Inhibition of Inosine-5â²-monophosphate Dehydrogenase from

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Inhibition of IMPDH from Bacillus anthracis: Mechanism revealed by pre-steady state kinetics Yang Wei, Petr Kuzmic, Runhan Yu, Gyan Modi, and Lizbeth Hedstrom Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.6b00265 • Publication Date (Web): 19 Aug 2016 Downloaded from http://pubs.acs.org on August 30, 2016

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Inhibition of IMPDH from Bacillus anthracis: Mechanism revealed by pre-steady state kinetics † Yang Wei,‡,⊥ Petr Kuzmiˇc,§,‡ Runhan Yu,k Gyan Modi,‡,¶ and Lizbeth Hedstrom∗,k,‡ ‡Department of Biology, Brandeis University, Waltham, Massachusetts 02454 §BioKin Ltd., Watertown, Massachusetts 02472 kDepartment of Chemistry, Brandeis University, Waltham, Massachusetts 02454 ⊥Current address: Sanford-Burnham-Prebys Medical Discovery Institute, La Jolla, California 92037 ¶Current address: Department of Pharmaceutics, Indian Institute of Technology, Banaras Hindu University, Varanasi-221005 E-mail: [email protected] Phone: +1 (781) 736-2333. Fax: +1 (781) 736-2349

Running header IMPDH inhibition kinetics

†

This work was supported by National Institutes of Health grants AI093459 and GM054403 (to L. H.).

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Abstract Inosine-5’-monophosphate dehydrogenase (IMPDH) catalyzes the conversion of inosine5’-monophosphate (IMP) to xanthosine-5’-monophosphate (XMP). The enzyme is an emerging target for antimicrobial therapy. The small molecule inhibitor A110 has been identified as potent and selective inhibitor of IMPDHs from a variety of pathogenic microorganisms. A recent X-ray crystallographic study reported that the inhibitor binds to the NAD+ cofactor site and forms a ternary complex with IMP. Here we report a pre-steady-state stopped-flow kinetic investigation of IMPDH from Bacillus anthracis designed to assess the kinetic significance of the crystallographic results. Stopped-flow kinetic experiments defined nine microscopic rate constants and two equilibrium constants that characterize both the catalytic cycle and details of the inhibition mechanism. In combination with steady-state initial rate studies, the results show that the inhibitor binds with high affinity (Kd ≈ 50 nM) predominantly to the covalent intermediate on the reaction pathway. Only a weak binding interaction (Kd ≈ 1 µM) is observed between the inhibitor and E·IMP. Thus the E·IMP·A110 ternary complex, observed by X-ray crystallography, is largely kinetically irrelevant.

Abbreviations CBS, cystathionine β-synthetase; CV, coefficient of variation (%); DE, Differential Evolution; IMP, inosine 5’-monophosphate; IMPDH, IMP dehydrogenase; BaIMPDH, IMPDH from Bacillus anthracis; BaIMPDH∆L, BaIMPDH Glu92-Arg220 deletion mutant; CpIMPDH, IMPDH from Cryptosporidium parvum; MPA, mycophenolic acid; XMP, xanthosine 5’-monophosphate;


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Introduction Antibiotic resistance is a world-wide problem threatening the effective treatment of infections caused by pathogenic bacteria (1, 2). New antibiotics and targets are urgently needed (3). It is also important to develop new antibiotics against potential bioterrorism agents (4), such as Bacillus anthracis, the causative agent of anthrax. Inosine-5’-monophosphate dehydrogenase (IMPDH) has recently emerged as a promising antimicrobial drug target (5, 6). IMPDH catalyzes the oxidation of inosine-5’-monophosphate (IMP) to xanthosine-5’-monophosphate (XMP) with simultaneous reduction of the cofactor NAD+ to NADH (7). The conversion of IMP to XMP is the first and rate-limiting step in guanine nucleotide biosynthesis pathway. Inhibiting IMPDH causes an imbalance in the purine nucleotide pool that suppresses proliferation. The reaction involves the initial attack of the active site Cys on C2 of IMP coupled to NAD+ reduction, to form a covalent intermediate. NADH departs, a disordered flap folds into the empty cofactor site and the covalent intermediate undergoes hydrolysis. Several inhibitors of mammalian IMPDHs, most notably mycophenolic acid, bind selectively to the covalent intermediate and prevent hydrolysis (8). Selective inhibitors of IMPDH from Cryptosporidium parvum were identified by high-throughput screening followed by structural refinement (9–15). These compounds effectively inhibit CpIMPDH activity in vitro, display significant antiparasitic activity against an engineered Toxoplasma gondii strain relying solely on CpIMPDH, and show a therapeutic effect in a mouse model mimicking acute human cryptosporidiosis (9–12, 14). Some of these compounds are also effective against IMPDHs from bacteria such as Bacillus anthracis and Mycobacterium tuberculosis, suggesting their potential use as antibiotic agents (5, 6, 13). The triazole compound A110, which inhibits BaIMPDH with IC50 = (43±3) nM (15), displays in vitro antibacterial activity against B. anthracis and Staphylococcus aureus (13). Related “A” series compounds are uncompetitive inhibitors with respect to IMP and noncompetitive (mixed) inhibitors with respect to NAD+ . These observations suggest that A110 binds after IMP, but do not reveal whether it binds to E·IMP or another downstream complex. Crystal structures of small 3 ACS Paragon Plus Environment

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molecule inhibitors complexes with BaIMPDH and Clostridium perfringes IMPDH have been solved, indicating that A110 binds to the cofactor site. However, these structures may not represent the highest affinity enzyme-inhibitor complexes (9, 15, 16). In this report, we utilize stopped-flow rapid kinetics techniques, supported by confirmatory initial rate experiments, to investigate the detailed inhibition mechanism of the small-molecule inhibitor A110 against a subdomain-deleted form of the B. anthracis enzyme, BaIMPDH∆L (15). The results show that A110 binds predominantly to the covalent intermediate E-XMP∗ . We discuss these kinetic results in terms of possible conformational and structural effects. We also report the values of microscopic rate constants that characterize the catalytic cycle of BaIMPDH∆L. The mechanistic insights should prove useful in the rational design of IMPDH targeted therapy.

Materials and Methods Materials IMP disodium salt was purchased from MP Biomedicals. NAD+ free acid was purchased from Roche and NADH disodium salt was purchased from Acros. Compound A110 was synthesized as described (17). DTT, TCEP and IPTG were from Gold Biotechnology. All other materials were from Fisher.

