X-Ray Crystallographic Analyses of the Antimicrobial Resistant Enzymes β-lactamases and Aminoglycoside Phosphotransferases

Matthew Au

Office of Science, Science Undergraduate Laboratory Internship (SULI)

University of California: Merced

Stanford Linear Accelerator Center (SLAC)

Menlo Park, CA

August 12, 2012

Prepared in partial fulfillment of the requirements of the Office of Science, Department of

Energy’s Science Undergraduate Laboratory Internship under the direction of Clyde Smith in the

Basic Sciences Division at the Stanford Linear Accelerator Center.

Participant: Signature

Research Advisor: Signature

Table of Contents

Abstract 3

Introduction 4

Materials & Methods 6

Results 10

Discussion & Conclusion 12

Acknowledgements 13

References 14

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Abstract

X-Ray Crystallographic Analyses of the Antimicrobial Resistant Enzymes β-lactamases and

Aminoglycoside Phosphotransferases. MATTHEW AU (University of California, Merced, CA

94116) CLYDE SMITH (Stanford Linear Accelerator Center, Menlo Park, CA 94025)

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Introduction

Long before the discovery of antibiotics 80 years ago, bacteria have been using similar

molecules as a type of “chemical warfare” against other bacteria, and have also developed

defense mechanisms against the “weapons” used by enemy bacteria, primarily catalytic enzymes

capable of destroying or neutralizing the chemical “weapons” employed in these ancient battles.

The discovery of penicillin in 1928 was a breakthrough in the treatment of the easily-contracted

infectious diseases, only to be rendered useless a few years later by these defense mechanisms of

the ever-evolving bacteria. Since then, scientists and bacteria have been playing an endless game

of tag. This chase has produced thousands of different antibiotics, over half of which many

strains of bacteria have now developed resistance to. Apart from reducing the dosage of

antibiotics and similar small steps in an effort to win this battle, scientists must understand

antibiotic resistance at a molecular level by looking at the structures and mechanisms of enzymes

employed by resistant bacteria.

The first group of resistant enzymes, the β-lactamases, is responsible for fighting off

antibiotics such as the pioneering antibiotic penicillins, cephalosporins, carbapenems, and

monobactams [1]. Their given name refers to their ability to target the antibiotic’s four

memebered β-lactam ring, which grants it its antibiotic properties by preventing the bacterium

from forming cell walls. The β-lactamase enzymes are thought to have evolved from enzymes

responsible for the buildup of the bacterial cell wall. They bind the drugs and hydrolyze the ring,

dismantling the ring and making the drug incapable of killing bacteria. Under the umbrella of β-

lactamases, there lie four various molecular classes based on their amino acid sequence

identities: A, B, C, and D [2]. Class A, C, and D enzymes require an active-site of serine for

catalysis, whereas class B enzymes are metal dependent [3]. Class A β-lactamases are the most

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abundant and diverse family of enzymes, which include this project’s main focus of the Guiana

Extended-Spectrum (GES) [2]. The first Guiana Extended-Spectrum was described in 2000,

GES-1, and later split into 8 other different types of GES enzymes. The GES-1 enzyme shows a

low level of carbapenemase activity compared to the other GES enzymes but however possesses

a potential to evolve into a stronger “carbapenem killer” [2][3]. The GES-1 enzyme will provide

the appropriate binding structure for our focus of the three carbapenem antibiotics: Meropenem,

Ertapenem, and Doripenem.

In addition to giving birth to the family of β-lactamases, bacteria produce the

antimicrobial resistant aminoglycoside-modifying enzymes. It is then branched out to three

major groups: aminoglycoside N-acetyltransferase (AAC), aminoglycoside O-adenyltransferase

(ANT), and aminoglycoside O-phosphotransferase (APH). From their names, each enzyme adds

its respective group (acetyl group, adenyl group, or phosphate group) to the aminoglycoside

antibiotics such as arbekacin, kanamycin, neomycin, streptomycin, gentamicin, and

paromomycin in order to deactivate them [5]. The aminoglycosides have mechanisms that

mutate the ribosomal target, reduce permeability and/or increased efflux of the drug, and

enzymatically deactivate the drug [6]. This project falls under the aminoglycoside 3’-

phosphotransferase family, more specifically the APH(2”) enzymes. Within this family, there

are other enzymes named APH(2”)-Ia, -Ib, -Ic, and -Id. However, scientists figured out these

enzymes only have a 28% - 32% amino acid sequence identity and their substrate profiles differ.

