Evaluating protocols and analytical methods for peptide adsorption experiments
© Fears et al.; licensee Springer. 2013
Received: 12 June 2013
Accepted: 17 July 2013
Published: 19 August 2013
This paper evaluates analytical techniques that are relevant for performing reliable quantitative analysis of peptide adsorption on surfaces. Two salient problems are addressed: determining the solution concentrations of model GG–X–GG, X5, and X10 oligopeptides (G = glycine, X = a natural amino acid), and quantitative analysis of these peptides following adsorption on surfaces. To establish a uniform methodology for measuring peptide concentrations in water across the entire GG–X–GG and X n series, three methods were assessed: UV spectroscopy of peptides having a C-terminal tyrosine, the bicinchoninic acid (BCA) protein assay, and amino acid (AA) analysis. Due to shortcomings or caveats associated with each of the different methods, none were effective at measuring concentrations across the entire range of representative model peptides. In general, reliable measurements were within 30% of the nominal concentration based on the weight of as-received lyophilized peptide. In quantitative analysis of model peptides adsorbed on surfaces, X-ray photoelectron spectroscopy (XPS) data for a series of lysine-based peptides (GGKGG, K5, and K10) on Au substrates, and for controls incubated in buffer in the absence of peptides, suggested a significant presence of aliphatic carbon species. Detailed analysis indicated that this carbonaceous contamination adsorbed from the atmosphere after the peptide deposition. The inferred adventitious nature of the observed aliphatic carbon was supported by control experiments in which substrates were sputter-cleaned by Ar+ ions under ultra-high vacuum (UHV) then re-exposed to ambient air. In contrast to carbon contamination, no adventitious nitrogen species were detected on the controls; therefore, the relative surface densities of irreversibly-adsorbed peptides were calculated by normalizing the N/Au ratios by the average number of nitrogen atoms per residue.
The interactions between biomolecules and surfaces play a crucial role in many technological processes and devices, such as molecular separation, biofouling, biosensors, biomedical implants, and biomimetic materials [1–6]. To begin understanding the processes by which proteins adsorb to surfaces, small peptides have been used as model systems because they avoid many challenges associated with the complexity inherent in natural proteins [7–10]. In particular, model peptides can be designed to isolate molecular interactions involving an individual amino acid residue, a peptide sequence, or a structural subunit of interest [11–15].
An example of a system designed to investigate surface interactions of an individual amino acid residue are the model oligopeptides examined in this work: they are representative of (or related to) the general GG–X–GG and X n (n = 5 or 10) series, where G is glycine and X is one of the naturally occurring amino acids. The diversity of physicochemical properties of peptides across such a series produces several important analytical considerations that must be carefully addressed before a meaningful quantitative comparison of peptide adsorption on surfaces can be carried out.
1.1 Solution analysis
We first considered issues that are particularly easy to overlook while planning a systematic survey of peptide-surface interactions: the peptides in a model series should have similar solubility in a common solvent and should be stable in that solvent against aggregation. A related consideration in systematic quantitative measurements is reliably establishing the concentration of different peptides across the entire model series, as methods for determining peptide concentration can depend on the size, structure, and residue composition of the peptides. Therefore, for our model peptides, we calculated concentration values from dry weight of dissolved peptides, which is by far the most commonly used method for calculating peptide concentrations, and systematically evaluated the performance of three common techniques for measuring peptide concentrations: (1) UV spectroscopy of peptides having a C-terminal tyrosine [16, 17], (2) the bicinchoninic acid (BCA) protein assay [17–19], and (3) amino acid (AA) analysis .
Incorporating tyrosine, tryptophan, or cysteine residues, which exhibit absorbance bands at 280 nm, can be used to determine the peptide concentration based on the assumption that a molecule’s extinction coefficient is a linear combination of its chromophores’ coefficients . More generally, UV spectroscopy provides relative concentrations of proteins or peptides based on a strong absorbance band at 195 nm; however, that band can contain contributions from both the peptide backbone and certain side chains, most notably aromatic side chains [17, 22]. Thus, the extinction coefficient at 195 nm must be independently verified and calibrated for each peptide sequence.