Experimental Methods Protein expression and purification The construction of the plasmid expressing His6 tagged BaIMPDH∆L was described in (15). The plasmid was transformed into E. coli BL21 ∆guaB competent cells (16). His-tagged protein was over-expressed at 18◦ C for 20 h, and purified with Ni-NTA Sepharose beads (GE) at 4◦ C in lysis buffer (50 mM phosphate buffer, pH 8.0, 500 mM KCl, 5 mM imidazole, 1 mM TCEP and 10% glycerol). After elution with 500 mM imidazole, pure protein fractions (> 95% as analyzed by


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SDS-PAGE) were collected and dialyzed first against dialysis buffer A (50 mM Tris HCl, pH 8.0, 1 mM TCEP, 1 mM EDTA, 300 mM KCl), then dialyzed twice against dialysis buffer B (50 mM Tris-HCl, pH 8.0, 150 mM KCl, 1 mM DTT, 3 mM EDTA). After dialysis, protein concentration was determined by Bradford assay using IgG as the standard and divided by a factor of 2.6 (18), and was stored at -80◦ C. Stopped-flow pre-steady state kinetics Stopped-flow experiments were performed at 25◦ C using Applied Photophysics SX17MV spectrophotometer. Syringe 1 contained BaIMPDH∆L protein (nominal concentration 8 µM subsequently optimized during data analysis) and IMP (2 mM) in assay buffer containing 2.5% (v/v) DMSO. Syringe 2 contained variable concentrations of NAD+ (0.5, 1, 2, 4, 8, 12, and 16 mM) in assay buffer containing 2.5% (v/v) DMSO. Syringe 2 also optionally contained either 120 µM NADH, as product inhibitor, or 12 µM A110, as inhibitor of interest. After preincubation, the contents of both syringes were mixed in the 1:1 ratio and the reaction progress was monitored by recording NADH absorbance at 340 nm. Each co-injection resulted in 10,000 time-points spanning from t = 0 to t = 2.5 sec, stepping by ∆t = 0.25 msec. Each individual data trace (absorbance vs. time) for kinetic analysis was obtained as an average of 12 separate injections. The experiments with NAD+ alone were replicated four times, on separate days, starting from fresh stock solutions in each daily session. The experiments with added NADH were replicated twice, as part of sessions 1 and 2. The experiments with added A110 were also replicated twice, as part of sessions 3 and 4. Thus the combined stopped-flow data set consists of 6,720,000 raw data points, resulting in 560,000 averaged (n = 12) time points, organized into 56 kinetic traces (eight replicated series each containing seven kinetic traces observed at [NAD+ ] = 0.25 – 8 mM). Each combined group of kinetic traces, obtained while simultaneously varying the concentrations of NAD+ , NADH, and A110, was subjected to global regression analysis (19). The nonlinear least-squares regression model for individual kinetic traces is defined by Eqn (1), where A is the absorbance (in mOD, 10−3 absorbance units) at 340 nm at reaction time t > 0.004 sec; A0 is the 5 ACS Paragon Plus Environment

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adjustable baseline offset at t0 = 0.004 sec, essentially a property of the instrument; rQ = 6.22 mOD/µM is the molar response coefficient of NADH; [Q] is the concentration of NADH at time t; and [EPQ] is the concentration of the ternary complex E-P.Q. It was assumed that the UV/Vis extinction coefficient of NADH (6.22 × 103 mM−1 cm−1 , (20)) does not change upon binding to the enzyme and therefore the extinction coefficients of NADH and the enzyme complex are exactly identical.

A = A0 + rQ ([Q] + [EPQ])

(1)

The concentrations of both UV/Vis detectable molecular species ([Q] and [EPQ]) at time t were computed from their initial concentrations at time zero by numerically solving an initialvalue problem defined by a system of differential equations (S1)–(S12) (Supporting Information). The numerical solution algorithm was the Livermore Solver of ODE Systems (LSODE) (21, 22). The absolute global truncation error tolerance was 10−14 µM; the relative global truncation error was 10−8 (eight significant digits). For further details regarding data handling and analysis see Supporting Information. Steady-state initial rate kinetics Steady state kinetics experiments were performed by measuring initial velocities at varying concentrations of NAD+ , NADH, and A110, by monitoring the production of NADH by absorbance at 340 nm (² = 6.22 mM−1 cm−1 ) using Hitachi U-2000, or Shimadzu UV-2600 spectrophotometer. All measurements were performed in the assay buffer (50 mM Tris, 150 mM KCl, 1 mM DTT, pH 8.0) at 25◦ C with saturating concentration of IMP (1 mM) and 14 nM enzyme (nominal concentration) in a total of 1 ml assay volume in 1 cm path length cuvettes. Initial rates were determined by either linear or exponential fit of the first five minutes of the assay (17 time points stepping by 20 seconds). Steady-state initial rate data, obtained while simultaneously varying the concentrations of NAD+ , NADH, and A110, were combined into a single multi-dimensional data set and subjected 6 ACS Paragon Plus Environment

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to global regression analysis (19). The fitting model is represented by Eqn (2), in which square brackets with lower index zero represent total or analytic concentrations of reactants; v0 is the reaction rate observed in the absence of inhibitors; and Ki∗ is the apparent inhibition constant corresponding to the given kinetic mechanism; see Eqns (S25)–(S33), (S41) and (S42) for definitions of both v0 and Ki∗ corresponding to the kinetic mechanism in Scheme 2. Note that at full IMP saturation, [E]0 stands for the total concentration of E·IMP complex. q v = v0

[E]0 − [I]0 −

Ki∗

+

([E]0 − [I]0 − Ki∗ )2 + 4 [E]0 Ki∗ 2 [E]0

(2)

For further details regarding data handling and analysis see Supporting Information.

Data Analysis Nonlinear least-squares regression analysis was performed by using a custom implementation of the Levenberg-Marquardt algorithm (23, 24). For verification, all regression analyses were repeated by using the hybrid Trust-Region data fitting method (25–27) (algorithm NL2SOL, version 2.3) with user-supplied Jacobian matrix of first derivatives. Initial estimates of microscopic rate constants were discovered with the aid the Differential Evolution global minimization algorithm (24, 28). For further details regarding initial estimates see Supporting Information, section 2.2, pp. 7–8. Asymmetric confidence interval for adjustable regression parameters (kinetic constants, initial concentrations, and offset on the signal axis) were determined by using the profile-t search method of Bates & Watts (29–31). In the case of steady-state initial rates, the confidence level for marginal confidence intervals of model parameters (as opposed to joint confidence regions, which were not evaluated) was 95%. However, in the case of stopped-flow pre-steady state kinetics data, the individual times points are not statistically independent. Therefore the critical value of the residual sum of squares was chosen by using the empirical approach advocated by Johnson (32– 34). According to this method, the parameter space is searched until the best-fit residual sum of


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squares increases by a reasonably large percentage of its best-fit value. All analyses reported here used ∆SSQ = 5%. All data analyses were performed by using the software package DynaFit (24, 35). The DynaFit input script files (model specification, initial values of nonlinear regression parameters, and the method of analysis) are listed in full in the Supporting Information.

