- Research article
- Open Access
Analysis of the stoichiometric metal activation of methionine aminopeptidase
© Chai and Ye; licensee BioMed Central Ltd. 2009
- Received: 28 September 2009
- Accepted: 17 December 2009
- Published: 17 December 2009
Methionine aminopeptidase (MetAP) is a ubiquitous enzyme required for cell survival and an attractive target for antibacterial and anticancer drug development. The number of a divalent metal required for catalysis is under intense debate. E. coli MetAP was shown to be fully active with one equivalent of metal by graphical analysis, but it was inferred to require at least two metals by a Hill equation model. Herein, we report a mathematical model and detailed analysis of the stoichiometric activation of MetAP by metal cofactors.
Because of diverging results with significant implications in drug discovery, the experimental titration curve for Co2+ activating MetAP was analyzed by fitting with a multiple independent binding sites (MIBS) model, and the quality of the fitting was compared to that of the Hill equation. The fitting by the MIBS model was clearly superior and indicated that complete activity is observed at a one metal to one protein ratio. The shape of the titration curve was also examined for activation of metalloenzymes in general by one or two metals.
Considering different scenarios of MetAP activation by one or two metal ions, it is concluded that E. coli MetAP is fully active as a monometalated enzyme. Our approach can be of value in proper determination of the number of cations needed for catalysis by metalloenzymes.
- Titration Curve
- Metal Site
- Hill Equation
- Total Metal Concentration
- Hill Function
A myriad of enzymes exploit the diverse and dynamic properties of metal ions for activity, many of which carry out crucial functions for cellular survival . An important feature in the understanding of their catalytic mechanisms entails the number of a metal ion that the active site needs for complete activation. A challenge transpires when the cofactor is loosely bound, making it difficult to perform direct metal speciation on purified enzyme. In the case of metalloenzymes, the number of an activating metal cofactor can be deduced from stoichiometric titration curves, where the rise in activity correlates with increasing metal concentrations . Herein, we report the application of a robust nonlinear regression approach in determination of the number of the catalytically relevant metal ion required by the metalloprotease methionine aminopeptidase (MetAP).
MetAP is involved in protein maturation by catalyzing the hydrolytic excision of the N-terminal methionine from nascent proteins , which was demonstrated to be an essential process [4–6]. The MetAP apoenzyme can be activated by a number of divalent cations, including Co2+, Mn2+, Fe2+, Ni2+ and Zn2+ [7, 8]. The majority of MetAP inhibitors discovered in the quest to develop antibacterial and anticancer agents bind to the active site, interacting directly with the catalytic metal cofactor. However, most of the compounds that inhibit a purified enzyme cannot show their effect at the cellular level . This was inferred to be partly due to discrepancies in the type and number of the cation used during in vitro assays and those found under physiological conditions [9, 10]. Although most of MetAP structures showed an active site containing two metal ions , E. coli MetAP was proposed to contain only one metal based on measurement of activities in solution as a function of metal concentrations . However, it was suggested to be inconclusive due to the small number of data points . Because metal stoichiometry is more accurately determined under a tight-binding situation, we previously determined a one metal per enzyme ratio by graphical analysis of a titration curve under the tight-binding condition . The result was disputed because computation of the Hill coefficient based on the same titration curve indicated that this enzyme required at least two metal ions . As a result of the diverging outcomes with significant implications in MetAP inhibitor development, the correct assignment of the number of the activating metal ion in MetAP becomes very significant. Therefore, we provide here a detailed analysis of the stoichiometric activation of E. coli MetAP by binding of Co2+. Our conclusion is that E. coli MetAP requires only one equivalent of metal ions for full activation.
