Abstract
Sensing and characterization of water-soluble peptides is of critical importance in a wide variety of bioapplications. Single molecule nanopore spectrometry (SMNS) is based on the idea that one can use biological protein nanopores to resolve different sized molecules down to limits set by the blockade duration and noise. Previous work has shown that this enables discrimination between polyethylene glycol (PEG) molecules that differ by a single monomer unit. This paper describes efforts to extend SMNS to a variety of biologically relevant, water-soluble peptides. We describe the use of Au25(SG)18 clusters, previously shown to improve PEG detection, to increase the on- and off-rate of peptides to the pore. In addition, we study the role that fluctuations play in the single molecule nanopore spectrometry (SMNS) methodology and show that modifying solution conditions to increase peptide flexibility (via pH or chaotropic salt) leads to a nearly 2-fold reduction in the current blockade fluctuations and a corresponding narrowing of the peaks in the blockade distributions. Finally, a model is presented that connects the current blockade depths to the mass of the peptides, which shows that our enhanced SMNS detection improves the mass resolution of the nanopore sensor more than 2-fold for the largest cationic peptides studied.
Keywords: resistive-pulse nanopore sensing, peptides, angiotensin, neurotensin, alpha hemolysin, Au25(SG)18, thiolate-capped clusters, nanomolecules
Graphical Abstarct
Biosensors offer an enticing direct route for detection and quantification of a host of medically relevant biomarkers (e.g., proteins, heavy metals, DNA, RNA, and peptides).1–10 Detecting, characterizing, and quantifying peptides is of particular interest given the role that proteolysis plays in the onset and development of a wide range of diseases11–13 (e.g., cancer,14–16 Alzheimer’s disease,17,18 and Huntington’s disease19). The vast majority of peptide sensors utilize an immunoassay strategy,20,21 although limitations of this approach have motivated the development of mass spectrometry for peptide detection.22–24 Both approaches utilize costly and time-consuming methodologies, which complicates the development of clinically based peptide sensing. Our goal is to explore alternate means of peptide characterization that could enable rapid and low-cost detection in a clinical setting. This requires the ability to identify label-free peptides in heterogeneous mixtures. One technique that shows promise toward this goal is resistive-pulse nanopore sensing.25–29
While nanopores have previously been used to detect and characterize proteins and peptides,30 further study is required to convert this technology into a robust scheme for identifying specific peptides from heterogeneous mixtures. Nanopore-based detection began in earnest as a method for next-generation DNA sequencing.31 In addition to DNA analysis, there is also a growing body of research into a wide range of other analytes.32–42 For nanopore sensing, the size of the target analyte can dictate the type of nanopore used. For example, full-length proteins have been investigated with solid-state nanopores fabricated in SiN43–45 and pulled glass nanopippettes.46,47 The pores used in these experiments were sufficiently large to enable transport and detection of protein molecules ranging in size from ~30 kDa to ~300 kDa. While these detectors offer an excellent label-free method to quantify protein concentration, it remains difficult to reproducibly fabricate solid state nanopores at the length scales required to distinguish between two similarly sized peptides without internal standards. This can be improved with biological nanopores which possess a high degree of reproducibility and precisely controlled assembly.48,49
One of the first demonstrations of protein analysis with biological nanopores studied polymers interacting with the nanopore and a protein too large to enter the pore’s lumen.50 However, to enable protein sensing directly with nanopores, the molecules must be able to partition into the nanopore. This can be accomplished in several ways. For instance, native peptides and proteins can fold into structures small enough to partition into the pore directly.39,51,52 In some cases the pore can be modified to enhance polypeptide attraction and subsequently lower the energetic barrier to partitioning.53 Proteins have also been chemically denatured,37,54,55 thermally melted,56 and cleaved into smaller fragments38,57–60 to facilitate entry into the pore. To detect specific analytes from a complex sample matrix (i.e., blood serum), peptides can be isolated with affinity tags and extracted for analysis with enzymatic reactions.58 Indirect sensing schemes have been demonstrated in nanopores where specific DNA sequences were detected through interactions with a protein recognition site.61–63 This scheme can be reversed whereby proteins can be detected after binding to a specific DNA molecule.64,65 These examples show the potential of nanopore sensing for proteomics.27 Furthermore, they suggest that nanopore sensors may complement existing analytical modalities because they offer the possibility of quantifying the peptide concentration, observing different peptide conformations,41,66 and measuring a variety of enzymatic reactions.31,67 We propose to advance nanopore-based peptide sensing by adopting single molecule nanopore spectrometry (SMNS).
