PADLOC: A Powerful Tool to Assign Disulfide Bond Connectivities in

Nov 29, 2011 - Most likely, inherent flexibility of disulfide bridges and spectral crowding, which disable straightforward detection of distance and a...
6 downloads 0 Views 1MB Size
Download PDF
Article pubs.acs.org/ac

PADLOC: A Powerful Tool to Assign Disulfide Bond Connectivities in Peptides and Proteins by NMR Spectroscopy Leszek Poppe,* John O. Hui, Joseph Ligutti, Justin K. Murray, and Paul D. Schnier Chemistry Research & Discovery and Protein Science, Amgen Inc., Thousand Oaks, California 91320, United States S Supporting Information *

ABSTRACT: The determination of the disulfide bond connectivity in a peptide or protein represents a significant challenge. It is notoriously difficult to use NMR spectroscopy to assign disulfide connectivities because NMR spectra lack direct evidence for disulfide bonds. These bonds are typically inferred from three-dimensional structure calculations, which can result in ambiguous disulfide assignment. Here, we present a new NMR based methodology, in which the disulfide connectivity is obtained by applying Bayesian rules of inference to the local topology of cysteine residues. We illustrate how this approach successfully predicts the disulfide connectivity in proteins for which crystal structures are available in the protein data bank (PDB). We also demonstrate how this methodology is used with experimental NMR data for peptides with complex disulfide topologies, including hepcidin, Kalata-B1, and μ-Conotoxin KIIIA. In the case of μ-Conotoxin KIIIA, the PADLOC connectivity (1−15,2−9,4−16) differs from previously published results; additional evidence is presented demonstrating unequivocally that this newly proposed connectivity is correct.

A

constraints,4 which are difficult to observe for small peptides. As we demonstrate here, many NMR structures deposited into the PDB database have resolutions insufficient for the unambiguous assignment of disulfide bonds. Alternatively, the intercysteine NOEs alone, recognized as being strongly indicative of the existence of disulfide bond,5 lose their diagnostic power in closely packed disulfide networks, where the cysteine proximity does not necessarily imply disulfide connectivity. The substitution of sulfur atoms by 77Se,6 as recently demonstrated for a synthetic spider toxin,7 leads directly to diselenide through-bond connectivities. However, this methodology is still far from being generally applicable. Here, we propose a new methodology where the disulfide bonds are inferred not from the NMR structure calculation but directly from the topology of cysteine residues, represented here by the NOE pattern and vicinal J-couplings. Contrary to common perception,8 we demonstrate that the NOE distance information between cysteine residues when combined with information about cysteine side chain conformations is sufficient to resolve the disulfide connectivity of a peptide or protein. This pattern of disulfides from the local constraints (PADLOC) is based on Bayesian probability calculus and current knowledge about disulfide bond architecture derived from high resolution X-ray structures deposited into the PDB database. Early work in this field also utilized the PDB database knowledge in NMR structure calculations; however, only the intercysteine distance information was taken into consider-

ssigning disulfide connectivities for peptides with extensive disulfide networks is a challenging task that often yields ambiguous results. One of the most commonly used analytical methods for disulfide mapping is partial reductive alkylation coupled with mass spectrometric detection. The resilience of disulfide rich peptides often makes this type of analysis extraordinarily complex and tedious.1 Moreover, the chemical methodology relies on breaking disulfide bonds and is prone to thiol-disulfide exchange, in which case the original connectivity can be lost in the course of an experiment. Recently, tandem mass spectrometric methods based on collision induced dissociation2 and ultraviolet dissociation3 have been proposed for disulfide mapping; however, gas-phase scrambling during the activation process is still potentially an issue obfuscating analysis. Therefore, it is desirable to have the ability to determine the disulfide connectivity from an intact sample, through the determination of three-dimensional structure, either by X-ray crystallography or NMR spectroscopy. Since the former requires suitable crystals, the latter often remains the method of choice; for molecules with molecular weights less than 5 kDa, approximately 80% of the structures in the PDB were determined by NMR. However, because it is not possible to detect through bond scalar coupling between two sulfur atoms by NMR, disulfide bonds cannot be directly assigned from NMR data. Instead, the disulfide pattern is usually inferred from the three-dimensional NMR structure, which is mainly derived from a set of interproton distance constraints obtained from the NOE data.4 A major drawback of this approach is that it does not necessarily lead to a unique solution, i.e., the same protein fold may be consistent with different disulfide patterns. The most sophisticated methodologies rely on a large number of long-range distance © 2011 American Chemical Society