The BCA protein assay [17–19], which relies on the chelation and subsequent reduction of Cu2+ ions by the amide backbone of polypeptide chains, has been used extensively to quantify proteins in solution, whereby the unknown protein concentration is calculated based on comparison to a calibration curve for a standard solution of bovine serum albumin (BSA). The BCA assay has been reported as both successful [12, 23, 24] and potentially problematic  for peptide sequences similar to ours, thus requiring us to evaluate the effectiveness of BCA measurements for GG–X–GG and X n model peptides.
For selected model peptides, we examined the applicability of AA analysis , which relies on acid hydrolysis to cleave the peptide bonds and yield the individual amino acids; however, cysteine and tryptophan side chains degrade during acid hydrolysis, limiting the generality of this method.
1.2 Surface analysis
Having established reliable protocols for preparing and characterizing peptide solutions, our next task is to characterize the adsorption of peptides on surfaces. Fluorescent [25, 26] and radioactive [27, 28] labels have been used to quantify surface-adsorbed peptides. However, the hazards and costs preclude radiolabeling in broad surveys like ours, while fluorescence quenching renders fluorescent labels impractical for quantitative analysis on important model surfaces, such as gold and other coinage metals. Label-free techniques, such as ellipsometry, surface plasmon resonance (SPR) spectroscopy, and quartz crystal microbalance (QCM) measurements, can quantify adsorbates on a surface [28–31]. Unfortunately, these techniques cannot distinguish between adsorbed peptides and, for example, adventitious contaminants. The effect of adventitious hydrocarbons on the adsorption of small peptides has not been carefully examined, but displacement or masking clearly cannot be assumed a priori, as is done for high-molecular-weight proteins. In principle, depositing and measuring peptides in situ can eliminate hydrocarbon adsorption. Rigorously cleaning the surfaces in situ before peptide deposition, however, is not trivial and may merely replace hydrocarbons by other surfactants.
UV spectroscopy has been used as a label-free technique to quantify protein adsorption [32, 33]; however, the selection of suitably transparent substrates for UV spectroscopy is limited. X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) are label-free surface analysis techniques that have been increasingly used for analysis of biomolecules irreversibly adsorbed on surfaces [27, 34, 35]. Neither technique suffers from the substrate limitations of UV spectroscopy; XPS particularly excels at quantifying adsorbates because it relies on ratios of adsorbate to substrate signals  rather than on reference adsorbate peaks, which can be affected by matrix effects and surface contaminants in ToF-SIMS measurements.
We addressed analytical considerations outlined above for both solution and surface characterization of model peptides. We examined protocols for determining the solution concentrations of model peptides before surface adsorption, then proceeded to establish protocols for the quantification of model peptides after surface adsorption on inorganic substrates using XPS. Our overall objective was to develop and validate a set of practical methods and protocols that would be generally applicable to surface analysis of model peptides and similarly biofunctionalized surfaces.
2.1 Concentration measurements
The peptides used in this study were synthesized and purified (>98%) by GenScript USA Inc. We chose to have the N- and C-termini of the peptides acetylated (–COCH3) and amidated (–NH2) to eliminate possible effects of free amino and carboxylic acid groups, respectively, on the conformation , and, potentially, adsorption behavior of the peptides. All buffer reagents were used as received from Sigma-Aldrich Co. Stock solutions of the peptides were prepared using a 10 mM phosphate buffer, adjusted to a pH of 7.0 by mixing the appropriate amounts of NaH2PO4 and Na2HPO4 solutions. The nominal peptide concentration based on the dry weight of lyophilized peptide added to buffer was recorded for comparison with other concentration measurement techniques.