Previous efforts with SMNS focused on polyethylene glycol (PEG) molecules.68–71 These results showed that biological nanopores are capable of identifying individual PEG molecules based on their size68 and chemistry69,72 with mass resolution to better than a single monomer unit (≈44 g/mol). High-resolution baseline separation has been demonstrated for other analytes,73,74 and SMNS efforts have been extended to include size-dependent detection of short nucleic acids.75 To demonstrate the viability of nanopore sensing for proteomics, one must identify peptide species with indeterminate size and sequence.
While PEG has been used extensively to develop SMNS,68,76–78 peptides present more difficulty given their diverse chemistry (i.e., charge, sequence, secondary structure, etc.). Nevertheless, this diversity has been studied via nanopore sensing in a number of ways including estimating peptide–pore binding interactions of cationic peptides,79 low-resolution structure measurements,80,81 or force-spectroscopy,66 and manipulating peptide-pore kinetics.53,82–84 These results represent important progress in using nanopore sensing to analyze peptides, but demonstrating SMNS-like detection of peptides requires accurate estimates of the current blockade from each peptide interaction with the pore. This can be achieved by manipulating the duration and noise of each blockade.85
There is a precise connection between current blockade depths and corresponding molecular size,68,69 first observed for PEG due to small current fluctuations (ΔiRMS ≈ 2 pA, V = 50–80 mV, bandwidth = 100 kHz) and long residence times (tres ≈ 0.5 ms). Further improvements to PEG-based SMNS were recently demonstrated with the addition of metallic clusters to the cis-side of the alpha-hemolysin (αHL) pore.85,86 The clusters increased the time PEG spent in the pore by more than an order of magnitude while having little effect on the current fluctuations, which led to a reduction in the observed peak widths in the blockade distribution.
Here we report on our efforts to improve the SMNS sensing protocol by using cluster-enhanced SMNS to detect and characterize small water-soluble peptides at the single molecule limit. We present a generalized model that predicts the molecular mass from the magnitude of the peptide-induced current blockade with and without a gold cluster in the pore. Finally, we emphasize how current blockade fluctuations play a critical role in establishing the resolving power of the SMNS technique and that one can further improve peptide analysis by modifying the solution conditions to create more flexible peptides and correspondingly lower noise blockades. This yields narrower peaks in the current blockade distributions. These results demonstrate a viable way forward to perform nanopore analysis of peptide detection and identification at the single molecule limit.
EXPERIMENTAL METHODOLOGY
The experimental procedure and data processing methodology have been described elsewhere.85,86 We briefly summarize the approach here. Unless stated otherwise, all chemicals and peptides were purchased and used as received from Sigma-Aldrich (St. Louis, MO). A 25-μm-thick, polytetrafluoroethylene (PTFE, Teflon) sheet with a 100 μm hole (Eastern Scientific, Rockville, MD, USA) was attached to a PTFE holder with polydimethysiloxane (PDMS, Kwik-Cast, World Precision Instruments, Sarasota, FL, USA). A 1 mg/mL pentane-based prepaint solution (1,2-diphytanoyl-sn-glycero-3-phosphocholine (DPhyPC, Avanti Polar Lipids, Alabaster, AL, USA)) was applied to both sides of the 100 μm hole after which the partition was positioned ca. 300 μm from the top of a microscope coverslip. The mounted partition was placed on top of an inverted microscope (AxioObserver D, Zeiss, Germany) for optical access. Approximately 500 and 2500 μL of 3 M KCl, 10 mM Tris electrolyte solution was added above and below the partition, respectively, to support the formation of a lipid bilayer membrane. Unless stated otherwise, the lower compartment (trans-side) was additionally filled with 20 μM of desired analyte.