Results and Discussion Stopped-flow kinetics We performed eight replicated series of stopped-flow experiments in four separate daily sessions. We used a CBS subdomain deleted variant of BaIMPDH∆L (the same as the crystal structure (15)). Enzyme (nominal concentration 4 µM) was preincubated with fixed saturating concentrations of IMP (1 mM; Km = 70 µM) and mixed with varied concentrations of NAD+ (0.25 – 8 mM) in the presence or absence of NADH (60 µM) or A110 (6 µM). Representative data are shown in Figure 1 and Supplementary Figure S2. Raw data files are available in the Supporting Information. Minimal kinetic mechanism For the purposes of this report, a “minimal” kinetic mechanism is defined as one where all individual steps and the associated microscopic rate constants – or at least their limiting values ( the lower or upper bounds) – are fully determined by the available experimental data. In contrast, a “redundant” kinetic mechanism contains rate constants that are assumed to exist on the basis of external evidence but are not directly supported by experimental data under consideration. In this work we set out to identify the minimal kinetic mechanism for the available stopped-flow transient kinetic data and the best-fit values, or at least the lower or upper bounds, for all microscopic rate constants appearing in the minimal mechanism. The starting point for the development of a minimal kinetic model is the redundant mechanism displayed in Scheme 1. The naming scheme for substrates (A, B) and products (P, Q) follows 8 ACS Paragon Plus Environment

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IUB/IUPAC recommendations (36) as well as conventions commonly used in classic enzyme kinetic texts (37). The dash in E-P represents the covalent intermediate. This reaction scheme is based on numerous previously published reports (for review cf. (8)). The covalent intermediate species E-P is proposed to exist in two distinct conformations (“open” and “closed”, (8)) which are assumed to interconvert essentially instantaneously on the time scale of the experiment. E-P.B E.B

k1'

k7 k-7

+A

+B

k-2'

k-1'

k2' k3

E

E.A.B

+A

k-1

+B

k2 k1

k5

k4 E-P

E-P.Q k-3

+Q

k-4

k-2 +I

k-8

k8

k-9 A B P Q I

IMP NAD+ XMP NADH inhibitor

k9

E-P.I

E +P

+I

+B

E.A

H2O

k6 E.P +I

k-10

k10

E.P.I

k-6 +I

k-11 k11 E.I

E.A.I

Scheme 1

By using the trial-and-error approach, eliminating either one microscopic step at a time or groups of microscopic steps until all remaining rate constants were fully defined by the data, the redundant kinetic mechanism in Scheme 1 was reduced to the minimal kinetic mechanism in Scheme 2. The tilde symbol in the species name signifies that E˜ P could be either the covalent intermediate on the reaction pathway or the noncovalent enzyme–product complex. The stopped-flow transient kinetic experiment does not provide sufficient information to distinguish between the two scenarios. Under IMP saturating conditions, the concentration of free enzyme E is by definition zero and therefore grayed segment in upper left corner of Scheme 2 is not operational. Solid black arrows with rate constant names appended to them represent those microscopic rate constants that can 9 ACS Paragon Plus Environment

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A B P Q I

E.B

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IMP NAD+ XMP NADH inhibitor

E~P.B k7 k-7 +B

k3 E

k4

E.A.B k2

E~P

E-P.Q k-3

+Q

k-4 +I

+B

k-2

k-9

E.A

k5 E~P.I

+I

k-8

k9

k8

+A saturating

P

E.A.I

Scheme 2

be unambiguously determined from the stopped-flow transient kinetic data, not only in terms of their best-fit values, but also including both the lower and the upper bounds. The four dashed arrows represent those microscopic rate constants (k4 , k−4 , k7 , and k−7 ), for which only the lower limit can be determined but not the upper limit. Rate constants k4 and k−4 represents product inhibition by NADH, whereas rate constants k7 and k−7 represent substrate inhibition by NAD+ . Both steps can be characterized as taking place with instantaneous equilibration (rapid equilibrium approximation). The equilibrium dissociation constants for these two steps are well defined by the data. Figure 1 displays seven of 21 kinetic traces that were all analyzed as a single global unit (19). The complete set of 21 traces is shown in Figure S2. In the particular experiment illustrated in Figure 1, NAD+ was varied in the presence of added A110. Each individual experiment involves multiple distinct phases, as evidenced by the presence of two distinct “shoulders” visible in the kinetic trace associated with the [NAD+ ] = 8 mM. The corresponding instantaneous rate plots (Figure S2, lower right panel) further support the observed multi-phasic nature of the transient data.


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Note that the enzyme and inhibitor concentrations are comparable in magnitude (“tight binding” (38)), which means that simplified pseudo-first-order approximation does not hold and therefore it would not be theoretically justified to analyze the transient kinetic data by the conventional multiexponential analysis. Instead, one must indeed resort to a global mathematical model formulated as a system of simultaneous differential equations. [NADH] = 0, [A110] = 6 µM

20 0

∆A340, mOD

40

0.25 0.5 1 2 4 6 8 mM

0.5

residuals

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0 -0.5 -1 -1.5

0.01

0.1

1

time, s

Figure 1: A representative data set from the global fit of stopped-flow transient kinetic data to the kinetic mechanism in Scheme 1. Concentrations: [IMPDH] = 4.0 µM (nominal), [IMP] = 1.0 mM, [A110] = 6 µM (nominal), [NAD+ ] see labels in Figure margin. Figure 2 shows the evolution of enzyme species concentrations corresponding to the [NAD+ ] = 1 mM kinetic trace displayed in Figure 1. Note again the highly complex shapes of the plots, essentially defying any possible use of the conventional multi-exponential analysis, as the overall rate of product formation is proportional to the instantaneous concentration of E·A·B (blue dashed curve). The two enzyme–inhibitor complexes are formed on different time scales and with different abundance at steady-state. The enzyme–substrate–inhibitor complex E·A·I is dominant during the pre-steady state phase of the experiment up to approximately t = 100 msec. The enzyme–product– inhibitor complex E˜ P·I is strongly dominant at steady state (t > 1 sec), although some amount of E·A·I also persists. This result predicts that in steady-state initial rate measurements performed under identical experimental conditions (i.e., at saturating concentrations of IMP), A110 should be 11 ACS Paragon Plus Environment

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identified as a mixed predominantly uncompetitive inhibitor. Based on the concentration plot in Figure 2, the uncompetitive inhibition constant can be predicted to be significantly smaller than its competitive inhibition constant. [NADH] = 0, [A110] = 6 µM, [NAD] = 1 mM

1

concentration,

µM

2

E.A E.A.B E~P.Q E~P E~P.B E.A.I E~P.I

0

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0.01

0.1

1

time, s

Figure 2: The evolution of enzyme species concentrations corresponding to the [NAD+ ] = 1 mM kinetic trace displayed in Figure 1. The dominant enzyme–inhibitor complex up to approximately t = 100 msec is E·A·I. However, at steady state (t > 1 sec) the dominant enzyme–inhibitor complex is E˜ P·I.

Rate constant bounds To establish the bounds on rate constants appearing in Scheme 2, the results of nonlinear regression analysis were averaged from 16 independent combinatorial replicates (see Supporting Information for details regarding combinatorial replication). Table 1 list the averages (n = 16) and the associated standard deviations from replicates for the best-fit values of rate constants and also for the corresponding lower and upper bounds evaluated by the 5% ∆SSQ according to Johnson’s empirical method (32–34). Similar results (not shown) were obtained at the more stringent 10% ∆SSQ confidence level.

