hydrophobicity,48 this correlation does not look surprising. The same general trend was observed for the hexamers from pool L6, correlation with normalized D66 (Figure S12). The lower correlation coefficient most probably originated by the presence of the set of dinucleotides in the hexamer with different ratings. These findings clearly show the importance of the unhampered direct contact of nucleobases with the surface of AuNPs for the facile adsorption of DNA. This also takes place at the interaction of AuNPs with other molecules such as surfactants like cetyltrimethylammonium bromide (CTAB) or bis(p-sulfonatophenyl) phenylphosphine (BSPP) and proteins.1,49,50 We analyzed the secondary structures for all oligonucleotides using the RNAstructure software.51 The most probable structures are shown in Table S2, Supporting Information. They are in good agreement with our model: most of the wellselectable structures contain weak stems and G, T, A-rich loops. Considering the published data, we can claim that structured DNA has the lowest affinity to AuNPs.7,21,36 Among the nonstructured sequences, it is C26, although under the conditions studied, the formation of i-motifs is possible.52,53

charge. This obviously influences both the kinetics and thermodynamics of adsorption. For all other oligonucleotides, we observed a reasonable correlation between Σ and t1/2. KD values do not correlate with Σ; even significantly deselected oligonucleotides bind to AuNPs tightly under equilibrium conditions (see Methods). Classical molecular dynamics simulations of nucleobase monolayers show that the interaction of the nucleobases with the gold surface is strongly modulated by base−base interactions and reaches a maximum when a full monolayer is formed.45 It was of interest to check the parameters of the stacking-unstacking process46 in oligonucleotides for correlation with the selection rating (Σ). We have analyzed the total changes of the nonpolar surface area at single-stranded stacking disruption (ΔA) calculated as the sum of impact of all dinucleotide steps in the oligonucleotides.46 The selection rating (Σ) strongly correlates with the total changes of the nonpolar surface area between stacked and unstacked conformations (ΔA). (Figure 15B). Two homopyrimidine sequences miscorrelate in this case and are not shown. Changes in the polar and summary surface area between stacked and unstacked conformations do not correlate with Σ (Figure S11). Hydrophobically driven effects at DNA binding 7925

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Langmuir The equilibrium concentration of nonstructured C26 can be significantly decreased in this case. Among the structured 26mers, this obviously should be (GC)13. The latest also has the lowest Σ (−20.59); the t1/2 value was not determined, and saturation of AuNPs with this oligonucleotide was not observed for 22 h. This additionally confirms that highly structured nucleic acids have low affinity to the AuNPs. The data obtained for all tested oligonucleotides also confirm the findings that unstable stem-loop structures, which can be easily disrupted at the binding with AuNPs, have some advantage at binding to the AuNPs due to the easier flattening of nucleobases at the AuNP surface.36

Additional information regarding the characterization of AuNPs by visible light absorbance spectroscopy, transmission electron microscopy, and DLS spectroscopy, complete outline of DNA selection ways with the electropherograms, statistical and thermodynamic analysis of structural motives of DNA hairpin for the complete 66-mer sequences for L0, L6, L7, and N pools, the typical nonlinear fitting curve of the saturation halftime (t1/2) for DNA binding to AuNPs, proposed structures for tested oligonucleotides, typical data on the influence of total changes of the nonpolar surface area at single-stranded stacking disruption within hexanucleotide chains, and normalized percentage of occurrence of the sequences for the selected L6 pool (PDF) Nucleotide sequences for all pools with rating, ratings of selected hexameric motives with heatmaps of their occurrence in selected pools, and the data on dimers, trimers, and hexamers: the occurrence and difference in the occurrence at a specific position for all pools (relative to the L0 pool) (XLSX)

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CONCLUSIONS For a deeper insight into the regularities of DNA binding to the gold nanoparticles, we performed a SELEX procedure for the pool of 66 nt ssDNA sequences containing the 26 nt randomized region (N26). To find the sequences that bind to the AuNPs strongly and faster than others, we performed selection procedures in nonequilibrium conditions. We observed the increase in binding strength during the selection and the specificity of the selected pools to the gold nanoparticles. Additional selection factors (polyA capping, nonequilibrium electrophoretic separation, and thermocycling conditions) widely used for PCR appear to play a secondary role in the selection process. In all cases, we were unable to determine the significant enrichment of the pools with unique 26-meric sequences. At the same time, according to the statistical analysis, specific structural motives, k-mers (k = 1−6 nt), were reliably positively or negatively selected in the randomized regions of libraries. A number of factors influence the direction of the selection of an individual k-mer, that is, nucleotide composition and nucleotide sequence in k-mers, their position in the variable N26 motif, and its involvement in the formation of intra- and intermolecular secondary structures. As a result, a rather large set of diverse A, T, and G enriched k-mers undergo a pronounced positive selection, and these sequences demonstrate faster and strong binding to the AuNPs. For facile binding, such structural motifs should be located in the loop region of weak intramolecular complexes hairpins with imperfect stem or other portion of the structure, which is unpaired under selection conditions. One of the main factors that ensure an increased affinity of an oligonucleotide to AuNPs is its ability to form the maximum number of hydrophobic planar contacts between the nucleobases and gold surface. The efficiency of such multipoint network contacts is strongly determined by the nucleotide composition, sequence, and structure of the oligonucleotide and the requirement to rearrange the intra- and interstrand base stacking interaction at the formation of the DNA layer at the AuNP surface. To summarize, the detailed analysis of DNA binding to AuNPs shows that sequence specificity of this interaction is low due to its nature, although the presence and the number of specific structural motifs in DNA affect both the rate of formation and the strength of the formed noncovalent associates with AuNPs.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Marsel Kabilov: 0000-0003-2777-0833 Dmitrii Pyshnyi: 0000-0002-2587-3719 Author Contributions §

P.V., A.E., and A.L. contributed equally.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The work was supported by the Russian State funded budget project (VI.62.1.4, 0309-2018-0004). The work of A.A.L. and M.R.K. in the development methods of computational analysis part was supported by the Russian Science Foundation (grant no. 18-14-00357).

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.langmuir.9b00661. 7926

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DOI: 10.1021/acs.langmuir.9b00661 Langmuir 2019, 35, 7916−7928

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DOI: 10.1021/acs.langmuir.9b00661 Langmuir 2019, 35, 7916−7928