Outsmarting metallo-β-lactamases by mimicking their natural evolution

Journal of Inorganic Biochemistry 102 (2008) 2043–2051

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Outsmarting metallo-b-lactamases by mimicking their natural evolution Peter Oelschlaeger * Chemistry Department and Center for Macromolecular Modeling and Material Design, California State Polytechnic University, Pomona, 3801 West Temple Avenue, Pomona, CA 91768, United States

a r t i c l e

i n f o

Article history: Received 16 March 2008 Received in revised form 20 May 2008 Accepted 21 May 2008 Available online 28 May 2008 Keywords: Metallo-b-lactamase Zinc b-lactamase Antibiotic resistance Evolution

a b s t r a c t Metallo-b-lactamases (MBLs) confer antibiotic resistance to bacteria by hydrolyzing and thus inactivating b-lactam antibiotics. They have raised concerns due to their broad substrate spectra, the absence of clinically useful inhibitors, and their rapid dissemination. The resulting threat to public health is enhanced by their potential to evolve into even more efficient enzymes through mutation. This is based on the assumption that these enzymes are relatively novel and in the beginning of their natural evolution. Their ongoing evolution has been manifested by the isolation of improved enzyme variants from clinical isolates, and improved variants have been generated under controlled laboratory conditions. Our ability to mimic and eventually predict the evolution of MBLs will likely put us into a better position to effectively combat MBL-conferred antibiotic resistance. This review summarizes how various approaches in recent years have brought us closer to that goal. Ó 2008 Elsevier Inc. All rights reserved.

1. Introduction One important notion in antibiotic resistance is that the application and development of new antibiotics and the response of microbes to the imposed selective pressure are competing processes [1,2]. If microbes were not ‘‘attacked” by antibiotics, they would not have to ‘‘defend” themselves. Such defense mechanisms or mechanisms of antibiotic resistance include rendering the cell membrane impermeable to the antibiotic, or, if the antibiotic can enter the cell, transporting it out of the cell through efflux pumps, inactivating it, or rendering the target of the antibiotic (e.g., in the case of b-lactam antibiotics a peptidyltransferase involved in the biosynthesis of the bacterial murein sacculus) insensitive to the antibiotic through mutation [3]. Bacterial resistance toward b-lactam antibiotics is most frequently achieved through their hydrolysis and inactivation by b-lactamases [4]. Metallo-b-lactamases (MBLs) are one class of b-lactamases (class B), aside from serine b-lactamases (classes A, C, and D). The role of MBLs in antibiotic resistance [5,6], their mechanism [7], and their structure [8] have been reviewed elsewhere. They are bacterial enzymes of about 25 kDa size that exhibit an abba fold [9] (Fig. 1), now referred to as the metallo-b-lactamase fold [10]. They contain two zinc binding sites and, based on sequence similarities, they have been classified into three subclasses, B1, B2, and B3 [11]. A standard numbering scheme has been suggested [11], which will be used in this review. In B1 and B3 enzymes, Zn1 is coordinated by His116, His118, and His196, while in B2 enzymes His116 is re-

* Tel.: +1 909 869 3693; fax: +1 909 869 4344. E-mail address: [email protected] 0162-0134/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.jinorgbio.2008.05.007