Scenarios in the activation of a metalloenzyme in an apoform by one or two metals
Stoichiometric titration curves have been widely employed in the determination of the number of a metal ion required for activation of metalloenzymes . A general procedure involves generation of a curve in terms of enzymatic activity as a function of the metal concentrations added. Because the protein concentration is known, the x-axis can be expressed in terms of metal/protein ratios. It is important to note that for accurate stoichiometric titrations, the enzyme concentration should be at least ten times higher than the equilibrium dissociation constant, to approach the tight-binding condition . We recently described a nonlinear curve fitting of metal titration curves using the multiple independent binding sites (MIBS) model to determine the equilibrium binding affinity, as a KD value, based on functional enzyme concentrations .
There are cases when a single metal cannot activate the apoenzyme and two metal ions are required for activation. It is therefore necessary to fill both the first binding site (M1 site) and the second site (M2 site) to observe enzyme activation. Case 2 (Fig. 1) deals with one of the situations when the activation of apoenzyme requires two metal ions with equal or different affinities. The shape of the titration curve will depend on the protein concentration in relation to the cation binding affinities to both metal sites (M1 and M2). When the protein concentration is much higher (10 fold or more) than both of the two dissociation constants (Case 2, Fig. 1B), one would expect a rectangular hyperbola, which clearly indicates a 2:1 metal to protein ratio. This case is exemplified by an aminopeptidase from Streptomyces griseus, where the titration curve showed a hyperbolic dependence of activity on metal concentration, and addition of two molar equivalents of metal to apoenzyme fully restored activity . For many dinuclear enzymes, each of the two metals is coordinated by a different set of amino acid residues. Therefore, it is common that a metal ion binds at the two sites with different affinities. However, no matter whether the binding affinities to the M1 and M2 sites are equal or different, a titration curve similar to that obtained for Case 2 (Fig 1B) is achieved as long as the protein concentration is much higher than both KD and KD2 values (a tight binding situation).
Case 3 (Fig. 1) represents another situation when binding to the first metal site (M1 site) is much stronger than to the second site (M2 site), due to a wide difference in affinity. A protein concentration can be selected, so that metal binding is tight to the first site but not to the second site. Because filling only the tight metal site does not result in enzyme activation, a lag in the observed activity corresponds to 1 equivalent of metal titrated. The signal arising from the activated enzyme is then as a result of metal binding to the weaker second M2 site. The steepness of this onset depends on the KD2 value relative to the protein concentration (Fig. 1C, upper vs lower curves). This type of metal-activation mode has been observed in a bacterial hydantoinase that required two metals for activity, while binding of a single cation per monomer failed to activate the enzyme, displaying an activity lag in the titration curve .
Analysis of the stoichiometric titration curve for activation of E. coli MetAP by Co2+
MetAP belongs to the dinuclear metallohydrolases . Many MetAP structures have been reported with two metal ions at the active site , and the two metal ions are coordinated by five conserved amino acid residues (D97, D108, H171, E204 and E235 in E. coli MetAP). The affinities of the two metal ions are quite different in solution. Based on electronic absorption spectra titration, isothermal titration calorimetry, and kinetics, D'souza et al. showed that dissociation constants for the tighter site were at or below micromolar (KD 0.3 μM, 0.2 μM and 6 μM for Co2+, Fe2+ and Mn2+, respectively), but the affinity for the second metal ion was much weaker with a millimolar dissociation constant (KD2 2.5 mM for Co2+) [18, 19]. During crystallization experiments, a high protein concentration is often required. The high protein concentration facilitates the binding of metal ions to both the first site and the second site. The presence of a substrate or inhibitor can further enhance the affinity of metal ions to the two sites. Therefore, it is not surprising to see the dimetalated MetAP form in crystal structures. By limiting the metal concentration during crystallization, we have obtained a monometalated MetAP structure in complex with an inhibitor .