The microscopic rate constants listed in Table 1 fall into four categories according to how well they are determined by the experimental data. In the first category are three of the four substrate catalytic constants pointing in the forward directions (k2 , k3 , and k5 ) and also the dissociation rate 12 ACS Paragon Plus Environment

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Table 1: Averages (n = 16) and the corresponding standard deviations of microscopic rate constants from replicates determined by the global fit of stopped-flow transient kinetic data. The lower and upper limits are asymmetric confidence intervals determined according to the method proposed by Johnson (32–34) at the 5% ∆SSQ level. See also Figures S4–S6, Supporting Information. unit k2 k−2 k3 k−3 k4 k−4 k5 k7 k−7 k8 k−8 k9 k−9

µM−1 s−1 s−1 s−1 s−1 s−1 µM−1 s−1 s−1 µM−1 s−1 s−1 µM−1 s−1 s−1 µM−1 s−1 s−1

best-fit 0.034 13 91 37 360000 3500 14.4 0.008 44 12 12 4.1 0.264

± ± ± ± ± ± ± ± ± ± ± ± ±

lower limit

0.001 3 3 10 400000(b) 4000(b) 0.4 0.002 7 0.9 0.5 0.5 0.004

0.03 4.9 81 18 180 1.7 13.6 0.0044 24 8 9.7 2.6 0.213

± ± ± ± ± ± ± ± ± ± ± ± ±

0.001 2 3 6 40 0.4 0.5 0.0006 2 0.9 0.4 0.4 0.004

upper limit 0.04 32 110 86

± ± ± ±

0.002 7 8 40

(a) (a)

15 ± 0.4 (a) (a)

17 16 6 0.321

± ± ± ±

1 0.6 0.6 0.006

(a)

Upper limit is undefined at the 5% ∆SSQ confidence level (33). However the dissociation equilibrium constants k4 /k−4 and k7 /k−7 were invariant during the confidence interval search, see Figure S5. (b)

Coefficient of variation (%CV) greater than 100%. However the dissociation equilibrium constant k4 /k−4 is very well defined by the data across all 16 combinatorial replicates, see Figure S6.

constants for both inhibitor binding steps (k−8 and k−9 ). All five rate constants listed above are very well defined by the available data, as the coefficient of variation from replicates (n = 16) is lower than 5% in all cases. In the second category are the reverse catalytic rate constants (k−2 and k−3 ) and the inhibitor association rate constants (k8 and k9 ). These four constants are marginally less well determined, as the corresponding coefficient of variation is approximately between 10% and 25%. However, both the upper limit and the lower limit of the asymmetric confidence interval is well defined for all four rate constants. In the third category are the two microscopic rate constants that characterize the substrate


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inhibition step, k7 and k−7 . Both best-fit values are well defined, with CV < 20%. The lower limit of the confidence intervals for k7 and k−7 is also very well defined, with CV < 25%. The upper limit of the confidence interval is not defined at the 5% ∆SSQ confidence level, which means that the range of plausible values for k7 and k−7 spans from their corresponding lower limits essentially to infinity. However, all pairs of k7 and k−7 values that were located within the 5% ∆SSQ empirical confidence intervals maintained a nearly invariant ratio, k−7 /k7 = 5.5 mM (Figure S5). Thus the equilibrium dissociation constant associated with NAD+ substrate inhibition is well defined by the transient kinetic data. Finally in the fourth category are the two rate constants that characterize product inhibition by NADH, k4 and k−4 . This is the only pair of microscopic rate constants where the best-fit values are poorly defined by the data, as the coefficient of variation is greater than 100% in both cases. However the ratio of both rate constants, i.e. the corresponding dissociation equilibrium constant, remains nearly invariant across all 16 replicated measurements and it is approximately equal to Kd(EP.NH) = 100 µM. The upper limits at the 5% ∆SSQ confidence level are undefined. Only the lower limits of both k4 and k−4 are well defined, with CV < 25% (Table S4). In particular the lower limit of the NADH association rate constant is approximately k−4 > 2 µM−1 s−1 . All pairs of k4 and k−4 values that were located within the 5% ∆SSQ empirical confidence intervals maintained a nearly invariant ratio, k4 /k−4 = 100 µM (Figure S5). Thus the equilibrium dissociation constant associated with NADH product inhibition is well defined by the pre-steady state kinetic data. Even though the upper limits of the bimolecular association rate constants k−4 and k7 are not sharply defined by the available transient kinetic data, those upper limits are imposed by physical constraints, in particular by diffusion control. Theoretical calculations predict that the “diffusioncontrolled encounter frequency of an enzyme and a substrate should be about 109 M−1 s−1 ” (39, pp. 164-166). The highest experimentally observed values (39) frequently fall in the range between 106 and 108

M −1 s−1 .

The lower limit of k4 (NADH rebinding) is approximately 2 × 106

M −1 s−1 ,

which means that NADH rebinding is extremely rapid. In contrast, the lower limit of k−7 (substrate inhibition by NAD+ ) is much lower, approximately 4 × 103

M −1 s−1 .


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In summary, out of 13 microscopic rate constants that appear in Scheme 2, 11 rate constants (i.e., all except k4 and k−4 ) were determined uniquely in terms of their well-reproduced best-fit values. Additionally, nine rate constants have well defined upper and the lower limits at the 5% ∆SSQ confidence level, according to Johnson’s empirical method (33). Johnson’s ∆SSQ method certainly represents a massive improvement over the conventional and frequently meaningless “standard error” method of assessing the uncertainly of nonlinear model parameters (40, pp. 696-698, Eqn (15.6.4)). However, it should also be noted that the empirical ∆SSQ method has no basis in rigorous statistical theory (29, 41–43). Instead, the investigator must choose an arbitrary “threshold” (33) value for ∆SSQ (for example, 5%, 10%, or 25%), based entirely on subjective personal preferences. Thus, it is possible that the distinct minima clearly visible in the likelihood profiles (44) for k7 and k−7 (see Fig. S4, lower panels) do in fact represent meaningful best-fit values. Importantly, the likelihood profiles k4 and k−4 (see Fig. S4, upper panels) are perfectly flat, without even the slightest hint of a true minimum on the least-squares hypersurface. Never-the-less, to remain firmly on the safe side, in the analysis of steady-state initial rates (see below), we have chosen to treat with full confidence only the nine rate constants that have clearly defined upper and lower limits at 5% ∆SSQ. It is highly unusual that at least nine microscopic rate constants should be uniquely determined from a single globally analyzed transient kinetic data set. In fact, we are not aware of any published report to that effect, although a large number of microscopic rate constants have been previously determined from multiple independent experiments, analyzed separately. In this fashion, Benkovic et al. (45, 46) determined 22 microscopic rate constants in the catalytic mechanisms of dihydrofolate reductase. Similarly, Anderson et al. (47) determined 12 microscopic rate constants in 8 separate rapid quench kinetic experiments (see Figs. 1 – 8 in ref. (47)), each of which was focused on a particular sub-set of the overall twelve-step mechanism. The largest number of microscopic rate constants ever determined in a global fit of a single global data set is six, as reported by Schroeder at al. (48, 49). The challenges of successful model identification grow very much faster than linearly with an increase in the number of adjustable model parameters. Thus, a kinetic