Lipid bilayer membranes were formed by using a glass bead formed at the end of a glass rod to paint a 10 mg/mL DPhyPC:hexadecane solution over the 100 μm hole. After several seconds or after the application of a sufficient electrical potential (>150 mV) across the hexadecane plug, a stable bilayer membrane formed. Following the formation of the membrane, a 0.1 mg/mL solution of αHL monomers (List Biological Laboratories Inc., Campbell, CA, USA) were ejected in the vicinity of the membrane until several hundred αHL pores were formed across the membrane. The number of αHL pores in the membrane was monitored by applying a small transmembrane voltage (≈1 mV) and comparing the current to the expected conductance of a single channel.87
A Ag/AgCl electrode was inserted into a quartz capillary (1 mm OD, 0.7 mm ID with filament, Sutter Instrument, Novato, CA) patch pipette tip containing 3 M KCl and ca. 40 μM Au25(SG)18 clusters. The cluster synthesis protocol has been described elsewhere.85 The patch pipet was lowered onto the membrane to isolate a single channel. The presence of a single pore can be determined by comparing the time-averaged current with the expected single channel current. Ionic currents were recorded using a patch clamp amplifier (Axopatch 200B, Molecular Devices, Carlsbad, CA) with a built-in 4-pole 10 kHz low-pass Bessel filter. Unless stated otherwise, the ionic current was sampled at 50 kHz using an analog-to-digital converter (Digidata 1440A, Molecular Devices) and processed with commercially available software (pClamp10, Molecular Devices). Data analysis was performed in a manner previously described85,86 with in-house software written in LabView 8.5 (National Instruments, Austin, TX). Briefly, a thresholding algorithm is used to identify blockades with the threshold set at 35% below the averaged open current for a time greater than the minimum cutoff-time. All results reported herein used a value of tcut = 180 μs unless otherwise noted.
RESULTS AND DISCUSSION
Despite the increasingly sophisticated understanding of how proteins and peptides interact with nanopores,79,88–90 little effort has focused on the quantitative connection between blockade depth and peptide size. The goal here is to show that SMNS analysis enables one to estimate the mass of a peptide from the magnitude of the current blockade. To demonstrate proof-of-principle, we selected five biologically relevant water-soluble peptides (leu enkephalin (LE) (Proteochem, Hurricane UT), angiotensin I (A1), angiotensin II (A2), polyglutamine-binding peptide 1 (QBP1) and neurotensin (NT)) whose descriptions, amino acid sequences, molecular weights and calculated charges at pH 7.2 and pH 5.8 are all listed in Section 1 and Table S1 in Supporting Information.
In a typical experiment, ionic current is driven through a nanopore at a fixed applied potential. When the peptide partitions into the trans-side of the pore it causes a short-lived resistive pulse (i.e., a decrease in the observed current). In Figure 1, the pore is tested with NT on the trans-side of the membrane, while anionic Au25(SG)18 clusters (D ≈ 2 nm91,92) are introduced on the cis-side. It was previously shown85 that these clusters are negatively charged with charge z = −5e to −9e, which precludes the entry of multiple clusters into the pore. When there is no cluster in the pore, resistive pulses are deep, short-lived, and infrequent (Figure 1 left and Table S2 in Supporting Information). When a cluster occupies the pore, the peptide-induced blockades remain deep and become longer-lived and more frequent (Figure 1 right and Table S2 in Supporting Information).
Each of the peptides examined here gives rise to current blockades that exhibit Gaussian fluctuations. In such cases, the most directly accessible blockade characteristic is the magnitude of the current interruption, the average blockade depth, presented as the ratio of the mean blocked current to the mean open channel current with or without a gold cluster in the pore. For each peptide studied, the resistive pulses were characterized and collected into histograms of the blockade depth ( and ) (Figure 2A). Each peptide yielded a blockade depth distribution that is dominated by a single peak. For A1 and NT, there are smaller peaks to the left of the main peaks that may arise from contaminants or secondary conformations of the peptide within the nanopore.45 Figure 2B shows corresponding residence time (toff) and time between event starts (ton) distributions. Fitting single exponential functions to these distributions enables one to extract the mean peptide residence time τres and mean peptide on-rate kon respectively.