placed by an asparagine [12] and (at least in CphA) no zinc ion is bound at this site [13]. Zn2 is coordinated by Asp120, Cys221, and His263 (B1 and B2 enzymes) or Asp120, His121, and His263 (B3 enzymes) [14]. In addition to these ligands, a hydroxide ion serves as a bridging ligand between the two zinc ions in binuclear MBLs. Probably this hydroxide exerts a nucleophilic attack on the amide bond of the b-lactam ring and initiates its hydrolysis [15]. This review focuses mostly on studies on MBLs of subclass B1, reflecting the fact that these are the most intensively studied. However, most of the techniques described could as well be applied to B2 and B3 enzymes. Most MBLs of subclass B1 efficiently hydrolyze penicillins, cephalosporins, and carbapenems (Fig. 2). This broad substrate spectrum may be attributed to the strong electrostatic interactions between the zinc ions and the negatively charged groups of the antibiotics and to the relatively unspecific hydrophobic interactions between a flexible loop covering the active site and hydrophobic parts of the antibiotics. Mutagenesis studies have shown that most of the zinc-ligating residues are critical for zinc binding and catalysis [16,17] and that Lys224, present in many enzymes, is critical for efficient binding of some substrates through an electrostatic interaction with the carboxyl group at C3 of penicillins and carbapenems or C4 of cephalosporins [18,19]. While mutating these and many other residues in and near the active site usually has detrimental effects on the catalytic efficiency of MBLs [20], mutating residues further away from the active site can enhance catalytic efficiency [20–24]. Such mutations can be induced by the selective pressure of antibiotics and allow pathogenic bacteria to survive at higher levels of these drugs, thereby posing a serious threat to antimicrobial chemotherapy. A well documented naturally occurring mutation that has improved the activity of the

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nucleotide change in its codon. DNA shuffling [41,46,49] combines random mutagenesis with in vitro homologous recombination in iterative cycles and is much more efficient in exploring sequence space and generating improved proteins than simple random mutagenesis. Using a random mutagenesis approach, Hall examined whether the imipenemase 1 (IMP-1) from Pseudomonas aeruginosa can evolve to increase the resistance level against imipenem (Fig. 2) and concluded that most likely it cannot [48]. In the calculation of the certainty for this conclusion (>99.9%), Hall also considered the possibility of double mutants. There may be three reasons for the fact that no improved variant was identified. First, IMP-1 is already optimized for the hydrolysis of imipenem. Second, the selection method might not have been sensitive enough to detect improved in vivo enzyme activity. This possibility is somewhat unlikely, because the potentially improved activity was not only tested by determining the minimum inhibitory concentration (MIC) of imipenem for the survival of Escherichia coli host cells harFig. 1. Three-dimensional structure of IMP-1, a B1 MBL. a-Helices are shown as red and yellow ribbons, b-sheets as cyan ribbons, and loops as grey tubes. In the active site, the two zinc ions are shown as grey spheres and the zinc-ligating residues are shown as blue sticks. The image was generated with MOLMOL [106].

Penicillins

Cephalosporins H2N

S N

MBL IMP-6 toward charged cephalosporins, penicillins, and the carbapenem imipenem is G262S, which gives rise to IMP-1 [21]. IMP-6 itself is separated from IMP-3 by only one mutation, G126E, but the substrate spectra of these two enzymes are comparable [21]. Overall, nineteen IMP variants, designated IMP-1 to IMP-19, have been described to date [6,21,25–33]. Many of them are quite distantly related and comprehensive substrate profiles have only been reported for a few, often using different experimental conditions [21,22,25,26]. Another B1 group that exhibits a similar number of variants is that of the VIM enzymes. Twelve VIM variants have been described to date, designated VIM-1 to VIM-16 [6,34–40,109]. Genes encoding the IMP and VIM enzymes are found on mobile genetic elements, which facilitate their horizontal transference between different genera and species. The large number of IMP and VIM variants that have been isolated from clinical settings suggests that MBL evolution is going on in real time, as has been described for serine b-lactamases [1]. Fortunately, recent advances have enabled us to anticipate some of these mutations and may give us a head start in the battle against antibiotic resistance. These approaches will be discussed in the following sections. 2. Mimicking the natural evolution of MBLs through directed evolution The most straight-forward way to mimic the natural evolution of MBLs may seem to simulate evolution in the laboratory. Directed evolution has become a very powerful and popular technique for protein engineers to improve proteins such as antibodies [41,42], biocatalysts [43–45], and also to study the potential evolution of serine [46,47] and metallo-b-lactamases [24,48]. In this approach, the rate at which nucleotide misincorporation occurs naturally (1  10 6 to 1  10 10) is increased to about 1  10 3 or about one to two misincorporations per gene using error-prone polymerase chain reaction (PCR) or mutator strain host cells, thus generating libraries of randomly mutated genes. These genes are then expressed and those gene products that confer advantageous properties are selected (Fig. 3). Since the misincorporation rate is usually about one in a thousand, it is highly unlikely to have two or even three misincorporations next to each other in the same codon. Therefore, in both natural and directed evolution, a given amino acid is usually only mutated to those few that result from a one-