Received: September 21, 2011 Accepted: November 29, 2011 Published: November 29, 2011 262

dx.doi.org/10.1021/ac203078x | Anal. Chem. 2012, 84, 262−266

Analytical Chemistry

Article

ation.5 The importance of cysteine side chain angles was clearly appreciated in the NMR analysis of knotted cyclotide frameworks,9 facilitating the assignment of the disulfide bonds and helping to resolve the controversy around the disulfide pattern of this peptide family, before the X-ray structure became available.10 The Bayesian approach, although not in a general form as presented here, was used in our previous work to resolve the controversy around the disulfide connectivity in the small peptide hormone hepcidin.11 PADLOC incorporates all these advancements into a robust tool which is capable of resolving the disulfide connectivity of crowded disulfide networks without the need for a highly accurate 3D model of the intact protein. This method utilizes particular advantages of NMR spectroscopy when applied toward the local vs the global structure determination.

maximum intercysteine sequence separation in the protein under investigation. The Bayesian probability for each disulfide pattern (Pk) in the network of 2N cysteines with N disulfide bonds can be expressed in the form of eqs 1−3:12,13

p(Pk|data, S) =

p(Pk|S) × p(data|Pk , S) N

N ) ! /(2 ∑(2 k

p(data|Pk , S) =

× N !)

p(Pk|S) × p(data|Pk , S)

Π

(1)

p(NOEij|dij)

i , j ∈ C(2N ,2)

× p(χiχj|Ci 9 kCj)

■

× p(dij|Ci 9 kCj , χiχj)

EXPERIMENTAL SECTION The PADLOC Model. A unique arrangement of two cysteine residues Cysi-Cysj in three-dimensional space can be described as a combination of side chain conformations χi and χj and intercysteine distances between three protons: Hα, HβR, HβS. Although up to four interproton distances, observed in the X-ray structures, can potentially result in observable NOEs, we considered only the shortest distance. This choice of database sampling is dictated by the practical aspects of NOE detection, where spin diffusion and/or conformational averaging may complicate accurate assignment of more than one direct interproton contact. This simple classification of the local disulfide constraints allows the projection of the entire database knowledge into an array, P. This array was built by sampling 17 833 disulfide bonds in 5 046 protein X-ray structures deposited into the PDB database with resolutions ≤2.5 Å, chosen as the best compromise between high structure quality and the number of protein structures available. The cysteine conformations (dihedral angle χ defined by S−Cβ−Cα−N atoms) were assigned into three groups: t with χ = 180° ± 60°, g+ with χ = 60° ± 60°, and g− with χ = −60° ± 60°. The minimum interproton distances between two cysteine residues were assigned into four groups: strong, dmin < 3 Å; medium, 3 Å < dmin < 4 Å; weak, 4 Å < dmin < 5 Å; and long, dmin > 5 Å. Protons were added to the X-ray structures using simple geometrical criteria. During the database construction, each distance was sampled with ±0.2 Å, ± 0.1 Å, and 0.0 Å bias to account for the inaccuracy of heavy atom coordinates in the Xray structures and the inaccuracy of the proton addition. Hence, array P represents different outcomes of the disulfide-bond sampling which are mutually exclusive and exhaustive and therefore suitable for the probability assignment. The size of P is 32 × 32 × 4 × Lmax × 2, where Lmax is the largest sequence separation between two cysteines. Each cell contains the count of cases corresponding to a particular configuration which can be addressed by five indexes: χ ∈ ⟨1,9⟩ is the cysteine pair conformation, d ∈ ⟨1,9⟩ is the type of the proton pair, r ∈ ⟨1,4⟩ is the distance category, l ∈ ⟨1,Lmax⟩ the cysteine pair sequence separation, c ∈ ⟨1,2⟩ is the cysteine pair category (free cysteines were excluded from the sampling). The contraction of the database array across any of the distance, conformation (χ), or sequence (S) dimensions or across any subset of cells is equivalent to the loss of concomitant information (vide infra). To avoid divisions by zero, the zero case numbers were replaced by 1/n values, where n = ∑χ=1:9,d=1:9.r=1:4, l=1:L Pχ,d,r,l,1 for the disulfide bonded cysteines and n = ∑χ=1:9,d=1:9.r=1:4, l=1:L Pχ,d,r,l,2 for the nonbonded cysteines and L corresponds to the

p(Pk|S) =

Π

p(Ci 9 kCj||i − j|)

i , j ∈ C(2N ,2)