2.1.1 Bicinchoninic acid assay
We measured the concentrations of the peptide stock solutions using a Pierce® BCA protein assay kit (Thermo Scientific) in accordance with the standard protocol. Peptides containing cysteine, tryptophan, or tyrosine residues were excluded from this analysis because these residues interfere with BCA assay . Samples were prepared in aqueous solutions then mixed at a ratio of 1:8 with the working reagent in a 96-well plate and incubated at 37°C for 30 minutes. The well plates were allowed to cool to room temperature before measurements at 562 nm on a Safire multi-detection monochromator-based plate reader (Tecan). A calibration curve for the assay was constructed using serial dilutions (0–2,000 μg/mL) of a BSA standard.
2.1.2 UV absorbance
where ϵ i is the molar extinction coefficient at 280 nm (M-1 cm-1), L is the optical path length (cm), and C is the peptide concentration (M). This calculation is based on the assumption that only tyrosine or tryptophan side chains contribute to the absorbance peak at 280 nm. Therefore, the molar extinction coefficient of a peptide that contains a single tyrosine or tryptophan residue is 1280 M-1 cm-1 or 5690 M-1 cm-1, respectively .
2.1.3 Amino acid analysis
AA analysis was performed by the Protein Chemistry Laboratory at Texas A&M University according to the following protocol. Aliquots of each peptide stock solution, except sequences containing cysteine or tryptophan, and the BSA control were mixed with two internal standards, norvaline and sarcosine, pipetted into borosilicate glass culture tubes in triplicate and dried under vacuum to yield approximately 20 μg of BSA or 10 μg of peptide in each tube. The samples were then subjected to acid hydrolysis by 6 N aqueous HCl at 150°C for 1.5 hours under argon and in the presence of phenol to limit halogenation of tyrosine residues. The hydrolyzed samples were resuspended in a 0.4 N borate buffer (pH 10) and transferred into an AminoQuant autosampler for automated derivatization by o-phthalaldehyde (OPA) and 9-fluoromethyl-chloroformate (FMOC). Following derivatization, the residues were separated by reversed-phase HPLC, and detected and quantitated by UV absorbance or fluorometry.
2.2 Circular dichroism
where MW is the molecular weight of the peptide (g/mol), C is the peptide concentration (g/mL), L is the optical path length through buffer (cm), and N is the number of AA residues.
2.3 Surface analysis
2.3.1 Substrate preparation
We used the following substrates for peptide adsorption experiments:  silicon wafers (University Wafers), gold-coated (100 nm of Au over 5 nm of Ti) silicon wafers (Platypus Technologies), fused-quartz plates (Chemglass), and 10-mm diameter calcium fluoride windows (ISP Optics). The wafers and plates were cut into smaller pieces (ca. 1.0 cm2) prior to the cleaning process. The CaF2 windows were cleaned by sonicating them for 5 minutes in an aqueous 0.005% (v/v) Triton® X-100 solution followed by 98 wt.% H2SO4. We sonicated all other substrates for 5 min in the following sequence of solutions: 0.005% (v/v) Triton® X-100, “piranha” wash (7:3 H2SO4 (98 wt.%)/H2O2 (30 wt.%)), and RCA standard clean 1 (1:1:5 NH4OH (28.0–30.0% NH3 basis)/H2O2 (30 wt.%)/H2O). (Caution: piranha solution is extremely oxidizing, reacts violently with organics, and should only be stored in loosely tightened containers to avoid pressure buildup). The substrates were thoroughly rinsed with 18 MΩ-cm deionized (DI) water after each sonication step and blown dry with nitrogen gas at the completion of the cleaning process. All substrates were cleaned immediately before the adsorption experiments to limit their exposure to contaminants in the atmosphere.