In order to clarify the differing conclusions arising from the same titration curve (graphical analysis  as opposed to analysis by the Hill equation ), Eq. (4) was used as a robust tool that can be employed at any enzyme concentration. The same titration curve was expressed in terms of the total metal concentration before fitting with Eq. (4) (MIBS model, Fig. 2B) or Eq. (7) (Hill equation, Fig. 2C). The fit with the MIBS model gave n = 1.0, indicating one metal per active site, while the calculated Hill coefficient of 2.3 would indicate a minimum of two metals per active site. Hu and coworkers fitted the Hill function to the total metal concentration against the corresponding activity in order to get initial parameters needed to obtain a plot of activity as a function of the free metal concentration . However, the determined initial estimations would have significant effects in the shape of the curve at latter stages of analysis, which possibly led to a different conclusion. They reported the fitting with an initial Hill coefficient of 2.1, and iteration of the data yielded n values significantly greater than 2 .
Comparison of the residuals obtained using both models (Figs. 2B and 2C inserts) reveals that the Hill equation produced poor data fitting at low metal concentrations, indicating systematic deviation of the fit from the data. The effect becomes more evident when the fits obtained from both models are plotted against the original data in a log-scale (Fig. 2D). In contrast to the reasonable fit by the MIBS model, the Hill equation model showed markedly departure from linearity at low values. We find this type of plot to be very informative as a way to visually describe how well a model fits the data. The Durbin-Watson statistic was employed to assess correlation among the residuals, giving the MIBS model a value of 1.5. On the other hand, the Hill function showed the Durbin-Watson statistic of 0.8. Further departure from the ideal value of 2 by the Hill function indicates higher correlation among residuals. Another method widely used to check for randomness of residuals is the Wald-Wolfowitz runs test [20–22]. Out of 18 residual points, there are 8 runs obtained from modeling with MIBS (P = 0.251) as opposed to only 5 runs for that modeled by the Hill equation (P = 0.013). The latter model was associated with a P value < 0.05, confirming systematic deviation.
It is evident from the analysis presented in Fig. 2 that our proposed MIBS model correlates with the results obtained by graphical analysis from the titration curve of activities as a function of metal/protein ratios (Fig. 2A). Certainly, the curve based on the raw data (Fig. 2A) has the same profile as the curves presented in Cases 1 and 2 (Figs. 1A and 1B), and it does not follow the behavior associated to Case 3 (Fig. 1C). The reported binding affinity of Co2+ to the second binding site M2 (KD of 2.5 mM) of E. coli MetAP is over 8,000 times weaker than that for the first metal site M1 (KD of 0.3 μM). Taking into consideration the total enzyme concentration used in the generation of Fig. 2A (20 μM), a tight-binding situation was created for the M1 site, but not for the M2 site. If two metal ions are required for MetAP activation, the shape of the curve would therefore resemble that for Case 3 (Fig. 1C). The dissociation constant reported for Co2+ to the second weaker site (vide supra) was obtained spectroscopically in the absence of a substrate. However, in the event that the presence of substrate decreased the KD value for the second binding site to such extent that a tight binding situation was created for both metal sites, then our proposed MIBS model would determine n = 2 and the raw data (Fig. 2A) would bear a resemblance to Fig. 1B.
Several factors can potentially affect the shape of a titration curve, such as protein aggregation and residual metal or metal chelator in an apoprotein preparation, and lead to different conclusions according to fitting of the curves. E. coli MetAP is a very soluble protein, and it was used as high as 1 mM in equilibrium dialysis without interference of aggregation . In the equilibrium dialysis study, it was concluded that the enzyme bound up to 1.1 equivalent of Co2+ in the metal concentration range likely to be found in vivo, which is consistent with our conclusion of 1:1 stoichiometry. We recently characterized MetAP from Mycobacterium tuberculosis for metal binding and activation and found that it is also a monometalated enzyme .