(2)

(3)

The (Pk|S) term is here the prior probability of the disulfide pattern given all intercysteine sequence separations, expressed by eq 3. This is the probability of a pattern before any NMR data is considered. Since the data represents independent observations for each cysteine pair, by using the product rule for the probabilities, the likelihood function p(data|Pk, S) can be further factorized according to eq 2.12 The subscripts i,j run over all possible combinations C(2N,2) of cysteine pairs, and ℛk denotes the relationship (bonded or nonbonded) between cysteine i and cysteine j appearing in a particular pattern k. The p(NOE|d) factor in eq 2 was introduced for calculation of the probabilities from the pdb file coordinates and corresponds to the probability of NOE given distance d, equal to (1 + (ln(d)21/ 540)−1 obtained from statistical analysis of historical NOE data. In the case of real NOE data, this factor is equal to 1. The interproton distances from the PDB structures were used with 0.0 Å and ±0.2 Å bias in the probability calculations. It is important to note that the sequence information (S) takes into account the number, but not the type, of amino acids separating two cysteines in a protein. In principle, it is possible to incorporate information about the protein context in this calculation as well.14 However, including only the sequence separation and the data likelihood was sufficient to resolve the correct disulfide pattern in all the cases investigated here. We also report the PADLOC probabilities where the sequence information S has been omitted (uniform priors), to demonstrate how the NMR data alone speak for a particular disulfide pattern. The probabilities p(Pk|data,S) in eq 1 can be directly calculated from the elements of the P array using the following relationship:

p(Pk|χ , d , r , S)

∏i , j Pχ , d , r , |i − j| , c(i , j , k) ∑k ∏i , j Pχ , d , r , |i − j| , c(i , j , k)

(4)

where all the subscripts have the same meaning as explained above. If the sequence information is neglected, the array P is contracted in the S dimension, the |i-j| index can be skipped and the cysteine numbering system is arbitrary. In the case of experimental NMR data, the input to PADLOC consists of data records containing encoded information about cysteine pair sequence locations (i,j), 263

dx.doi.org/10.1021/ac203078x | Anal. Chem. 2012, 84, 262−266

Analytical Chemistry

Article

conformation (χ), distance type (d), and distance category (r), all of which are subscripts to the P array. PADLOC also handles uncertainty which may arise as a result of complex spin dynamics (chemical exchange, strong coupling effects, multispin relaxation) and/or spectral overlap, leading to the ambiguous and/or alternative constraints, respectively. In the former case, the likelihood function in eq 1 takes the form: p(data1∨data2|Pk,S), where the ∨ sign designates exclusive “or”, which can be calculated as a simple sum: p(data1|Pk,S) + p(data2|Pk,S).15 In the latter case, the PADLOC probability p(Pk|data1∨data2,S) is in-between the p(Pk|data1,S) and p(Pk| data2,S) values.13 Both cases are illustrated for the analyzed peptides in Tables 2S−4S in the Supporting Information. All the calculations and database sampling were performed with Matlab R2008a software (MathWorks, Inc.) Preparation and Characterization of Synthetic KIIIA(116). All amino acids were purchased from Midwest Biotech or Novabiochem. The peptide was synthesized on a CS BIO 336X automated peptide synthesizer using standard 9-fluorenylmethoxycarbonyl (Fmoc) peptide chemistry protocols. After standard resin cleavage and side chain deprotection, the crude linear peptide was dissolved in 50:50 water/acetonitrile and transferred to an oxidative folding buffer17 (100 mM TrisHCl pH 7.0, 0.68 mM cystamine, 4.8% acetonitrile, pH = 7.2, 53 μM peptide). After overnight stirring, the folding reaction was quenched with TFA (pH = 2.5) and purified by preparative reverse-phase high-performance liquid chromatography (RPHPLC) using a Phenomenex Jupiter 20 mm × 200 mm column, lyophilized, and stored at −80 °C. LC−MS analysis of the peptide gave a monoisotopic mass of 1 882.65 Da (theoretical = 1 882.64 Da). Its purity was determined to be >95%. Experimental details are provided in the Supporting Information. The biological activity of our synthetic KIIIA(1-16) was measured against a panel of human voltage gated sodium channels (VGSCs) using the PatchXpress automated electrophysiology platform (Supporting Information). Measured IC50 values of 0.021 μM (hNav1.4), 0.512 μM (hNav1.2), 0.549 μM (hNav1.3), >25 μM (hNav1.5), and 0.458 μM (hNav1.7) were obtained. In the case of hNav1.2 and hNav1.7, the binding appeared to be almost irreversible consistent with previous reports.18 The determination of disulfide linkages in KIIIA by chemical method is described in the Supporting Information. NMR Experiments. Experiments were carried out on a Bruker Avance-III 800 MHz spectrometer, equipped with a triple resonance TCI cryoprobe, at sample temperatures of 293 and 306 K at 2 mM sample concentration. Chemical shifts were referenced to DSS. All experiments were acquired using the standard Bruker pulse program library. All spectra were processed with the Topspin 3.0 software.