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mechanism containing either nine or even 11 adjustable rate constants is significantly more difficult to establish by fitting a single data set, compared to a kinetic mechanism with six microscopic rate constants – the largest number reported so far (48, 49). Therefore, in order to stress-test our regression model in terms of reproducibility, we have performed 16 replicated regression analyses, by utilizing the combinatorial “mix-and-match” method described above. The successful determination of at least nine microscopic rate constants in the global analysis (19) of a single combined transient kinetic data set was almost certainly facilitated by the fact that the UV/Vis spectrophotometric signal was sensitive not only to the presence of the final reaction product (NADH, Q in Scheme 2), but very importantly also to the presence of the enzyme–product complex (E-P.Q in Scheme 2; see also Eqn (1)). Partially constrained model Based on the fact that only lower limits can be determined for kinetic constants that characterize either substrate inhibition by NADH (k4 and k−4 ) or product inhibition by NAD+ (k7 and k−7 ), the kinetic model shown in Scheme 2 was stress-tested in a series of regression analyses where the association rate constants k−4 and k7 were both assigned arbitrarily chosen “rapid equilibrium” values. The five arbitrarily chosen rate constant values were kon = (10, 20, 50, 100, 1000) µM−1 s−1 . The purpose was to determine to what extent (if any) the best-fit values of the remaining rate constants appearing in Scheme 2 are affected by the arbitrary choice of kon = k−4 = k7 . The results are summarized in Table S5. Within the examined range of the kon values, the best-fit values of the corresponding koff rate constants were such that the Kd = koff /kon remained invariant. Specifically for product inhibition by NADH, the dissociation equilibrium constant k4 /k−4 remained within 5% of 100 µM. Similarly for substrate inhibition by NAD+ , the dissociation equilibrium constant k−7 /k7 remained within 10% of 5.5 mM. Importantly, the remaining microscopic rate constants appearing in Scheme 2 varied by less than 5% in response to arbitrary variations in the assumed kon value spanning three orders of magnitude. Thus the ultimate values of microscopic rate constants that were subjected to


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subsequent validation by steady-state initial rate kinetic measurements are those that are listed in the right-most column of Table S5. These values assume that the rapid-equilibrium assumption is sufficiently well characterized by kon = 100 µM−1 s−1 .

Steady-state initial rate kinetics Predicted vs. observed reaction rates Steady-state initial rates were determined in four types of experiments, depending on the varied reaction component(s). In all these experiments, the substrate concentration was held fixed at the saturating concentration [IMP] = 1.0 mM. The fact that [IMP] = 1.0 mM is indeed saturating can be established by inspection of Figure S9, upper left panel. Note that two IMP saturation curves observed at [IMP] = 0.75 mM and 1.0 mM, respectively, are virtually indistinguishable. In the first type of experiment, aimed at establishing the cofactor saturation curve, [NAD+ ] was varied between 0.1 mM and 8.0 mM, in the absence of either NADH as product inhibitor or A110 as the inhibitor of interest. This experiment was performed in triplicate. In the second type of experiment, aimed at the product inhibition effect of NADH, [NAD+ ] was varied between 0.1 mM and 1.0 mM at various levels of [NADH] (0, 50, 100, and 150 µM). In the third type of experiment, aimed at the inhibition properties of A110, [NAD+ ] was varied between 0.12 mM and 1.2 mM at various levels of [A110] (0, 15, 30, 60, 120, and 180 nM). The fourth and final type of experiment was a variation on the immediately preceding experiment type and involved relatively high concentrations of [NAD+ ], varied between 1.0 mM and 8.0 mM at various levels of [A110] (0, 15, 30, 60, 90, and 150 nM). The experimental data from all four types of experiment were combined into a single superset of data and analyzed by the global fit (19) method. The nonlinear regression model was Eqn (2). The various algebraic terms that define the uninhibited rate v0 , Eqn (S41), and the apparent inhibition constant Ki∗ , Eqn (S42), are defined as shown in Eqns (S25)–(S33). In Eqns (S25)– (S33), the assumed values of all microscopic rate constant were those obtained in the stopped-flow transient kinetic study, namely: k2 = 0.0318 µM−1 s−1 ; k−2 = 10.9 s−1 ; k3 = 82.1 s−1 ; k−3 = 44.3 17 ACS Paragon Plus Environment

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s−1 ; k4 = 11500 s−1 ; k−4 = 100 µM−1 s−1 (rapid equilibrium approximation); k5 = 13.6 s−1 ; k7 = 100 µM−1 s−1 (rapid equilibrium approximation); k−7 = 566000 s−1 , k8 = 13.9 µM−1 s−1 ; k−8 = 11.0 s−1 ; k9 = 5.51 µM−1 s−1 ; and k−9 = 0.27 s−1 . Importantly, in this first round of initial rate analysis, all microscopic rate constants listed above were held fixed at values derived from the stopped-flow experiment. The only adjustable model parameter in the regression Eqn (2) was the active enzyme concentration, [E]0 . The enzyme concentration was optimized locally for each particular type of experiment, because the different steady-state experiments were performed over the period of approximately one year and thus the active enzyme concentration might have changed slightly over time. The nominal enzyme concentration was 14 nM, and the best fit values of the active-site concentration varied from 8.4 nM to 10.7 nM. Given the fact that all rate constants were held fixed in the regression and only the enzyme concentration was optimized, the highly restricted “fit” of the combined initial rate data is merely a comparison between the observed initial rates and those that are predicted by the theoretical model derived form the stopped-experiment. The results are shown in Figure 3. The full text of the requisite DynaFit input file is listed in Appendix SA.2.2; the best-fit values of [E]0 are listed in Table S6. Note that that throughout this report the reaction rates are expressed in directly observable absorbance units per second, rather than in concentration units. The main reason for this choice is that, in particular in the analysis of stopped-flow data, the molar response coefficients are generally treated as adjustable model parameter. In the specific case of Figure 3, the observed reaction rate (approximately 0.4 × 10−3 dimensionless absorbance units per second) corresponds to the chemical reaction rate of approximately 0.4/6.22 = 0.064 µM/sec in NADH formation. The results displayed graphically in Figure 3 show that the 13 microscopic rate constants determined by the stopped-flow transient kinetic measurements predict the steady-state initial rate data reasonably well. For example, the position of the maximum on the NAD+ saturation curve (approximately at [NAD+ ] = 1.5 mM), in the upper left panel, is well predicted, as is the slope of


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NAD

NAD + NADH 0.4

Q = 0 mM Q = 0.05 Q = 0.10 Q = 0.15

0.2

v, mOD/sec

0

0

0.1

0.2

v, mOD/sec

0.3

0.4

A = 0.75 mM A = 1.0 (a) A = 1.0 (b) A = 1.0 (c)

0

2000

+

4000

6000

[NAD ], µM

0

500 +

[NAD ], µM

(a)

1000

(b)

NAD (low) + A110

NAD (high) + A110

0.3

0.4

I = 0 nM I = 15 I = 30 I = 60 I = 90 I = 150

0

0

0.1

0.2

v, mOD/sec

0.4

I = 0 nM I = 15 I = 30 I = 60 I = 120 I = 180

0.2

v, mOD/sec

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0

500

[NAD+], µM

1000

0

2000

(c)

4000

[NAD+], µM

6000

(d)

Figure 3: Comparison between observed initial rates (symbols) and initial rates predicted from the theoretical model derived for the stopped-flow transient kinetic data (curves). For details see text. the downward portion due to substrate inhibition. In the upper right panel of Figure 3, there is a good agreement between the predicted (curves) and observed (symbols) product inhibition effect of NADH. The lower left panel shows the predicted vs. observed inhibition effect of A110 at relatively low concentrations of NAD+ . Again the relative spacing of the substrate saturation curves is well described by the theoretical model derived from the stopped-flow data. The same applies to the lower right-hand panel of Figure 3, displaying the inhibitory effect of A110 at relatively high concentrations of NAD+ .