O O Ceftazidime N 1 COO- HN 7 6 S 2

Benzyl-

O penicillin HN 6 5 S1 2 7

N

O

4

8

3

COO

-

3

N

O

5

+

N

4

COO-

S O Cephaloridine HN

O

NH2

Ampicillin

HN O

S

O

S N COO

COO

HN

-

O H N

NO2

N COO-

O

N

Cefotaxime

S N

O

HN

S

Meropenem

OH

O

N N

O COO-

O

O

H2N

N H

NO2

S

NH2+

-

S

-

Nitrocefin

S

HN

OH Imipenem 6 5 1 2 S 7 N 3 4

+

N

Carbapenems

COO

N

O

O H2N

O

S N

Cephalexin

N

O

COO

O

-

S

-

COO

O

Cephalothin

HN H2N

HO

S N N

O O

N

O COO-

Ceftobiprole

HN O

S N

O

O N

S

O O

O

O

N

N COO

-

O

Fig. 2. Chemical structures of the antibiotics discussed in this review. In benzylpenicillin, ceftazidime, and imipenem, the heavy atoms of the penem, cephem, and carbapenem bicyclic ring systems have been numbered.

P. Oelschlaeger / Journal of Inorganic Biochemistry 102 (2008) 2043–2051

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ATGGAACCTTCGAAACCCTTTGGGAACTTGACCTCGACTGACTGGACCTTGGAAACC

random mutagenesis * ATGGAACCTTCGAAACTCTTTGGGAACTTGACCTCGACTGACTGGACCTTGGAAACC ATGGAACCTTCGAAACCCTTTGGGAACTTGACCTCGACTGACTGGACCTTAGAAACC * ATGGAACCTTCGAAACCCTTTGGGAACGTGACCTCGACTGACTGGACCTTGGAAACC * ATGGACCCTTCGAAACCCTTTGGGAACTTGACCTCGACTGACTGGACCTTGGAAACC

expression and selection Fig. 3. Directed evolution scheme of a hypothetical nucleotide sequence. Most genes are about one order of magnitude longer. Single nucleotide changes occur randomly, in reality about one order of magnitude less frequently than shown here. Exemplarily, four nucleotide sequences with one nucleotide change as a result of random mutagenesis (shaded grey) are shown. In only three of these sequences, the nucleotide change results in an amino acid substitution (indicated by asterisks). In the subsequent selection, gene products that confer favorable traits will be selected, whereas the other ones will be discarded.

boring IMP-1 variants, but also by the disc diffusion test. Since the determination of MICs usually employs two-fold dilutions of antibiotics and the determining criterion is simply growth versus no growth, it is a rather rough estimate. The disc diffusion test, however, measures the radius of a halo of inhibited growth around a paper disc containing antibiotic and is therefore a more quantitative and sensitive method to detect improved in vivo activity. A third possibility is that even though an enzyme might be more active in vitro, this improvement may not be apparent in vivo, because certain factors, such as the expression level of functional enzyme and transport of substrate to and product from the enzyme are not monitored. Also, it has to be considered that the concentration of antibiotic in these assays is usually well beyond the Michaelis constant KM, so that the turnover rate kcat may actually be more important than the catalytic efficiency kcat/KM. This scenario assumes that the concentration of antibiotic in the periplasm, where b-lactamases are naturally expressed is comparable to that in the medium. This assumption is supported by the fact that the outer bacterial membrane is quite permeable to small molecules due to porins, but to my knowledge the concentration of antibiotics in the periplasm has not been determined experimentally. We have been able to isolate a point mutant of IMP-1 (IMP-1-F218Y) that exhibited a 1.5-fold increased catalytic efficiency toward imipenem compared to the wild type in vitro [23] (see below). As in Hall’s