Figure 1. PADLOC probabilities for disulfide patterns of a selected set of X-ray structures denoted by PDB entry symbols. Calculations represented by the red, blue, and black bars include the distance, distance and conformation, distance, conformation, and sequence information, respectively (see Table 1S in the Supporting Information for more details).

probability of 1 means that the odds of a different disulfide connectivity are less than 1 in 10 000. Successful resolution of disulfide patterns in the X-ray structures prompted us to apply the same algorithm to the NMR structures deposited into the PDB. The results are quite surprising (Figure 1S in the Supporting Information) and demonstrate that in about 30% of the structures available, the disulfide pattern is structurally undetermined, i.e., the probability of the structural connectivity varies across the NMR ensemble and/or is significantly less than one. Although PADLOC is an inference tool, its logical outcome must obey rules of deductive reasoning. Thus a PADLOC probability of one obtained from the coordinates of an NMR structure leads to the vicious circle in the proof,16 where the disulfide connectivity, meant to establish the NMR structure, is based upon that structure itself. PADLOC can show the indeterminacy of disulfide pattern for a given NMR structure or structural ensemble; however, it cannot logically resolve the pattern if the structure is inaccurate or uncertain. PADLOC infers disulfide connectivity only from the experimentally

■

RESULTS AND DISCUSSION The performance of PADLOC in predicting disulfide bond connectivities from the X-ray coordinates was tested on a select set of challenging protein structures which have crowded networks of up to seven disulfide bonds. Figure 1 (Table 1S in the Supporting Information) demonstrates that disulfide networks which are degenerate, i.e., have more than one maximum probability pattern in the NOE space (red bars), are resolved with few exceptions: 1AOL, 3HOT, and 1H34, in the combined distance and χ space (blue bars), all are fully resolved in the combined distance, χ and S space (black bars). Here, the 264

dx.doi.org/10.1021/ac203078x | Anal. Chem. 2012, 84, 262−266

Analytical Chemistry

Article

observed genuine intercysteine constraints. This connectivity may be subsequently used as a constraint in software calculating 3D structures from the NMR data. In Table 1, we show three practical applications of PADLOC to cases where conflicting disulfide connectivities have been Table 1. Disulfide Connectivities from PADLOC Calculationsa peptide

connectivity

Kalata-B1 Hepcidin KIIIA

1−15,5−17,10−22 7−23,10−13,11−19,14−22 1−15,2−9,4−16

probabilityb 0.9998 1.0000 1.0000

Figure 2. Ensemble of 10 minimized NMR structures of KIIIA (a) determined in this work and (b) BMRB data entry 20048 (ref 19).

0.9936 1.0000 1.0000

difference in the appearance of both ensembles is directly related to the different disulfide patterns. Most importantly, the conformational heterogeneity of the 1−15 bond in the structure shown in Figure 2A corresponds to the dynamic nature of this bond, whereas structural disorder across the ensemble shown in Figure 2B represents the inconsistency of the information. The published structure was based on almost twice as many NMR restraints; however, only one intercysteine 4−16 NOE was observed.19 The KIIIA case strongly indicates that high quality intercysteine constraints and not necessarily the quantity of NMR constraints are the driving force for the correct cysteines pairing in well-defined structures of disulfide rich peptides. Most likely, inherent flexibility of disulfide bridges and spectral crowding, which disable straightforward detection of distance and angular constraints for cysteine residues, are responsible for the imprecision and inaccuracy of possibly many published NMR structures. Fortunately, these difficulties can usually be resolved by judicious control of spin dynamics by the rf pulse sequence and/or temperature variation of the NMR sample, isotope labeling,20 and importantly, ultrahigh magnetic fields. Energy differences and energy barriers between conformational states of disulfide bonds are of the orders suitable for shifting the Boltzmann ensemble to a single conformation or achieving adequate spectral averaging within the accessible temperature range of an NMR experiment.11,21 Small peptides with multiple disulfide bonds are usually stable for periods of time long enough to collect critical cysteine NOE and coupling constant data for accurate location of disulfide bonds.