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Predicted vs. observed composite kinetic constant Based on the best-fit values of microscopic rate constants determined in the stopped-transient kinetic experiments (see Table 1) and given the definition of steady-state kinetic constants as shown in Eqns (S25)–(S33), the predicted values of kinetic constants are listed in Table 2, column “predicted”. In order to validate this prediction, the combined initial rate data was again fit globally (19) to Eqn (2), this time while treating all kinetic constants (kcat , Km(B) , Ki(I) , Ki(I,B) , Ki(B) , Ki(Q) , Ki(Q,B) ) as adjustable model parameters. Table 2: Comparison of predicted and observed steady-state kinetic constants from global fit of combined initial rate data to Eqn (2), where kinetic constants are defined by Eqns (S25)–(S33). The columns labeled “low” and “high” are lower and upper limits of the asymmetric confidence interval (29, 31) at 90% likelihood level. For further details see text. # parameter 1 2 3 4 5 6 7

kcat , s−1 Km(B) , µM Ki(I) , µM Ki(I,B) , µM Ki(B) , µM Ki(Q) , µM Ki(Q,B) , µM

predicted observed ± std.err. cv,% 11.6 415 0.791 0.0572 6610 301 87.2

11.6 460 0.5 0.051 7400 620 97

± ± ± ± ± ± ±

0.2 20 0.2 0.002 400 360 9

1.8 4.0 43.6 4.3 5.3 57.7 8.9

low

high

11.2 430 0.3 0.047 6800 320 84

12.0 490 1.5 0.054 8100 4600 113

The best-fit values obtained in this second round fitting the initial rate data are shown in numerically in Table 2 and graphically in Figure S9. The results of fit in good agreement with the values predicted from the theoretical model derived the stopped-flow transient kinetic experiment. The “uncompetitive” inhibition constant Ki(I,B) predicted from stopped-flow measurements is 57 nM; the experimentally observed value from initial rate measurements was (51 ± 2) nM, with the 95% confidence level interval spanning from 47 to 54 nM. In previous reports on the kinetics of full-length BaIMPDH, the “uncompetitive” inhibition constant for A110 with respect to variable NAD+ was reported as (58 ± 4) nM (13) and (57 ± 7) nM (50). The IC50 of A110 inhibiting BaIMPDH∆L was reported as (43 ± 3) nM at 1.0 mM IMP and 1.5 mM NAD+ . The “competitive” inhibition constant Ki(I) predicted from stopped-flow measurements is 0.8 µM; the experimentally observed value from initial rate measurements is (0.5 ± 0.2) µM, with 20 ACS Paragon Plus Environment

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the 95% confidence level interval spanning from 0.3 to 1.5 µM. Thus the initial rate experiments confirm the prediction of inhibition mode based on the results of the stopped-flow experiments. A110 was predicted to behave as a mixed predominantly uncompetitive inhibitor of BaIMPDH∆L and this is in fact confirmed by initial rate measurements. Predicted vs. observed kinetic isotope effect The stopped-flow kinetic results yielded distinctly nonzero best-fit value of the E·A·B → E·A + B dissociation rate constant, k−2 , see Table 1. However, k−2 appears to be effectively zero (i.e., NAD+ is a sticky substrate) for at least some IMPDHs (51). To verify the stopped-flow kinetic result in this respect, the D (kcat /Km ) isotope effect was determined by assaying BaIMPDH∆L with saturating 1 H-IMP and 2 D-IMP. The results are reported in detail in Supporting Information, section 3.3.4. (Table S7). Briefly, the value of k−2 listed in Table 1 predicts that D (kcat /Km ) should be significantly greater than one. On the other hand if k−2 were negligibly small (“sticky” substrate) then the predicted value of D (kcat /Km ) should be by definition equal to one. The observed value is (2.4 ± 0.1). The isotope effect on kcat /Km(B) confirms that that NAD+ dissociates readily from the E·A·B complex, in agreement with the pre-steady state kinetic results. A110 plus XMP double-inhibitor experiment To determine if A110 binds to E-P or E·P, the apparent inhibition constant for A110 was determined at varying concentrations of [XMP] (0.125 – 2.0 mM) in the presence of sub-saturating concentrations of IMP (0.2 mM) and NAD+ (1.5 mM). Experimental dose-response curves were fitted to Eqn (2), where [E]0 was held fixed at 10 nM while v0 and Ki∗ were treated as optimized model parameters. The raw experimental data are shown in Figure 4A. The best-fit values of the apparent inhibition constant at various XMP concentrations and the associated formal standard errors from nonlinear regression are listed in Table S8. The apparent inhibition constant increased approximately linearly with an increase in the product concentration (Figure 4B). This observa21 ACS Paragon Plus Environment

Biochemistry

tion suggests that E·XMP does not form a high affinity complex of A110. The observed changes in Ki∗ with [P] were subjected to weighted linear regression analysis. The results of linear fit are shown graphically in Figure 4B. The dimensionless slope of the linear regression line was (7.0 ± 0.6) × 10−5 ; the intercept representing Ki∗ at [P] = 0 was (137 ± 6) nM. A110 : Ki

(app)

A110 : Ki(app) vs. [XMP]

vs. [XMP]

0.3

0

0

0.2

0.1

0.2

Ki(app), µM

0.6

0.8

P = 0 uM P = 125 P = 250 P = 500 P = 1000 P = 2000

0.4

v, mOD/sec

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0

0.1

0

0.2

[I], µM

(a)

1000

[XMP], µM

2000

(b)

Figure 4: A110 plus XMP double-inhibitor experiment. (a) Determination of the apparent inhibition constant for A110 at various fixed concentrations of [XMP]. (b) Linear least squares fit of experimentally observed values of Ki∗ vs. [P]. Figure S14 shows the result of a heuristic simulation designed to allow an interpretation of the experimentally observed dependence. The simulation results can be summarized as follows. If the binding affinity of A110 toward the noncovalent intermediate E·P were greater than the binding affinity toward the covalent complex E-P, the plot of Ki∗ vs. [P] would be sloping downward. If both binding affinities were nearly identical, the plot would be approximately horizontal. Finally, if the binding affinity of A110 toward the covalent intermediate E-P were dominant, the plot of Ki∗ vs. [P] would be sloping upward. The experimentally observed plot has an upward slope, signifying that A110 does not bind predominantly to the noncovalent product complex but instead binds preferentially to the covalent intermediate. The mathematical details are are explained in Supporting Information, section 3.3.5. A very approximate estimate of the ratio of the two relevant inhibition constants suggests that the binding to E·P might be at least an order of magnitude weaker than the binding to E-P. 22 ACS Paragon Plus Environment