Therefore categorizing them into APH(2”)-Ia, -IIa, -IIIa, and –IVa, respectively [4]. The enzyme,

APH(2”)-IIa, confers resistance to a small subset of these aminoglycosides. These APH’s

phosphorylate by sending the medium, phosphate group, derived from ATP, to the antibiotic. A

mutant of this enzyme (R92H/D268N) has been discovered that exhibits newly-elevated

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resistance towards two additional antibiotics (amikacin and isepamicin) compared to the wild-

type enzyme.

Materials & Methods

The protein crystallization process has been grown and completed for both the

aminoglycoside-3’ phosphotransferase [APH(2”)-IIA] and the GES-1 β-lactamase enzyme. The

methodologies for the crystal structure can be found in various research studies [3][4].

Data Collection and Processing

Once the protein was crystallized, X-ray crystallography gave us the necessary data,

pictures that were taken at and per second. The GES-1 carbapenems had around 500 to 600

pictures each, which was first processed through the program of HKL2000. HKL2000 aids in

solving the unit cell by using the “Index” and “Integrating” tab, which gives us our angles (a, b,

c, and β) in our cube-like crystal:

Figure 1. This is the typical model for the unit cell. Each letter represents an angle of the cell except for

x, y, and z, which are the axes labels.

It cross-references various models in a Bravais Lattice Table to find the best match; the lower the

percentage (≈ 0.00%), the better the fit is:

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Figure 2. The Bravais Lattice Table of the Doripenem. The lowest percentage is shown in green; first

green is the reference. Primitive monoclinic space group fits for Doripenem.

The output file should be “Scaled” to a .sca format and converted to .mtz to find out the space

group.

CCP4i, the next program, contains the programs MolRep (Molecular Replacement) and

RefMac5 (Refinement Molecule) which brings the quantitative data into a more visual and

graphics-integrated model of the molecule with the assist of Coot. MolRep applies an already

known protein structure from the PDB (Protein Data Base) to the newly constructed GES-1

carbapenem and isepemicin aminoglycoside bound structures. The electron densities should

match up with amino acid side chains and the main chain. We manually check over 2000

specific bonds and molecules for any errors of the resistant enzymes first before adding on the

antibiotics in order to avoid inaccurate placements. A couple of factors are taken into

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consideration while modifying and refining include isomer (same molecule but atoms are in

different positions) and determining if water/magnesium/chloride molecules are significant or

not. This is done at around 1.00 rmsd, sigma; the lower the sigma, the larger the electron density

cloud gets:

(a) (b) (c)

Figure 3. These are the electron densities shown through the program of Coot. The blue clouds represent

the actual electron density and the yellow lines are the enzyme structure. (a) represents the electron

density rmsd at 1.60; (b) represents the electron density rmsd at 1.00; (c) represents the electron density

rmsd at 0.40.

After a few rounds of refinement (matching the enzyme in the blue electron density as best as

possible), the R-free and R-work should drop around 0.2000 and almost equalize. The final and

most useful program is Phenix (Python-based Hierarchical Environment for Integrated

Xtallography), which acts as a fine-tune system to help you center everything in the blue electron

density within a reasonable distance. After the R-free and R-work are at a desired number, the

refinement is run again but anisotropically: without waters.

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Figure 4. This is the overall results of the Phenix refinement program. It provides the Resolution, R-

work, R-free, RMS(bonds), RMS(angles), and Ramachandran which will be useful for our data.

Addition of the antibiotics

When comfortable with the enzyme that was built, HIC-Up provides the correct protein

data base for each of the antibiotics [7]. The structure is loaded along with a .cif file, which

allows the modifier to alter the bond angles and types. In case the drug is an isomer of the

provided electron density, ccp4i provides a monomer library sketcher that matches the correct

configuration. For the GES-1 carbapenems, the active site for where the inhibitor is bound is at

an oxygen on Serine 64 to the carbon C7 atom of the meropenem, doripenem, or the ertapenem.

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Figure 5. Addition of Ertapenem in yellow. Light blue line on the right represents SER64, the binding

site. The green electron density signifies a different conformation.