2.3.2 X-ray photoelectron spectroscopy
We assessed the purity of peptide stock solutions by pipetting 3 μL drops onto warmed (ca. 75°C) Au substrates to form thick peptide films. XPS data were acquired at room temperature in an ultra-high vacuum analysis chamber with the base pressure <5 × 10−9 mbar using a commercial XPS instrument equipped with a monochromated Al Kα X-ray source. These reference films were analyzed as-prepared and after being sputtered for 15 sec with low-energy (200 eV) Ar+ ions to remove surface contaminants and reveal the bulk composition of the reference peptide films. Spatially-uniform charge neutralization across these samples was provided by beams of low-energy (≤10 eV) electrons and Ar+ ions. We acquired high-resolution spectra of the Au 4f, C 1s, N 1s, O 1s, and S 2p regions to determine the elemental and chemical composition of the thick peptide films.
For surface adsorption experiments, clean substrates were placed in pure buffer solution in glass vials, the appropriate amount of peptide stock solution then was added to produce an incubation solution of 1.0 mM for pentapeptides and 0.5 mM for decapeptides, corresponding to approximately constant mass concentration. We incubated the substrates at room temperature for 24 h to allow the surface-adsorbed peptides to approach a quasi-equilibrium density. After the incubation period, we filled the incubation well to overflowing with DI water—in effect, infinitely diluting the peptide solutions to prevent the adhesion of any molecules that may have built up at the air-water interface as the substrates pass through that interface. The substrates were rinsed again with DI water after removal from the incubation well and dried under a stream of nitrogen. We collected normal-emission survey and high-resolution spectra for the applicable substrate peaks (Au 4f, Si 2p, Ca 2p, and F 1s) and the peaks corresponding to elements present in the peptides (C 1s, N 1s, and O 1s).
The microfocused X-ray source illuminated a spot of ca. 400 × 600 μm2 on the sample; at least three separate spots were analyzed for each sample. Survey spectra were acquired with 1 eV step size at ca. 1.7 eV resolution; high-resolution spectra were acquired with 0.15 eV step size at ca. 0.5 eV resolution. The energy of the monochromated Al Kα X-ray source was regularly calibrated and maintained at 1486.6 ± 0.2 eV. The binding energy (BE) scale of the spectrometer was regularly calibrated based on an automated procedure to produce the Au 4f7/2, Cu 2p3/2, and Ag 3d5/2 peaks within <0.05 eV from the standard reference BE values . The aliphatic C 1s peak was observed at BE = 284.5 ± 0.2 eV for peptide samples. Peak fitting for high-resolution spectra was performed in Unifit (Version 2011), using a convolution of Lorentzian and Gaussian line shapes to fit the individual components . The standard “atomic %” elemental compositions were quantified using calibrated analyzer transmission functions, Scofield sensitivity factors , and effective attenuation lengths (EALs) that were calculated using the standard TPP-2M formalism for photoelectrons [36, 41].
2.3.3 Statistical analysis
We report all calculated values in terms of their mean plus or minus 95% confidence interval (C.I.) for n ≥ 3. We used Student’s unpaired t-test (p ≤ 0.05) to determine if mean values were statistically different between datasets.
Percent differences reported in comparative concentration analyses were calculated by normalizing the absolute value of the difference between two concentration measurements by the average of those concentrations. This choice of difference normalization is appropriate for widely divergent pairs of values: presumptively selecting one of the concentration measurements as being the more accurate of the two can systematically bias the comparison.
3 Results and discussion
The main objective of this study was to develop and validate a combination of preparation protocols and analytical methods for performing reliable, systematic, and quantitative measurements of surface adsorption for a series of model peptides. The work involved three major components: characterization of peptide solutions prepared for deposition experiments, investigation of sample contamination sources, and detailed XPS analysis of peptides adsorbed on inorganic substrates.