A cluster in the cis-side of the pore has previously been shown to serve as a localized charge trap that attracts cationic analyte entering from the trans-side.85,86 Cationic peptides will diffuse within the trans-side lumen and interact with the cluster near the constriction separating the lumen and vestibule sections of the pore. This interaction will either slow down the translocation of the peptide through the pore or increase the free energy barrier to escape out the trans-side into which it originally entered. Regardless of the exact mechanism of peptide escape from the pore, the presence of the gold cluster serves to increase the mean residence time and the on-rate of analyte to the pore. Increasing the residence time leads to more accurate estimates of the blockade depth () and this in turn yields narrower peaks in the blockade distributions (fwhm).85,86 Figure 2 and Table S2 in Supporting Information illustrate that this is also true when detecting and analyzing blockades from cationic peptides. In all cases the cluster increases the mean residence time ca. 3-fold. All of these results show that introducing charged metallic clusters in the cis-side of the pore leads to improved sensing of the cationic peptides as evidenced by the increased on-rate of peptides to the pore, residence time of peptides in the pore, and narrowing of the blockade distributions by up to 25% from the open pore system. It is worth noting that while the QBP1 anionic peptide shows an unexpected increase in the mean residence time with a cluster in the pore, the efficacy of sensing the anionic peptide with the gold-occupied pore is reduced because the repulsive interaction between the anionic gold-cluster and QBP1 lowers the peptide on-rate and destabilizes the gold states (see Figure S3 and Table S2 in the Supporting Information). Note that given the shortened time for LE in the pore, all LE data was sampled at 100 kHz and the cutoff duration for each blockade was set to 60 μs. The constant slope seen in Figure 2B for LE’s residence time distributions suggests the 10 kHz filter had little effect on our estimates for τres(LE).
To reduce the reliance on internal standards, and to extend SMNS methodology to analytes beyond regular repeating homopolymers (e.g., PEG, homo-DNA), a connection between current blockade magnitude and the molecular weight of the analyte must be generalized. Figure 3 shows the most probable blockade depth (peak locations from Figure 2A) plotted as a function of corresponding molecular weight for the peptides studied herein and a corresponding distribution of PEG molecules. A clear trend emerges between the current blockade depths and the mass of the sequestered polymer. Already demonstrated for PEG68 and poly dA,75 the trend shown here suggests a connection also exists between the blockade depth and molecular weight for this random collection of peptides. As with previous results, the deepest current blockade peaks correspond to the largest molecules, and the shallowest blockade to the smallest molecules.
Two of us have previously developed a model that characterized the current blockades (blockade depth and residence time) from PEG using a thermodynamic description for the polymer-pore-electrolyte interaction.69,72,93 In this model, the pore is treated as a piecewise linear combination of open pore and polymer occluded pore. The polymer modifies the current through two mechanisms: volume exclusion and interactions between the ions and the polymer. Here a simplified model is developed that explicitly addresses volume exclusion and treats physical interactions (e.g., electrolyte interactions, electroosmosis, etc.) peripherally in an ad hoc manner. This model connects the peak blockade depth for each polymer to that polymer’s mass
(1) |
where Mp is the mass of the polymer, Mf is the mass of the smallest polymer that completely fills the nanopore volume, ν is the Flory exponent, and a and γ are free parameters that describe ionic binding to the polymer in the pore. (A derivation of eq 1 with more complete descriptions of the variables can be found in Section 2 of the Supporting Information.)
While the clusters were shown to increase the mean residence time for the cationic peptides (Figure 2B) this enhancement is not useful for routine quantitative identification if the connection between current blockade depth and molecular weight is lost. Figure 3B reports the blockade peak positions as a function of molecular weight for the polydisperse PEG mixture and the four cationic or neutral peptides from Table S1 with a gold cluster in the pore. We find a correlation similar to the open pore case between the mean current blockade depth and molecular weight. To model the data in Figure 3B we transform eq 1 by incorporating a gold cluster into the cis-side of the pore. A complete description of this transformation procedure can be found elsewhere,86 but briefly the gold cluster in the pore leads to a slight modification of the pore resistance that can be taken into account with a piecewiselinear ohmic model that also incorporates an interaction term between the polymer and gold cluster. The overall result transforms the blockade depth without a cluster in the pore into the following expression
(2) |
where is the experimentally determined ratio of the open pore current to the current through the gold occupied pore and d is a freely adjustable model parameter that characterizes the interaction between cluster and peptide. The data shown in Figure 3A,B was simultaneously fitted with eqs 1 and 2. To minimize the number of free parameters, we fixed the value of Mf = 3000 g/mol77 and assumed that the peptides behave as polymers in a good solvent so the Flory exponent is ν = 0.6.94,95 In addition, we used the experimentally determined value of c = 1.38, which leaves three adjustable parameters a, γ, and d with which to fit the 9 data points plotted across both Figure 3A and B. Least-squares fitting yields values of a = 1.68 ± 0.12, γ = 0.34 ± 0.06, and d = 0.13 ± 0.04 (±1 S.D.). Notably, the d-parameter is in good agreement with previous PEG-based results.86 The quality of the fit, parametrized by the residuals (R) in Figure 3, suggests one can deduce the molecular weight of near-neutral peptides from the magnitude of the corresponding current blockades both with and without a gold cluster in the pore.