Results

GES-1 Carbapenems

Table 1. Data Collection Statistics for GES-1 Carbapenems

Doripenem Ertapenem Meropenem

Maximum resolution (dmin) (Å) 1.339 1.457 1.463

Unit cell dimensions

a (Å) 43.01 43.04 42.94

b (Å) 81.28 81.11 81.24

c (Å) 71.89 72.24 71.93

(°)

Space Group

102.25

P2 1

102.34

P2 1

102.0

P2 1

Observed reflections 507,305 1,190,305 879,901

Unique reflections to dmin 108,460 83,892 84,243

Rmerge a (%)

I/I 10.0 11.8 11.4

Completeness (%)

Redundancy 2.6 2.7 4.2a Rmerge = |I - <I>| / I x 100, where I = the observed intensity and <I> is the mean intensity.

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Table 2. Structure Refinement Statistics for GES-1 Carbapenems

Doripenem Ertapenem Meropenem

Resolution range (Å) 28.5 – 1.3 30.7 – 1.4 29.2 – 1.4

Rwork (%) 0.1559 0.1721 0.1536

Rfree (%) 0.2019 0.2065 0.1876

Rall (%) b

rms deviation from ideality

- bonds (Å) 0.006 0.006 0.006

- angles (Å) 1.39 1.33 1.28

Ramachandran plot

- residues in favored regions(%) 98.3 98.3 98.3b Calculated using all reflections (working and free).

APH(2”)-IIa Mutant

Table 3. Data Collection Statistics for APH(2”)-IIa R92H/D268N mutant

R92H/D268N mutant

Maximum resolution (dmin) (Å) 1.219

Unit cell dimensions

a (Å) 97.63

b (Å) 103.93

c (Å) 70.70

β (°)

Space Group

102.99

C2

Observed reflections 3,339,714

Unique reflections to dmin 203,457

Rmerge a (%)

I/I 13.4

Completeness (%)

Redundancy 3.0

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Table 4. Structure Refinement Statistics for APH(2”)-IIa R92H/D268N mutant

R92H/D268N mutant

Resolution range (Å) 20.0 – 1.89

Rwork (%) 0.1624

Rfree (%) 0.1939

Rall (%) b

rms deviation from ideality

- bonds (Å) 0.005

- angles (Å) 1.099

Ramachandran plot

- residues in favored regions 98.5 %

Discussion & Conclusion

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Acknowledgements

This research was conducted at the Stanford Linear Accelerator Center. I would like to

express my gratitude to my mentor, Clyde Smith, for sharing his expertise, patience, and

attention during my short time here. Thank you also to the U.S. Department of Energy for the

unique opportunity of participating in the SULI program, one of the most useful learning

experiences I’ve ever been privileged to take part in.

References

[1] Fred C. Tenover, “Mechanisms of Antimicrobial Resistance in Bacteria”, The American Journal of Medicine, Vol. 119, (6A), S3-S10, Mar. 2006

[2] Jan Walther-Rasmussen, Niels Hoiby, “Class A carbapenemases”, Journal of Antimicrobial Chemotherapy, Vol. 60, 470-482, Jun. 2007

[3] C. A. Smith, M. Caccamo, K. A. Kantardjieff, and S. Vakulenko, “Structure of GES-1 at atomic resolution: insights into the evolution of carbapenemase activity in the class A extended-spectrum β-lactamases”, Acta Crystallographica D, Vol. 63, Part 9, pp 982-992, 2007

[4] Marta Toth, Hilary Frase, Joseph W. Chow, Clyde Smith, and Sergei B. Vakulenko, “Mutant APH(2”)-IIa Enzymes with Increased Activity against Amikacin and Isepamicin”, Antimicrobial Agents and Chemotherapy, Vol. 54, No.4, 1590-1595, Jan. 2010

[5] Gerard D Wright, “Aminoglycoside-modifying enzymes”, Antimicrobial Research Centre, 499- 503, 1999

[6] Paul G Young, Rupa Walanj, Vendula Lakshmi, Laura J. Byrnes, Peter Metcalf, Edward N. Baker, Sergei B. Vakulenko, and Clyde Smith, “The Crystal Structures of Substrate and Nucleotide Complexes of Enterococcus faecium Aminoglycoside-2” – Phosphotransferase-IIa [APH(2”)-IIa] Provide Insights into Substrate Selectivity in the APH(2”) Subfamily”, Journal of Bacteriology, Vol. 191, No. 13, 4133-4143, Apr. 2009

[7] Kleywegt, G.J. (2007). Crystallographic refinement of ligand complexes. Acta Cryst D63, 94-100

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