3.1 Characterization of peptide deposition solutions
Reliable quantitative measurements of peptide adsorption must begin with validated protocols for preparation and characterization of the deposition solutions. Solution concentration is a key parameter in surface adsorption experiments as it can affect the adsorption kinetics, surface saturation, and conformation of adsorbed peptides. The most common method of calculating the concentration of peptides or proteins is based on the dry weight of lyophilized samples. For our repetitive short sequences, the degree of uncertainty produced by the potential presence of associated water or solvent molecules in lyophilized powders was not clear a priori, therefore, we performed a comparative analysis of three common methods for measuring the concentration of peptides in solution. Complementary CD measurements substantiated the interpretation of concentration measurements and confirmed the desirable absence of strong secondary structure or aggregation tendencies for GG–X–GG peptides in deposition solutions.
3.1.1 BCA assay
Percent differences between peptide concentrations determined by BCA assay and their nominal concentrations based on dry weight a
% Diff. WCb
% Diff. WCb
% Diff. WCb
17 ± 2
28 ± 9
20 ± 2
94 ± 34
Aspartic Acid (D)
20 ± 5
157 ± 36
169 ± 24
Glutamic Acid (E)
6 ± 3
128 ± 22
178 ± 5
13 ± 6
87 ± 26
101 ± 31
167 ± 34
193 ± 6
21 ± 9
105 ± 43
111 ± 41
26 ± 16
66 ± 25
5 ± 5
3.1.2 BCA and PPII structure
The notable inability of the BCA assay to assess the concentration of the homogenous penta- and decapeptides, X5 and X10, (Table 1) warranted further investigation. In particular, P5, which forms a PPII helix, was virtually undetected by the BCA assay; coupled with the better quantification of the unstructured GG–X–GG peptides, this result suggests a possible interference from peptide secondary structure in general or specifically from the PPII structure.
The specific role of the PPII rather than that of any secondary structure was revealed from comparison of S5 and S10 peptides; concentration of S5 was grossly underestimated by BCA, whereas there was only a 5% difference between the BCA and dry-weight concentrations for S10. We observed that the CD spectrum of S10 was characteristic of a β sheet, with a maximum at 195 nm and a single minimum at 221 nm, close to the values of 197 and 222 nm, respectively, reported by Urry et al. for poly-L-serine in water . This observation indicates that, as the length of the homo-oligoserine increases from S5 to S10, the peptide begins to behave like poly-L-serine, which has been shown to form antiparallel β sheets in aqueous solution . These results suggest that PPII helices in particular, rather than all structured conformations, pose a problem for the BCA assay.
3.1.3 Intrinsic chromophores
Comparison of different methods for measuring the solution concentration in 10 mM sodium phosphate buffer (pH 7) of peptides containing chromophoric residues
UV abs. (280 nm)
AA Analysis (mean ± 95% C.I., n = 3)
% Diff. WCa
% Diff. WCa
% Diff. UVb
1.31 ± 0.07
16 ± 6
4 ± 3
0.96 ± 0.04
9 ± 4
2 ± 1
1.04 ± 0.04
2 ± 3
7 ± 3
0.97 ± 0.11
15 ± 10
27 ± 9
We examined GG–X–GGY and X5Y (X = G, A, or P) model peptides that included a C-terminal tyrosine residue as a chromophore. Although G5 and A5 were soluble at 1.0 mM concentration in the aqueous stock solutions, G5Y and A5Y precipitated out of solution already at a much lower concentration (0.1 mM). Aggregation caused by the appended tyrosine may explain these observations, as Measley et al. reported that the addition of a C-terminal tyrosine promotes self-aggregation in alanine-based octamers . Similarly, we observed that GGYGG was water-soluble but GGGGGY formed aggregates, suggesting that the location of the tyrosine residue rather than its mere presence is responsible for the aggregation of peptides labeled with a C-terminal tyrosine.
For GGAGGY and GGPGGY, we found a strong correlation between the concentrations measured by weight, UV absorbance, and AA analysis, with values that were less than 10% different from one another (Table 2). However, we found larger discrepancies in the concentrations of PPPPPY measured by different techniques, particularly for UV spectroscopy versus AA analysis. With the exclusion of proline-based peptides, all concentrations determined by UV spectroscopy and AA analysis were either statistically equal to or less than the nominal weight-based concentration.