a

Input data is in Tables 2S, 3S and 4S in the Supporting Information. b The first and the second number in each row corresponds to the calculation with and without the sequence information, respectively.

published. We used the intercysteine NMR constraints reported for kalata B19 and hepcidin,11 and in place data (vide infra) for the μ-conotoxin KIIIA(1-16) (denoted as KIIIA), all three representing different classes of disulfide rich peptides of significant therapeutic potential. In the case of hepcidin, the only observed through space dipolar couplings are between the cysteines of the same disulfide bond and the PADLOC connectivity is intuitively obvious (Table 2S in the Supporting Information). However, in the case of kalata B1 peptide, there are five observed NOE contacts corresponding to the different cysteine pairs, where only one of them is connected by a disulfide bond (Table 3S in the Supporting Information). The kalata B1 case also illustrates how PADLOC may assist in designing NMR experiments to gain the intercysteine constraints not accessible in a straightforward way. Interestingly, in contrast to the hepcidin solution structure, the crystallographic hepcidin (3HOT in Figure 1) has an unresolved disulfide pattern if the sequence information is ignored in the PADLOC model (calculation C2 in Table 1S in the Supporting Information). This difference can be explained by the different conformations of hepcidin11 in the crystal and at the elevated temperatures in the solution state, where the NMR data for PADLOC input have been recorded. In the case of kalata B1, the NMR and the X-ray based (3E4H in Figure 1) PADLOC results are very similar. The disulfide connectivity in μ-conotoxin KIIIA(1-16) is an intriguing example since it is different from the previously reported disulfide connectivity for KIIIA18,19 (1−9,2−15,4− 16). All proton chemical shifts obtained for our synthetic material at the same experimental conditions as in ref 19 are virtually the same as previously reported, with the largest difference not exceeding 0.02 ppm, strongly suggesting that the same biochemical substance, equivalent to the endogenous peptide, was studied in both cases.19 The derivation of the angular and distance constraints for the PADLOC calculation (Table 4S in the Supporting Information) is given in Tables 6S and 7S in the Supporting Information, respectively. The discrepancy in the disulfide connectivity prompted us to independently confirm the PADLOC result by mapping the disulfide bonds in KIIIA by chemical analysis, as described in the Supporting Information. In addition, the 3D structure for KIIIA was calculated with the PADLOC connectivity and a number of other NMR restraints listed in Table 8S in the Supporting Information. We obtained a well-defined structure shown in Figure 2A, with an overall topology remarkably similar to the published structure shown in Figure 2B. The

■

CONCLUSIONS

In small proteins or peptides with multiple disulfide bonds, a unique disulfide pattern is consistent with a unique protein fold. However, the inverse is not always true, i.e., a unique protein fold may be consistent with more than one disulfide patterns. This asymmetry may easily lead to ambiguous disulfide bond assignment from the NMR structure calculations, particularly in cases where sufficient quality of intercysteine restraints is not available. We proposed a new approach for determination of disulfide connectivity which is based on coherent and objective Bayesian methodology for learning and drawing reasonable conclusions from the data. In our case, the data is represented by the combination of historical X-ray structures and current, cysteine-only NMR data, both compiled into a robust inference tool. This combination of different data sources should allow accurate disulfide connectivity to be determined in many cases where an isolated experimental approach fails. 265

dx.doi.org/10.1021/ac203078x | Anal. Chem. 2012, 84, 262−266

Analytical Chemistry

■

Article

ASSOCIATED CONTENT

S Supporting Information *

Data tables associated with PADLOC computations, KIIIA preparation, hNav electrophysiology experiments, NMR analyses, and disulfide mapping. The Matlab PADLOC script and the Matlab script used for the construction of the array P can be requested from the corresponding author. This material is available free of charge via the Internet at http://pubs.acs.org.