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Conclusions IMPDH controls the guanine nucleotide pool, and thus proliferation, in virtually every organism. Human IMPDH inhibitors are used as immunosuppressive, antiviral and anticancer therapy, and microbial IMPDHs have emerged as potential drug targets. Prokaryotic and eukaryotic IMPDHs bind NAD+ in distinctive sites that recognize very different cofactor conformations (15). This difference has been exploited to develop selective inhibitors of CpIMPDH in six different frameworks. The cofactor binding site of CpIMPDH is very similar to that found IMPDHs from from many pathogenic bacteria, including B. anthracis, Campylobacter jejuni, C. perfringes, Streptococus pyogenes, Heliobacter pylori and M. tuberculosis (5). Despite this similarity, the affinity of the inhibitors for these enzymes can vary by 100-1000-fold. Structures have been solved of several inhibitors bound to E·IMP complexes, but these structures do not reveal the basis for this surprising variation. Here we have delineated the kinetic mechanism of one representative inhibitor, A110, as shown in Scheme 3, which displays the dominant inhibitor binding mode. IMP O NAD+

H

NADH N

HN N

N

HN

N R

N

Cys S

Cys-S

XMP O

E-XMP* O H2O

N R

O

E

N

HN N

N R

Cys SH

E

E

A110

O

O N N

HN

N R

N

Cys S E

O

N N N

• Cl

E-XMP* • A110

Scheme 3

According to Scheme 3, this compound binds preferentially to the covalent intermediate. Thus 23 ACS Paragon Plus Environment

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the crystal structures do not represent the high affinity enzyme-inhibitor complex, which may explain why they do not provide insight into the varied spectrum of IMPDH inhibitors.

Acknowledgement The authors thank Cynthia Tung for assistance with the synthesis of A110.

Supporting Information Available The following files are available free of charge: (1) BaIMPDHdL-A110-transient-SI.PDF: Description of mathematical and statistical procedures (PDF format); (2) BaIMPDHdL-A110-transient-DynaFit.ZIP: Raw experimental data (CSV text format) and DynaFit input scripts (ASCII text format). This material is available free of charge via the Internet at http://pubs.acs.org/.

References 1. WHO, Antimicrobial resistance:

Global report on surveillance. http://bit.ly/

1hV6O7E, 2014; [Online; accessed 04-Sep-2015]. 2. Fischbach, M. A., and Walsh, C. T. (2009) Antibiotics for emerging pathogens. Science 325, 1089–1093. 3. Bush, K. et al. (2011) Tackling antibiotic resistance. Nat. Rev. Microbiol. 9, 894–896. 4. Sarkar-Tyson, M., and Atkins, H. S. (2011) Antimicrobials for bacterial bioterrorism agents. Future Microbiol. 6, 667–676.


Page 24 of 30

Page 25 of 30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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5. Gollapalli, D. R., MacPherson, I. S., Liechti, G., Gorla, S. K., Goldberg, J. B., and Hedstrom, L. (2010) Structural determinants of inhibitor selectivity in prokaryotic IMP dehydrogenases. Chem. Biol. 17, 1084–1091. 6. Hedstrom, L., Liechti, G., Goldberg, J. B., and Gollapalli, D. R. (2011) The antibiotic potential of prokaryotic IMP dehydrogenase inhibitors. Curr. Med. Chem. 18, 1909–1918. 7. Jackson, R. C., Weber, G., and Morris, H. P. (1975) IMP dehydrogenase, an enzyme linked with proliferation and malignancy. Nature 256, 331–333. 8. Hedstrom, L. (2009) IMP Dehydrogenase: structure, mechanism, and inhibition. Chem. Rev. 109, 2903–2928. 9. Gorla, S. K., Kavitha, M., Zhang, M., Liu, X., Sharling, L., Gollapalli, D. R., Striepen, B., Hedstrom, L., and Cuny, G. D. (2012) Selective and potent urea inhibitors of Cryptosporidium parvum inosine 5’-monophosphate dehydrogenase. J. Med. Chem. 55, 7759–7771. 10. Gorla, S. K., Kavitha, M., Zhang, M., Chin, J. E., Liu, X., Striepen, B., MakowskaGrzyska, M., Kim, Y., Joachimiak, A., Hedstrom, L., and Cuny, G. D. (2013) Optimization of benzoxazole-based inhibitors of Cryptosporidium parvum inosine 5’-monophosphate dehydrogenase. J. Med. Chem. 56, 4028–4043. 11. Johnson, C. R., Gorla, S. K., Kavitha, M., Zhang, M., Liu, X., Striepen, B., Mead, J. R., Cuny, G. D., and Hedstrom, L. (2013) Phthalazinone inhibitors of inosine-5’-monophosphate dehydrogenase from Cryptosporidium parvum. Bioorg. Med. Chem. Lett. 23, 1004–1007. 12. Gorla, S. K., McNair, N. N., Yang, G., Gao, S., Hu, M., Jala, V. R., Haribabu, B., Striepen, B., Cuny, G. D., Mead, J. R., and Hedstrom, L. (2014) Validation of IMP dehydrogenase inhibitors in a mouse model of cryptosporidiosis. Antimicrob. Agents Chemother. 58, 1603– 1614.


Biochemistry

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

13. Mandapati, K., Gorla, S. K., House, A. L., McKenney, E. S., Zhang, M., Rao, S. N., Gollapalli, D. R., Mann, B. J., Goldberg, J. B., Cuny, G. D., Glomski, I. J., and Hedstrom, L. (2014) Repurposing Cryptosporidium inosine 5’-monophosphate dehydrogenase inhibitors as potential antibacterial agents. ACS Med. Chem. Lett. 5, 846–850. 14. Kirubakaran, S., Gorla, S. K., Sharling, L., Zhang, M., Liu, X., Ray, S. S., MacPherson, I. S., Striepen, B., Hedstrom, L., and Cuny, G. D. (2012) Structure-activity relationship study of selective benzimidazole-based inhibitors of Cryptosporidium parvum IMPDH. Bioorg. Med. Chem. Lett. 22, 1985–1988. 15. Makowska-Grzyska, M., Kim, Y., Maltseva, N., Osipiuk, J., Gu, M., Zhang, M., Mandapati, K., Gollapalli, D. R., Gorla, S. K., Hedstrom, L., and Joachimiak, A. (2015) A novel cofactor-binding mode in bacterial IMP dehydrogenases explains inhibitor selectivity. J. Biol. Chem. 290, 5893–5911. 16. MacPherson, I. S., Kirubakaran, S., Gorla, S. K., Riera, T. V., D’Aquino, J. A., Zhang, M., Cuny, G. D., and Hedstrom, L. (2010) The structural basis of Cryptosporidium-specific IMP dehydrogenase inhibitor selectivity. J. Am. Chem. Soc. 132, 1230–1231. 17. Maurya, S. K., Gollapalli, D. R., Kirubakaran, S., Zhang, M., Johnson, C. R., Benjamin, N. N., Hedstrom, L., and Cuny, G. D. (2009) Triazole inhibitors of Cryptosporidium parvum inosine 5’-monophosphate dehydrogenase. J. Med. Chem. 52, 4623–4630. 18. Wang, W., Papov, V. V., Minakawa, N., Matsuda, A., Biemann, K., and Hedstrom, L. (1996) Inactivation of inosine 5’-monophosphate dehydrogenase by the antiviral agent 5-ethynyl1-beta-D-ribofuranosylimidazole-4-carboxamide 5’-monophosphate. Biochemistry 35, 95– 101. 19. Beechem, J. M. (1992) Global analysis of biochemical and biophysical data. Meth. Enzymol. 210, 37–54.