3.1.4 Tyrosine chromophore and local structure
3.2 Effects of surface contamination
Uncontrolled contamination can easily invalidate surface adsorption experiments. Therefore, we looked for evidence of contaminants (e.g., post-synthetic fragments) present in the stock solutions of peptides or competing with peptides during the adsorption experiments. We also investigated the effects produced by post-deposition adventitious contamination on the XPS signatures of surface-adsorbed peptides, as such post-deposition contamination may compromise the interpretation of the XPS data, even if the underlying adsorption experiments were not affected.
3.2.1 Characteristic C 1s components
To investigate whether the aliphatic carbon contamination came from the peptide solution or from adventitious carbon in the atmosphere, we formed a thick peptide film on an Au substrate by drying a drop of the K5 stock solution. The components of the carbon region that correspond to C–O or C–N groups (C2, BE = 285.8 eV), C=O groups (C3, BE = 287.5 eV), and O–C=O or N–C=O groups (C4, BE = 289.1 eV)—that is, carbon species arising from the deposited peptide—are clearly evident in the spectrum of the dried drop (Figure 3b) [53–56]. Although the characteristic peptide peaks in the C 1s region were more prominent than in the adsorbed K5 layer (Figure 3a), the C/N ratio of the film was 4.0 ± 0.2—that is, above the theoretical value (based on the elemental composition of K5 peptide), but 20% lower than for the adsorbed of K5 layer. The decrease in C/N ratio for a thick film of the dried peptide solution suggests that the observed contamination originates at the surface rather than in the stock solution.
To confirm this inference, we removed the uppermost ca. 0.5 nm of the thick peptide film by sputtering with a low-energy Ar+ beam , and re-analyzed the same spots. The C/N ratio was further reduced to 3.5 ± 0.1 and the C1 component was no longer the predominant component in the spectrum (Figure 3c). The elevated ratio, with respect to the theoretical value, indicates that some non-aliphatic contaminants may remain. However, this analysis of the peptide film supports our inference that the contaminants adsorb primarily from atmosphere. The low energy (200 eV) and short duration (15 sec) of the Ar+ sputtering would have minimized the damage to the chemistry of the peptide film , although details such as the ratio of C2 and C3 components may have been affected, so the spectral envelope in Figure 3c may not be fully characteristic of “bulk” K5 peptide. The potential sputtering damage, however, would not have affected the inference about removing an adventitious carbon overlayer, because the typical expected Ar+ damage would have increased the C1 component at the expense of the higher-BE components. Finally, we note that this approach works well for freshly prepared samples, but becomes much less effective for samples that have been stored for several days, suggesting that some intermixing of the contaminants into the dried peptide film occurs over time.
3.2.2 Quantifying adventitious contamination
Atomic percentages of elements on surface contamination control samples a
60.0 ± 2.3
29.7 ± 2.9
10.3 ± 0.8
Sputtered in UHV
Ambient (1 s)
84.7 ± 2.6
15.3 ± 3.1
Ambient (3 min)
68.3 ± 2.1
27.1 ± 2.5
4.7 ± 1.0
3.3 Quantification of adsorbed peptides
In the N 1s spectra, the main component (N1) is centered at BE = 400.1 eV, corresponding to the amide groups in the peptide backbones and amine groups on the side chains [55, 57–59]. The shift to a lower binding energy of the small component (N2; BE ≈ 398.8 eV) is consistent with amide groups that are strongly interacting with inorganic substrates [57, 60]. K5 and K10 also exhibited a peak (N3) at BE ≈ 402 eV indicating the presence of protonated amine groups [57–59, 61], whereas the single amine group in GGKGG appears to be uncharged after surface adsorption. As there was no adventitious nitrogen detected on the surface of the blank-buffer control, the atomic percentage of nitrogen was used to quantify the surface densities of adsorbed peptides.