Metal cofactors form integral parts in a great variety of enzymes, and the correct assignment of the number of an activating metal is an essential progression towards understanding mechanistic aspects, and in the case of MetAP, towards the development of lead candidates for drug discovery. We have applied the MIBS model with a more cautious interpretation of the fits, arriving at the conclusion that a single metal ion per active site is sufficient to restore the complete E. coli MetAP activity. We have also outlined the interplay among KD values and the total protein concentration in defining the shape of the titration curve, which is illustrated by different scenarios involving one- or two-metal activation. We believe that these assessments are relevant in the proper analysis of stoichiometric binding and activation of metalloenzymes by metal ions.
where KD is the dissociation constant, n is the number of independent binding sites, [MP] is the concentration of bound metal, [M]F and [P]T are the concentrations for free metal and total protein, respectively.
Eq. (4) was used to fit the raw data points that make up the E. coli MetAP titration curve (Fig. 2B). This equation was also utilized to generate simulated curves for Cases 1 and 2 (Figs. 1A-B). Eq. (4) is applicable to assay conditions at any given protein concentration, and it allows for simplification in curve fitting, because concentrations are based on the total protein and metal in the assay solution instead of the free (unbound) species. Therefore, the need for conversion from the total concentration to the free concentration of individual species is avoided.
Curve fitting and analysis were performed using Igor Pro (Wavemetrics, Lake Owego, OR) and Sigma Plot (Systat Software, San Jose, CA). Simulated curves were generated by Microsoft Excel.
This work was supported by National Institutes of Health Grant R01 AI065898 (to Q.-Z. Y.).
- Wilcox DE: Binuclear Metallohydrolases. Chem Rev. 1996, 96 (7): 2435-2458. 10.1021/cr950043b.View ArticleGoogle Scholar
- Winzor DJ, Sawyer WH: Quantitative Characterization of Ligand Binding. 1995, John Wiley & SonsGoogle Scholar
- Bradshaw RA, Brickey WW, Walker KW: N-terminal processing: the methionine aminopeptidase and N alpha-acetyl transferase families. Trends Biochem Sci. 1998, 23 (7): 263-267. 10.1016/S0968-0004(98)01227-4.View ArticleGoogle Scholar
- Chang SY, McGary EC, Chang S: Methionine aminopeptidase gene of Escherichia coli is essential for cell growth. J Bacteriol. 1989, 171 (7): 4071-4072.Google Scholar
- Li X, Chang YH: Amino-terminal protein processing in Saccharomyces cerevisiae is an essential function that requires two distinct methionine aminopeptidases. Proc Natl Acad Sci USA. 1995, 92 (26): 12357-12361. 10.1073/pnas.92.26.12357.View ArticleGoogle Scholar
- Miller CG, Kukral AM, Miller JL, Movva NR: pepM is an essential gene in Salmonella typhimurium. J Bacteriol. 1989, 171 (9): 5215-5217.Google Scholar
- D'Souza VM, Holz RC: The methionyl aminopeptidase from Escherichia coli can function as an iron(II) enzyme. Biochemistry. 1999, 38 (34): 11079-11085. 10.1021/bi990872h.View ArticleGoogle Scholar
- Li JY, Chen LL, Cui YM, Luo QL, Li J, Nan FJ, Ye QZ: Specificity for inhibitors of metal-substituted methionine aminopeptidase. Biochem Biophys Res Commun. 2003, 307 (1): 172-179. 10.1016/S0006-291X(03)01144-6.View ArticleGoogle Scholar