Page 26 of 30

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Biochemistry

20. Horecker, B. L., and Kornberg, A. (1948) The extinction coefficients of the reduced band of pyridine nucleotides. J. Biol. Chem. 175, 385–390. 21. Hindmarsh, A. C. (1980) LSODE and LSODI, two new initial value ordinary differential equation solvers. ACM SIGNUM Newslett. 15, 10–11. 22. Hindmarsh, A. C. In Scientific Computing; Stepleman, R. S., Carver, M., Peskin, R., Ames, W. F., and Vichnevetsky, R., Eds.; North Holland: Amsterdam, 1983; pp 55–64. 23. Marquardt, D. W. (1963) An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11, 431–441. 24. Kuzmiˇc, P. (2009) DynaFit - A software package for enzymology. Meth. Enzymol. 467, 247– 280. 25. Dennis, J. E., Gay, D. M., and Welsch, R. E. (1981) An adaptive nonlinear least-squares algorithm. ACM Trans. Math. Software 348–368. 26. Dennis, J. E., Gay, D. M., and Welsch, R. E. (1981) Algorithm 573: NL2SOL. ACM Trans. Math. Software 7, 369–383. 27. Dennis, J. E., and Schnabel, R. B. Numerical Methods for Unconstrained Optimization and Nonlinear Equations; Prentice-Hall: Upper Saddle River, NJ, 1983. 28. Price, K. V., Storm, R. M., and Lampinen, J. A. Differential Evolution - A Practical Approach to Global Optimization; Springer Verlag: Berlin - Heidelberg, 2005. 29. Bates, D. M., and Watts, D. G. Nonlinear Regression Analysis and its Applications; Wiley: New York, 1988. 30. Brooks, I., Watts, D., Soneson, K., and Hensley, P. (1994) Determining confidence intervals for parameters derived from analysis of equilibrium analytical ultracentrifugation data. Meth. Enzymol. 240, 459–78. 27 ACS Paragon Plus Environment

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31. Watts, D. G. (1994) Parameter estimation from nonlinear models. Methods Enzymol. 240, 24–36. 32. Johnson, K. A., Simpson, Z. B., and Blom, T. (2009) Global Kinetic Explorer: A new computer program for dynamic simulation and fitting of kinetic data. Anal. Biochem. 387, 20–29. 33. Johnson, K. A., Simpson, Z. B., and Blom, T. (2009) FitSpace Explorer: An algorithm to evaluate multidimensional parameter space in fitting kinetic data. Anal. Biochem. 387, 30– 41. 34. Johnson, K. A. (2009) Fitting enzyme kinetic data with KinTek Global Kinetic Explorer. Meth. Enzymol. 267, 601–626. 35. Kuzmiˇc, P. (1996) Program DYNAFIT for the analysis of enzyme kinetic data: Application to HIV proteinase. Anal. Biochem. 237, 260–273. 36. Nomenclature Committee of the International Union of Biochemistry, (1983) Symbolism and terminology in enzyme kinetics. Biochem. J. 213, 561–571. 37. Segel, I. H. Enzyme Kinetics; Wiley: New York, 1975. 38. Morrison, J. F. (1969) Kinetics of the reversible inhibition of enzyme-catalysed reactions by tight-binding inhibitors. Biochim. Biophys. Acta 185, 269–286. 39. Fersht, A. Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding, 3rd ed.; W. H. Freeman: New York, 1999. 40. Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. Numerical Recipes in C; Cambridge University Press: Cambridge, 1992. 41. Rawlings, J. O. Applied Regression Analysis: A Research Tool; Wadsworth Inc.: Pacific Grove, California, 1988. 42. Seber, G. A. F., and Wild, C. J. Nonlinear Regression; Wiley-Interscience: New York, 1989. 28 ACS Paragon Plus Environment

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Biochemistry

43. Rawlings, J. O., Pantula, S. G., and Dickey, D. A. Applied Regression Analysis: A Research Tool, 2nd ed.; Springer-Verlag: New York, 1998. 44. Raue, A., Kreutz, C., Maiwald, T., Bachmann, J., Schilling, M., Klingmüller, U., and Timmer, J. (2009) Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics 25, 1923–1929. 45. Benkovic, S. J., Fierke, C. A., and Naylor, A. M. (1988) Insights into enzyme function from studies of mutants of dihydrofolate reductase. Science 239, 1105–1110. 46. Fierke, C. A., Johnson, K. A., and Benkovic, S. J. (1987) Construction and evaluation of the kinetic scheme associated with dihydrofolate reductase from Escherichia coli. Biochemistry 26, 4085–4092. 47. Anderson, K. S., Sikorski, J. A., and Johnson, K. A. (1988) A tetrahedral intermediate in the EPSP synthase reaction observed by rapid quench kinetics. Biochemistry 27, 7395–7406. 48. Schroeder, G. K., Johnson, W. H., Huddleston, J. P., Serrano, H., Johnson, K. A., and Whitman, C. P. (2012) Reaction of cis-3-chloroacrylic acid dehalogenase with an allene substrate, 2,3-butadienoate: Hydration via an enamine. J. Am. Chem. Soc. 134, 293–304. 49. Huddleston, J. P., Schroeder, G. K., Johnson, K. A., and Whitman, C. P. (2012) A pre-steady state kinetic analysis of the αY60W mutant of trans-3-chloroacrylic acid dehalogenase: Implications for the mechanism of the wild-type enzyme. Biochemistry 51, 9420–9435. 50. Makowska-Grzyska, M. et al. (2012) Bacillus anthracis inosine 5’-monophosphate dehydrogenase in action: The first bacterial series of structures of phosphate ion-, substrate-, and product-bound complexes. Biochemistry 51, 6148–63. 51. Riera, T. V., Wang, W., Josephine, H. R., and Hedstrom, L. (2008) A kinetic alignment of orthologous inosine-5’-monophosphate dehydrogenases. Biochemistry 47, 8689–8696.


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