The ability to connect the blockade depth to the molecular weight suggests that the SMNS technique should be relevant to a wide range of molecules beyond homopolymers (i.e., PEG).68,69 One reason PEG so clearly illustrates blockade distributions with single monomer resolution is that fluctuations within each blockade are small enough to yield accurate estimates of the mean current. Specifically, the standard deviation of typical current blockades from PEG in 3 M KCl under an applied voltage of 70 mV is on the order of 2 pA. It was previously demonstrated85 that averaging a blockade with this degree of noise over a duration of 1 ms yields a peak in the current blockade distribution with fwhm ~8 × 10–3, which is smaller than the typical peak separation for PEG (). This difference explains why PEG-induced peaks are easily resolved with the SMNS approach. Therefore, in order to make the SMNS approach viable for peptides one must increase the mean blockade time (via gold clusters) and also reduce the fluctuations so the peaks in the current blockade distribution are sufficiently narrow to enable clear distinctions between different mass peptides. Narrowing these blockade distribution peaks enables one to identify different populations of peptides in a mixture and further narrowing of the peaks to baseline resolution would enable one to identify a peptide from a single current blockade. This motivates additional study of methods for reducing the current fluctuations within each peptide induced blockade.
Figure 4 compares the degree of fluctuations between PEG and A1. Typical current blockades for each molecule are shown in Figure 4A and it is clear that A1 produces larger current fluctuations than PEG. This is parametrized in the blockade standard deviation scatter plots shown in Figure 4B. The A1 peptide yields fluctuations ca. 8× greater than the PEG. This discrepancy explains why the peak for A1 is so much wider in Figure 2A than similar peaks for PEG.68 One possibility for why the PEG yields smaller fluctuations than A1 is that PEG molecules are more flexible than peptides so they undergo more rapid fluctuations in the pore that are undetectable at the time scale set by the inverse bandwidth of the measurement (B = 10 kHz). This is supported by the fact that the persistence length of PEG (lp ≈ 0.38 nm96) is much less than typical persistence lengths for homomeric peptides (lp ≈ 4 nm97,98).
In addition to persistence length, structure also plays a role in establishing polymer dynamics.99 This suggests that modifying the peptide structure either through pH titration or the addition of chaotropic salts should have an effect on the fluctuations within each current blockade. To test this assertion, we first performed a pH titration experiment on A1 and NT to see the effect on current blockade fluctuations.
The expected charge of each peptide can be estimated by assuming a simple acid–base dissociation model. The results for the effective charge calculations of A1 and NT as a function of pH are shown in the Supporting Information (Section 3 and Figure S1 Supporting Information), and they demonstrate that while the NT charge remains nearly constant over the pH range shown, the A1 charge undergoes a dramatic change as expected given its histidine residue (pKa ≈ 6). Circular dichroism and NMR measurements on angiotensin peptides have shown the role that the protonated state of the histidine residue has on modifying the local environment of the nearby tyrosine residue and by extension the structure of angiotensin.100,101 This is consistent with more recent efforts that studied β-hairpin peptide interactions with alpha hemolysin under acidic conditions.41 In that work, protonated histidines were shown to destabilize a β-hairpin into a random coil structure. Therefore, if more flexible peptides reduce the standard deviation of the current blockades, then lowering the solution pH should reduce the fluctuations from A1 induced peptides while having little to no effect on NT induced blockades. This is supported by the data shown in Figure 5 and suggests that modifying the solution conditions and structural state of peptides can reduce the current fluctuations of the current blockades.
In addition to modifying the charge of the peptide with pH, one can also modify the structure of the peptide by disrupting hydrogen bonds that stabilize β-folds with a chaotropic salt such as guanidine hydrochloride (Gdm-HCl).102 If our hypothesis regarding the connection between peptide flexibility and blockade fluctuations is correct then we expect a reduction in blockade standard deviation with the addition of Gdm-HCl due to the fact that the broken hydrogen bonds will yield a more flexible peptide. Figure 6 demonstrates this result for A2, A1, and NT. In all three cases it is clear that the addition of 1 M Gdm-HCl to the trans-side of the pore yields blockades with standard deviations that are nearly halved. This, along with the pH titration experiment, illustrates that modification to the peptide structure via targeted protonation of residues or destabilization of hydrogen bonds can reduce the fluctuations in individual current blockades, which should yield narrower peaks in the current blockade distributions. I–V curves that show 1 M Gdm-HCl on the trans-side of the pore has very little effect on the αHL pore are shown in Section 6 of the Supporting Information.