Fig. 4. Image of the active site of BcII and residues mutated in the DNA shuffling study by Tomatis et al. [24]. The image was generated with VMD [107] using the coordinates of PDB entry 1BC2 [108]. Zinc ions are shown as green spheres. The side chains and Ca atoms of zinc-ligating amino acid residues are shown as sticks colored by atom (C, grey; N, blue; O, red; S, yellow) and labeled at Ca. The two residues Asn70 and Gly262 that were mutated to serine in the BcII-M5 variant are shown with backbone atoms and are also labeled at Ca. Substrate binding occurs from the top.

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study, however, this very small increase in catalytic efficiency was not sufficient to decrease the susceptibility of E. coli cells harboring this variant to imipenem, that is, increase the MIC. It has to be noted that our MIC assay used cells that expressed MBLs in the cytoplasm [22,23] instead of the periplasm (all the other studies described here) and is therefore not an adequate marker of resistance. We will repeat these experiments using an expression vector for periplasmic expression. The increase in catalytic efficiency was entirely due to a decrease of KM and not to an increase of kcat, in support of the considerations mentioned above. Both Hall’s and our studies suggest that the evolution of IMP-1 into a variant that confers significantly enhanced resistance toward imipenem is unlikely. As opposed to using simple random mutagenesis on an enzyme that seems to be optimized for a certain antibiotic, Tomatis et al. employed DNA shuffling on BcII under the selective pressure of the cephalosporin cephalexin (Fig. 2) [24]. As mentioned above, DNA shuffling is a more powerful approach to generate improved enzyme mutants. In addition, BcII is believed to be a prototype of B1 MBLs, leaving plenty of room for improvement. Indeed, the authors found a four-fold mutant of BcII (named M5) that exhibited enhanced catalytic efficiency toward many cephalosporins without severely affecting activity toward imipenem and benzylpenicillin. Interestingly, this variant contains a G262S mutation (Fig. 4), which also distinguishes IMP-1 from IMP-6 [21,22]. Similar to a domino, Ser262 in IMP-1 (Fig. 5) has been proposed to better support its neighbor His263, a Zn2 ligand, in the presence of cephalosporins with positively charged moieties at C3, penicillins, and imipenem, than Gly262 in IMP-6 [50]. In the directed evolution study of Tomatis and coworkers, the G262S mutation in BcII had a slightly different impact, though. While it also favored conversion of cephalosporins with positively charged substituents at C3 (ceftazidime and cephaloridine), its effect on the inactivation of benzylpenicillin and imipenem (Fig. 2) was reverse to that of the G262S mutation in IMP-6. This may be due to the different enzyme architectures of IMP-6 and BcII or the effect of another mutation, N70S,

Fig. 5. Image of the active site of IMP-1 and residues 121, 218, and 262 mutated in Refs. [22,23]. The image was generated and rendered as described for Fig. 4 using the coordinates of PDB entry 1DD6 [64]. For both Ser121 and Ser262, the side chain is pointing to the left. IMP-6 is identical to IMP-1 except that the residue at position 262 is glycine as in BcII shown in Fig. 4.