■ ■ ■

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected]. ACKNOWLEDGMENTS We thank Izydor Apostol, Mike Toupikov, and Thomas Szyperski for critically reading the manuscript. REFERENCES

(1) Schnaible, V.; Wefing, S.; Bücker, A.; Wolf-Kümmeth, S.; Hoffmann, D. Anal. Chem. 2002, 74, 2386−2393. (2) Gupta, K.; Kumar, M.; Balaram, P. Anal. Chem. 2010, 82, 8313− 8319. (3) Agarwal, A.; Diedrich, J. K.; Julian, R. R. Anal. Chem. 2011, 83, 8455−6458. (4) Boisbouvier, J.; Blackledge, M.; Sollier, A.; Marion, D. J. Biomol. NMR 2000, 16, 197−208. (5) Klaus, W.; Broger, C.; Gerber, P; Senn, H. J. Mol. Biol. 1993, 232, 897−906. (6) Salzmann, M.; Socking, E. M.; Silks, L. A. III; Senn, H. Magn. Reson. Chem. 1999, 37, 672−675. (7) Mobli, M.; Dantas de Araújo, A.; Lambert, Lambert, L. K.; Pierens, G. K.; Windley, M. J.; Nicholson, G. M.; Alewood, P. F.; King, G. F. Angew. Chem., Int. Ed. 2009, 48, 9312−9314. (8) Mobli, M.; King, G. F. Toxicon 2010, 56, 849−854. (9) Rosengren, K. J.; Daly, N. L.; Plan, M. R.; Waine, C.; Craik, D. J. J. Biol. Chem. 2003, 278 (10), 8606−8616. (10) Wang, C. K.; Hu, S.; Martin, J. L.; Sjögren, T.; Hajdu, J.; Bohlin, L.; Claeson, P.; Göransson, U.; Rosengren, J.; Tang, J.; Tan, N.; Craik, D. J. J. Biol. Chem. 2009, 284 (16), 10672−10683. (11) Jordan, J. B.; Poppe, L.; Haniu, M.; Arvedson, T.; Syed, R.; Li, V.; Kohno, H.; Kim, H.; Schnier, P. D.; Harvey, T. S.; Miranda, L. P.; Cheetham, J.; Sasu, B. J. J. Biol. Chem. 2009, 284 (36), 24155−24167. (12) Jaynes, E. T. Probability Theory. The Logic of Science; Cambridge University Press: Cambridge, U.K., 2003. (13) Jeffrey, R. Subjective Probability. The Real Thing; Cambridge University Press: Cambridge, U.K., 2004. (14) Singh, R. Briefings Funct. Genomics Proteomics 2008, 7 (2), 157− 172. (15) Cox, T. R. The Algebra of Probable Inference; The John Hopkins Press: Baltimore, MD, 1961. (16) Tarski, A. Introduction to Logic and the Methodology of Deductive Sciences; Dover Publications, Inc.: New York, 1995. (17) Bulaj, G.; West, P. J.; Garrett, J. E.; Watkins, M.; Zhang, M.-M.; Norton, R. S.; Smith, B. J.; Yoshikami, D.; Olivera, B. M. Biochemistry 2005, 44, 7259−7265. (18) Zhang, M.-M.; Green, B. R.; Catlin, P.; Fiedler, B.; Azam, L.; Chadwick, A.; Terlau, H.; McArthur, J. R.; French, R. J.; Gulyas, J.; Fivier, J. E.; Smith, B. J.; Norton, R. S.; Olivera, B. M.; Yoshikami, D.; Bulaj, G J. Biol. Chem. 2007, 282 (42), 30699−30706. (19) Khoo, K. K.; Feng, Z.-P.; Smith, B. J.; Zhang, M.-M.; Yoshicami, D.; Olivera, B. M.; Bulaj, G.; Norton, R. S. Biochemistry 2009, 48, 1210−1219. (20) Waleska, A.; Skalicky, J. J.; Davis, D.; Zhang, M.-M.; Lopez-Vera, E.; Watkins, M.; Han, T. S.; Yoshikami, D.; Olivera, B. M.; Bulaj, G. J. Am. Chem. Soc. 2008, 130, 14280−14286. (21) Otting, G.; Liepinsh, E.; Wüthrich. Biochemistry 1993, 32, 3571−3582. 266

dx.doi.org/10.1021/ac203078x | Anal. Chem. 2012, 84, 262−266