- Schiffmann R, Heine A, Klebe G, Klein CD: Metal ions as cofactors for the binding of inhibitors to methionine aminopeptidase: A critical view of the relevance of in vitro metalloenzyme assays. Angew Chem Int Ed Engl. 2005, 44 (23): 3620-3623. 10.1002/anie.200500592.View ArticleGoogle Scholar
- Chai SC, Wang WL, Ye QZ: FE(II) Is the Native Cofactor for Escherichia coli Methionine Aminopeptidase. J Biol Chem. 2008, 283 (40): 26879-26885. 10.1074/jbc.M804345200.View ArticleGoogle Scholar
- Lowther WT, Matthews BW: Structure and function of the methionine aminopeptidases. Biochim Biophys Acta. 2000, 1477 (1-2): 157-167.View ArticleGoogle Scholar
- Copik AJ, Swierczek SI, Lowther WT, D'Souza VM, Matthews BW, Holz RC: Kinetic and spectroscopic characterization of the H178A methionyl aminopeptidase from Escherichia coli. Biochemistry. 2003, 42 (20): 6283-6292. 10.1021/bi027327s.View ArticleGoogle Scholar
- Hu XV, Chen X, Han KC, Mildvan AS, Liu JO: Kinetic and mutational studies of the number of interacting divalent cations required by bacterial and human methionine aminopeptidases. Biochemistry. 2007, 46 (44): 12833-12843. 10.1021/bi701127x.View ArticleGoogle Scholar
- Ye QZ, Xie SX, Ma ZQ, Huang M, Hanzlik RP: Structural basis of catalysis by monometalated methionine aminopeptidase. Proc Natl Acad Sci USA. 2006, 103 (25): 9470-9475. 10.1073/pnas.0602433103.View ArticleGoogle Scholar
- Chai SC, Lu JP, Ye QZ: Determination of binding affinity of metal cofactor to the active site of methionine aminopeptidase based on quantitation of functional enzyme. Anal Biochem. 2009, 395 (2): 263-264. 10.1016/j.ab.2009.07.054.View ArticleGoogle Scholar
- Ben-Meir D, Spungin A, Ashkenazi R, Blumberg S: Specificity of Streptomyces griseus aminopeptidase and modulation of activity by divalent metal ion binding and substitution. Eur J Biochem. 1993, 212 (1): 107-112. 10.1111/j.1432-1033.1993.tb17639.x.View ArticleGoogle Scholar
- Huang CY, Hsu CC, Chen MC, Yang YS: Effect of metal binding and posttranslational lysine carboxylation on the activity of recombinant hydantoinase. J Biol Inorg Chem. 2009, 14 (1): 111-121. 10.1007/s00775-008-0428-x.View ArticleGoogle Scholar
- D'Souza VM, Bennett B, Copik AJ, Holz RC: Divalent metal binding properties of the methionyl aminopeptidase from Escherichia coli. Biochemistry. 2000, 39 (13): 3817-3826. 10.1021/bi9925827.View ArticleGoogle Scholar
- D'Souza VM, Swierczek SI, Cosper NJ, Meng L, Ruebush S, Copik AJ, Scott RA, Holz RC: Kinetic and structural characterization of manganese(II)-loaded methionyl aminopeptidases. Biochemistry. 2002, 41 (43): 13096-13105. 10.1021/bi020395u.View ArticleGoogle Scholar
- Motulsky HJ, Ransnas LA: Fitting curves to data using nonlinear regression: a practical and nonmathematical review. FASEB J. 1987, 1 (5): 365-374.Google Scholar
- Miller JC, Miller JN: Statistics for Analytical Chemistry. 1993, Ellis Horwood Limited, West Sussex, 148-149.Google Scholar
- Beyer WH: Handbook of Tables for Probability and Statistics. 1968, The Chemical Rubber Co., Cleveland, 414-424.Google Scholar
- Larrabee JA, Leung CH, Moore RL, Thamrong-nawasawat T, Wessler BS: Magnetic circular dichroism and cobalt(II) binding equilibrium studies of Escherichia coli methionyl aminopeptidase. J Am Chem Soc. 2004, 126 (39): 12316-12324. 10.1021/ja0485006.View ArticleGoogle Scholar
- Drake AW, Klakamp SL: A rigorous multiple independent binding site model for determining cell-based equilibrium dissociation constants. J Immunol Methods. 2007, 318 (1-2): 147-152. 10.1016/j.jim.2006.08.015.View ArticleGoogle Scholar
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