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close to the active site in M5 [24]. Most B1 and B2 enzymes have asparagine at position 70 except the IMP enzymes, which have histidine at this position [12,30]. B3 enzymes seem to be more diverse at this position: in L1 from Stenotrophomonas maltophilia this residue is threonine and in FEZ-1 from Fluoribacter gormanii it is alanine [12]. In addition to the similar effect in IMP-6 and BcII with respect to ceftazidime and cephaloridine hydrolysis, the G262S mutation had no effect in both enzymes on the catalytic efficiency toward nitrocefin, a cephalosporin with a dinitrostyryl moiety at C3 (Fig. 2) [22,24]. Using a Co(II)-substituted variant of M5, a change in a Cys-Co(II) charge transfer band was observed compared to wild-type BcII, suggesting that the G262S and/or the N70S mutation affect the position of the Co(II) ion (and supposedly the Zn(II) ion in the native enzyme) in the Zn2 binding site [24]. Recent studies of the BcII-N70S and BcII-G262S point mutants as well as the BcII-N70S-G262S double mutant by Tomatis et al. suggest that the G262S mutation is responsible for enhancing the catalytic efficiency toward cephalexin by better positioning Zn2 through hydrogen bonds with Cys221 (personal communication). The N70S mutation, on the other hand, removes hydrogen bonds with neighboring residues and leads to a more flexible active site cleft, which results in an overall broadened substrate spectrum. Based on these observations, Tomatis et al. propose a new paradigm for protein evolution: In contrast to cooperative double mutants in serine b-lactamases, where one mutation enhances activity and another mutation restores enzyme stability [51–53], in the case of BcII, G262S and N70S seem to cooperate by enhancing activity and rendering the enzyme more flexible (Tomatis et al., personal communication). In summary, Tomatis et al. have successfully employed directed evolution to predict the potential natural evolution of BcII into a superior enzyme. The observed G262S mutation is strongly reminiscent of the evolution of IMP-6 to IMP-1, which has already occurred naturally and created an enzyme that cannot evolve into an enzyme conferring enhanced imipenem resistance, as suggested by Hall. Thus, directed evolution experiments are useful for predicting, but also for excluding (although 100% certainty can probably never be achieved), the emergence of enhanced MBL-conferred antibiotic resistance. As we will see below, however, both IMP-6 and IMP-1 still have the potential to significantly enhance their activity toward other b-lactam antibiotics. 3. Codon randomization and selection of MBLs Codon randomization is a method that allows for the generation of libraries of protein variants with mutations at predefined regions [54]. In contrast to directed evolution experiments, this approach is usually designed to allow for all possible amino acid substitutions at the desired positions by randomly mutating all three nucleotides of a codon. Libraries created in that way can then be selected for beneficial properties, much like libraries generated by random mutagenesis (Fig. 6). Since all nucleotides of a codon can change, this technique may not appear suitable to mimic natural evolution. However, it provides very important information about the positions at which enzymes may evolve: it indicates amino acid positions that do not tolerate mutations and therefore appear critical for structure and/or function and, on the other hand, amino acid positions that do tolerate mutations and therefore appear to have a certain amino acid type rather randomly [20]. While the former residues are unlikely to mutate naturally, the latter are expected ‘‘hot spots” for mutation and deserve special attention. Researchers in the Palzkill laboratory have explored this approach to characterize both serine [55–57] and metallo-b-lactamases [20,58]. Materon and Palzkill studied the effect of

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ATGGAACCTTCGAAACCCTTTGGGAACTTGACCTCGACTGACTGGACCTTGGAAACC

codon randomization * ATGGAACCTTCGAAAGTCTTTGGGAACTTGACCTCGACTGACTGGACCTTGGAAACC * ATGGAACCTTCGAAAACTTTTGGGAACTTGACCTCGACTGACTGGACCTTGGAAACC stop ATGGAACCTTCGAAATAATTTGGGAACTTGACCTCGACTGACTGGACCTTGGAAACC * ATGGAACCTTCGAAAGGATTTGGGAACTTGACCTCGACTGACTGGACCTTGGAAACC

expression and selection Fig. 6. Codon randomization scheme of a hypothetical nucleotide sequence. A codon encoding an amino acid that has been selected for mutation (in this case codon 6, shaded grey) is randomly permutated, resulting in a library of sequences with all possible triplets at codon 6 (of which only four are shown here). In this example, three changed codons result in an amino acid substitution (indicated by asterisks), and one results in a nonsense mutation (indicated by ‘‘stop”). As in directed evolution, gene products conferring favorable traits can be selected. The same procedure can be applied to other codons in the nucleotide sequence.