Motivated by the results in Figures 5 and 6, we explored the role that reducing the peptide fluctuations have on the quality of the current blockade distributions. Figure 7A,B,C compares the current blockade distributions for the three largest cationic peptides A1, A2, and NT. LE and QBP1 were omitted from this analysis because LE is too small to expect any significant denaturing under Gdm-HCl while QBP1 is anionic and shows no enhancement in the presence of anionic clusters. For all three peptides, the blockade distributions are narrowed from the open pore pH 7.2 case (dashed lines) to the gold-in-pore, pH 5.8, 1 M Gdm-HCl case (solid lines). This is expected given the reduction in blockade fluctuations seen in Figures 5 and 6 and reported in Table S3 of the Supporting Information. It is worth noting that the small secondary peaks in the blockade distributions for A1 and NT remain with the addition of Gdm-HCl, although to a lesser degree, suggesting the presence of contaminant in these samples. In any case, it is clear that decreasing the blockade fluctuations (via lowering pH and adding chaotropic salt) and increasing the residence time (via gold clusters) leads to narrowing of the peak in the current blockade distribution and improved selectivity of the SMNS technique. Figure 7D shows the blockade depth peak positions for the idealized solution conditions is still well described by the model from eqs 1 and 2. Optimizing the solution conditions and introducing gold clusters reduces the overall mass resolution for the three largest cationic peptides ((A1, A2, NT) from Δm = 84.2 ± 9.9 Da to Δm = 37.8 ± 2.8 Da; see Table S3 in Supporting Information) more than 2-fold improvement over the open pore case. See the caption of Table S2 in Supporting Information for a complete description of Δm.
CONCLUSION
SMNS analysis requires increasing the analyte residence time in the pore while decreasing the current blockade fluctuations. This leads to a narrowing of the peaks in the current blockade distribution. The ultimate goal being baseline-resolved peaks so that one can say for certain that a particular current blockade corresponds to a particular peptide. As has been demonstrated here and elsewhere, the addition of the anionic gold cluster serves to increase the analyte’s time in the pore. For the case of PEG detection, this was shown to increase the quality of the corresponding current blockade distributions. Advancing SMNS detection from PEG to more biologically relevant molecules such as small peptides requires further analysis of the magnitude of the fluctuations within each blockade. The origin of these fluctuations is multifold, but one part that plays an important role is the degree of secondary structure within the peptides. The results shown in Figures 5 and 6 show that manipulating the solution conditions can lead to a significant reduction of the blockade fluctuations, which in turn can reduce the width of the corresponding peaks in the blockade distributions as demonstrated in Figure 7.
In this article we report several important results. For the molecules studied herein, it appears that a connection can be made between the depth of the current-blockade and the corresponding molecular weight, and this connection can be understood with a straightforward linear-ohmic model. Second, the presence of an anionic, thiolate-capped gold cluster increases cationic peptide residence times nearly 3-fold while increasing the peptide on-rate nearly 2-fold. Finally, by modifying the solution conditions and removing the stable secondary structure of a peptide one can achieve significant reduction of the current blockade fluctuations. This in turn yields a more accurate estimate of a given peptide’s current blockade and a better means for characterizing peptides via the SMNS technique.
Supplementary Material
ACKNOWLEDGMENTS
J.E.R. acknowledges funding from the Thomas F. and Kate Miller Jeffress Memorial Trust, VCU start-up funding and a generous equipment donation from NIST in Gaithersburg, MD. N.K. and A.D. were supported by NSF Grant no. 1255519.
ABBREVIATIONS
- A1
angiotensin I
- A2
angiotensin II
- NT
neurotensin
- LE
leuenkephalin polyglutamine-binding peptide 1 (QBP1)
- SMNS
single molecule nanopore spectrometry
- PEG
polyethylene glycol
- αHL
alpha hemolysin
Footnotes
Supporting Information
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssensors.7b00362.
Description of the five peptides used; derivation of the Ohmic model; a description of the method used to calculate the effective charge of the peptides; calculated effective charge of the peptides; residence time enhancement; representative current traces; alpha hemolysin I–V curves; supplemental data (PDF)
The authors declare no competing financial interest.
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