3.3.1 Molecular surface densities
This N/Au normalization (where “# of N atoms” and “# of residues” are the numbers of N atoms and residues, respectively, in each peptide) produces an estimate of the surface density of peptide residues. The “atomic %” values used in Eq. (3) are normalized to account for the differences in analyzer transmission function, Scofield sensitivity factors, and EALs between the Au 4f and N 1s photoelectrons. The respective EAL values have not been calculated assuming a specific overlayer structure model; however, they provide an approximate correction for the energy-dependent difference of the sampling depths between Au 4f and N 1s photoelectrons.
Elemental ratios of lysine peptides adsorbed on Au a
Norm. N/Au ratio
0.051 ± 0.005
9.1 ± 0.5
3.6 ± 0.07
0.041 ± 0.004
7.6 ± 0.9
1.7 ± 0.3
0.067 ± 0.003
5.2 ± 0.4
1.5 ± 0.2
The peptide-specific spectral components in Figure 5 reflect the quantitative results in Figure 6 and Table 4. The K10 spectra clearly show the highest intensity of N 1s and peptide-specific C2–C4 components. The C2–C4 components are slightly decreased from GGKGG to K5, reflecting the lower peptide coverage. The N 1s components are noticeably stronger for K5 than for GGKGG, corresponding to nearly double the number of N atoms in each molecule of the former.
3.3.2 Sample stoichiometry
Quantifying the sample stoichiometry is complicated by the presence of coadsorbates, which is most clearly revealed by the elevated C/N and O/N ratios (Table 4). While the excess oxygen is likely from physically adsorbed water molecules, the excess carbon clearly indicates that, despite our efforts to limit the exposure of the surfaces to air, carbonaceous coadsorbates or contaminants are still present.
In contrast, the atomic % of Au remained fairly constant when the peptides were adsorbed to clean Au substrates (Figure 7, bottom), despite the surface density of peptide residues being much higher for K10 than for GGKGG or K5. This observation suggests that in our standard experiments the peptides do not adsorb onto a layer of surface contaminants. Instead, the peptides appear to either displace surface contaminants that are present before peptide adsorption, or, alternatively, the peptides leave unoccupied surface sites where the contaminants adsorb after peptide adsorption, when the samples are exposed to air; the trend in intensity of the C1 component in Figure 5 could be rationalized in terms of either interpretation. We believe that the contamination analysis shows that despite the detected coadsorbates or contaminants, their presence minimally influences peptide adsorption quantified in our experiments, and thus our protocols are suitable for evaluating the differential irreversible adsorption of model peptides on Au surfaces.
3.3.3 Photoelectron attenuation
The minimal variation of the apparent attenuation of the gold substrate XPS signal (orange bars in Figure 7, bottom) among the peptide samples deposited using our standard protocol indicates that the photoelectron attenuation is produced by a combination of the peptide and adventitious adsorbates. While the relative amounts of these two overlayer components change as the function of the amount of adsorbed peptides, the total effective overlayer thickness is similar in all cases. Accordingly, the simple normalized N/Au at. % ratios of Eq. (3) should provide a reliable quantitative measure of the variation in the amount of the adsorbed peptides (because the Au signal provides an approximately constant internal reference). Conversely, developing and validating a rigorous model that accounts for the signal attenuation across a wide range of adsorbed peptide samples would be very challenging, as such a model would need to include information about the overlayer structure, e.g., the relative distribution of the peptide and adventitious components in each case.