mutations in and near the active site of IMP-1 on their ability to confer to E. coli host cells resistance toward ampicillin [20]. They were able to verify that several positions with conserved residues such as the zinc ligands are critical and do not tolerate substitutions. On the other hand, they discovered that some positions that were expected to be critical because they are conserved can actually be mutated without losing activity. For instance, Asn233, a conserved residue whose side chain had been proposed to help form an oxyanion hole [59], was found to be not critical for the conversion of ampicillin. Actually, an isolated and purified N233A mutant of IMP-1 exhibited enhanced catalytic efficiency toward ampicillin, nitrocefin, cefotaxime, and cephaloridine (Fig. 2) relative to wild-type IMP-1, mostly due to increases in kcat and for ampicillin also a decrease in KM [20]. Materon et al. then took these studies to the next level by selecting mutants not only based on their ability to confer resistance toward ampicillin, but also toward cefotaxime, cephaloridine, and imipenem [58]. Interestingly, the set of selected IMP-1 variants depended on the identity of the antibiotic used for selection. As in the previous study [20], some amino acid positions did not tolerate substitutions, whereas others did. However, some amino acid positions were critical for the conversion of some antibiotics, but not for the conversion of others [58]. For instance, Lys69 is critical for the hydrolysis of the cephalosporins cefotaxime and cephaloridine, but ampicillin and imipenem are also efficiently converted when Lys69 is mutated to serine. Pro225 is critical for the inactivation of imipenem and cephaloridine, but ampicillin is also efficiently converted when Pro225 is mutated to alanine, and cefotaxime is converted when Pro225 is mutated to leucine, arginine, lysine, or glutamine. Ser262 is critical for the inactivation of all tested antibiotics except cefotaxime, which is also hydrolyzed when Ser262 is mutated to glycine. An antibiotic-dependent effective number of amino acid types at a certain position that yields functional enzyme, k*, was defined: if a certain residue cannot be mutated, k* equals 1.0; if all twenty naturally occurring amino acids appeared with the same frequency, k* would equal 20.0 [58]. Averaged over the investigated positions, k* was higher when cefotaxime was used for selection (2.47) than when cephaloridine (1.87), ampicillin (2.02), or imipenem (1.84) were used. This led the authors to propose that the sequence requirements for hydrolysis of cephaloridine, ampicillin, and imipenem are more stringent than that for hydrolysis of cefotaxime [58]. This information is very important for the application and development of b-lactam antibiotics and inhibitors. In the presence of cephaloridine, ampicillin, imipenem, or similar antibiotics, IMP-1 has fewer options to evolve (for instance to escape the

P. Oelschlaeger / Journal of Inorganic Biochemistry 102 (2008) 2043–2051

action of an inhibitor) than in the presence of cefotaxime or similar drugs [58]. 4. Insights into MBL evolution from biochemical and structural studies Valuable information about the evolution of MBLs can also be obtained by thoroughly evaluating and interpreting biochemical and structural evidence. By investigating the substrate profiles of IMP-3 and IMP-1, which differs from IMP-3 by two mutations, G126E and G262S, as well as two artificial IMP-3 variants carrying the two mutations separately, Iyobe et al. plausibly concluded that IMP-1 evolved from its precursor IMP-3 through these two mutations and that especially the G262S mutation is responsible for the superior substrate profile of IMP-1 [21]. Interestingly, the artificial intermediate between IMP-3 and IMP-1 (the G126E mutant of IMP-3) was later isolated as IMP-6, an enzyme that seems to be specialized for the inactivation of meropenem (Fig. 2), another b-lactam antibiotic of the carbapenem type [26]. In another biochemical study, the possible evolution of a binuclear MBL like CcrA from Bacteroides fragilis from a mononuclear MBL like BcII from Bacillus cereus was investigated by introducing a C121R mutation into CcrA [60]. BcII contains arginine at position 121 next to the zinc ligand Asp120, which could be the reason for its relatively poor binding of Zn2 and low catalytic efficiency. Although the CcrA–C121R mutant still bound two zinc ions, the catalytic efficiency toward nitrocefin was reduced by about one order of magnitude compared to wild-type CcrA. When removing one equivalent of zinc from the enzymes using the chelating agent 4-(2-pyridylazo)resorcinol (PAR), the catalytic efficiency of the mutant enzyme was again reduced by one order of magnitude relative to the wild type. For both enzymes, the mononuclear version exhibited a catalytic efficiency of about one third of that of the binuclear enzyme [60]. The second zinc ion stabilizes an anionic intermediate resulting from hydrolysis of the amide bond in the b-lactam ring [15], resulting in a change of the rate-limiting step from cleavage of the amide bond to protonation of the anionic intermediate [60]. The authors conclude that both the R121C mutation and employing a second zinc ion can enhance the catalytic efficiency and may be important steps in the evolution of MBLs [60]. More recent studies, however, have shown that Arg121 does not impair the binding affinity of the Zn2 binding site in BcII [61,62]. Comparing X-ray crystal structures of different enzymes is another avenue to obtain information about certain aspects of MBL evolution. In a recent report on the X-ray crystal structure of the Sao Paulo MBL (SPM-1) from Pseudomonas aeruginosa, Murphy et al. speculate on how this enzyme may evolve into an improved enzyme in the future [63]. In the crystal structure, SPM-1 only binds one zinc ion in the Zn1 binding site, but not in the Zn2 binding site, which appears to be due to oxidation of Cys221. SPM-1 harbors a serine at position 84 as opposed to aspartate in the more efficient binuclear enzymes such as IMP-1 and CcrA. In IMP-1, Asp84 forms a salt bridge to Lys69, which in turn interacts electrostatically with the backbone carbonyl of Asp120, thus orienting its side chain for efficient coordination of Zn2 [64]. In CcrA, Asp84 does not form a salt bridge with residue 69, which is serine in CcrA, but coordinates a sodium ion, which is also coordinated by the backbone carbonyl of Asp120 [59]. This interaction again seems to orient the Asp120 side chain for efficient coordination of Zn2. The absence of such interactions in SPM-1 may reduce the affinity of the Zn2 site in the unoxidized enzyme. Such observations led Murphy and coworkers to the proposal that SPM-1 may evolve into a more efficient enzyme through an S84D mutation [63].

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5. Predicting MBL evolution based on molecular modeling Molecular modeling is a powerful technique that can complement experimental efforts in studying the mechanism and evolution of MBLs. In the following, some examples of how molecular modeling has helped elucidate molecular mechanisms of MBLs will be given. Then, efforts to use molecular modeling to predict improved MBL variants that could evolve naturally will be discussed. Molecular modeling studies such as docking, molecular dynamics (MD) simulations, quantum mechanics (QM) calculations, or a combination of the latter two have been employed to investigate aspects of MBL activity that cannot easily be observed experimentally. The mononuclear BcII enzyme has been studied extensively [65–69]. These studies focus mainly on details of the mechanism, such as the protonation state of the zinc-bound water/hydroxide, possible proton donors/acceptors, active site hydrogen bonding networks [65–67,69], and substrate binding [66,68]. Among the binuclear MBLs, the computationally most extensively studied are CcrA [70–76] and IMP-1 [23,50,77–82]. Details about the catalytic mechanism of these binuclear enzymes, such as the protonation state of Asp120 [71,73], the nature of the Zn1–OH–Zn2 bridge [71,78], and the reaction pathway [75,76] were studied, as well as binding of inhibitors [70,77–81], substrates [68,69,72] and substrate intermediates [50,78,82]. In agreement with many experimental reports, these studies have established that the catalytic mechanisms of the mononuclear and binuclear enzymes differ. Whereas in the mononuclear BcII nucleophilic attack of the zinc-bound hydroxide on the amide bond and its cleavage are rate-determining, the rate-determining step in some binuclear enzymes (e.g. CcrA [15] and L1 [83]) occurs after amide bond hydrolysis. Then, the resulting anionic nitrogen interacts with Zn2 and protonation becomes the rate-determining step. Since the anionic intermediate is relatively stable, the activation free energy for the nucleophilic attack and cleavage of the amide bond is decreased, which results in an overall enhancement of catalytic efficiency of these binuclear enzymes relative to the mononuclear BcII. Since modeling zinc enzymes, especially the binuclear ones, is quite challenging, the fact that some observations from computer simulations were subsequently observed in experiment is very encouraging. Thus, an increase of the Zn1–Zn2 distance was observed in MD simulations when the hydroxide in the free CcrA did not coordinate Zn2 [71] or when the hydroxide in IMP-1 was replaced with a substrate anionic intermediate [78]. Yamaguchi et al. later observed a similar phenomenon in X-ray crystal structures of IMP-1 and its D120E and D120A point mutants [84]. In the two mutants, His263 was more distant from the wild-type Zn2 binding site and the Zn1–Zn2 distance was larger by 0.3 Å relative to wild-type IMP-1. The authors concluded that the Zn2 ligand His263 and the Zn1–Zn2 distance are quite flexible [84]. Crisp et al. very recently observed that in one of four subunits of a crystal structure of the D120N mutant of the MBL L1 from Stenotrophomonas maltophilia the bridging water/hydroxide was missing and the Zn1–Zn2 distance in that subunit was 0.6 Å larger than in the wild-type structure with the bridging water/hydroxide [85]. Also very recently, the role of the position of Zn2 in MBL catalysis has been investigated in BcII by a combination of mutagenesis in the Zn2 binding site, kinetics, X-ray crystallography and spectroscopy [86,87]. We made another unique observation. In an extensive MD simulation of IMP-1 in complex with a cephalothin (a neutral cephalosporin) intermediate, we observed a flip of the dihydrothiazine ring in a way that the sulfur atom S1 replaced the anionic nitrogen N5 as the Zn2 ligand [78]. Since we had no rationale for this observation at the time, we considered it an artifact of our modeling procedure. Interestingly, in a recent extended X-ray absorption fine