3.3.4 Effect of peptide concentration
To test whether uncertainty in the nominal concentration of the peptide solution (as determined from the weight of dry powder) significantly affects the observed surface densities of the adsorbed peptides, we incubated clean substrates in peptide solutions diluted tenfold (0.1 mM or 0.05 mM) with respect to the standard deposition solutions used in the experiments described above. We observed no statistical difference in the surface density between the series of peptides adsorbed at the standard or diluted concentration (Figure 6). As we previously discussed, the nominal concentration by weight is within 30% of the reliable solution concentration values determined by different methods; thus, we believe that the nominal solution concentration based on the dry weight of peptide is sufficiently accurate for our experiments. While we observed no difference in surface densities over a tenfold concentration range in the case of the lysine peptides, we note that other peptide systems could be more sensitive to the incubation concentration with respect to surface adsorption.
3.3.5 Peptide adsorption on different substrates
We evaluated methods for quantifying model oligopeptides both in solution and after their adsorption onto inorganic substrates.
Each of the three methods that we investigated to determine peptide concentration in solution proved to have shortcomings. We found good agreement between UV spectroscopy and AA analysis, but the addition of a C-terminal tyrosine for UV measurements resulted in solubility problems and structural changes for many of our small model peptides. The structure of some of the model peptides apparently affected the BCA assay. In general, the nominal concentration based on the weight of as-received lyophilized peptide was within 30% of reliable measurements by the other methods; we hypothesize that the possible presence of water and/or TFA in lyophilized samples is the main source of this uncertainty of weight-based concentration values. In a separate test, we observed no statistically significant differences between the surface densities of our model peptides adsorbed from solutions differing tenfold in peptide concentration. We therefore conclude that determining peptide concentrations from the dry weight of the dissolved powder is sufficiently accurate for carrying out systematic adsorption experiments with our model peptides. The estimated uncertainty of 30% is also likely to be more generally applicable to the determination of peptide concentrations based on their dry weight; whether this level of uncertainty in peptide concentrations significantly affects surface adsorption should be determined for each specific peptide-surface system.
XPS proved useful for quantifying the amount of the model peptides that were irreversibly adsorbed onto a surface. Hydrocarbon contaminants were present on the peptide-coated substrates, but Ar+ sputtering showed that the bulk of the contaminants adsorb from the atmosphere rather than from the stock solutions of purified peptides. In contrast to the carbon contamination, we found that the substrates examined do not become contaminated with any nitrogen-containing species. This observation enabled us to calculate the relative surface densities of different peptides on the same surface; however, we refrained from quantitatively comparing the relative surface densities across substrates due to differences in the quantification of the reference substrate peaks. The similarity of the peptide peaks on different surfaces indicates that XPS is a viable method for measuring differential adsorption of peptides on a wide selection of substrates .
Finally, we found that not only were surface contaminants present on all substrates, but that the amounts of these contaminants varied. Just a few minutes of exposure to ambient air can produce a significant amount of adventitious contamination; therefore, completely avoiding contamination is likely impractical, if not impossible, in experiments involving peptide adsorption from solutions. Fortunately, we determined that XPS is useful for distinguishing between the adsorbed peptides and carbonaceous contaminants on samples prepared following practical protocols; in contrast, techniques that measure molecular adsorption non-specifically (e.g., SPR and QCM) may prove inadequate in distinguishing between peptide and non-peptide molecules (e.g., contaminants, detergents, or coadsorbates). We stress that a detailed analysis of the characteristic spectral components is necessary to verify specific peptide deposition and to understand potential sources and effects of contamination; relying only on total elemental atomic % values can lead to both false positive and false negative interpretations of the XPS data for low-coverage peptide films.
5 Authors’ information
KPF and DYP are AVS members.
Support for this work was provided by the Office of Naval Research (ONR) and the Air Force Office of Scientific Research (AFOSR). KPF thanks the National Research Council for a Postdoctoral Fellowship at NRL during the initial stages of this project. The authors also thank Dr. George P. Anderson (NRL) for the use of Jasco J-815 spectrometer. DYP thanks Dr. Robert Willett (Bell Labs) for useful discussions and for peptide samples provided for initial comparative analysis.
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