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structure (EXAFS) study by Costello et al. a nitrocefin product was apparently bound to the binuclear MBL L1 in the same way: the dihydrothiazine ring had flipped and S1 coordinated one of the zinc ions [88]. With EXAFS, it is not possible to determine with which of the two zinc ions S1 interacted, but due to the expected geometry and orientation of the product, Zn2 seems more likely. In the nitrocefin product-bound L1, the Zn1–Zn2 distance was also increased by 0.2 Å compared to the free enzyme [88], reminiscent of results from MD simulations with substrate intermediates [50,78]. While it is currently not clear how these observations relate to the mechanism, let alone the evolution of MBLs, the fact that computational and experimental data are in concord, provides some confidence in computational approaches and their usefulness in studies of MBLs. The prominent question in this review remains how these computational techniques may help us predict the evolution of MBLs into more efficient enzymes. Over the last few years, we have undertaken some efforts into that direction. Reliable and robust techniques to model MBLs and to predict catalytic efficiencies are still being developed. Therefore, one important aspect of our research design has been the combination of experiment and computation. A typical cycle in this approach consists of (1) a molecular modeling study to predict MBL mutants with enhanced catalytic efficiencies, (2) experimental validation of these predictions, (3) evaluation of the computational and experimental results, and (4) improvement of the molecular modeling approach. We have employed MD simulations using the AMBER force field [89] and a cationic dummy atom representation for the Zn(II) ions [78,90– 92] to model the stability of MBL-substrate intermediate complexes as an approximation to the stability of the transition state. The latter is related to the activation free energy, which in turn is related to the catalytic efficiency kcat/KM. Before attempting to predict catalytic efficiencies of new MBL variants, we wanted to make sure that we can reproduce the trend of published catalytic efficiencies of two wild-type enzymes, IMP-1 and IMP-6 [21]. Four cephalosporins (cephalothin, cefotaxime, cephaloridine, and ceftazidime) were used as substrates, because it was easier to model these antibiotics with a six-membered dihydrothiazine ring than penicillins and carbapenems with five-membered tetrahydrothiazole and dihydropyrrole rings [50]. After several trials and errors, we learned that the stability of an MBL-substrate intermediate complex in an MD simulation, defined by stable coordination of Zn1 by the carboxylate group resulting from amide bond hydrolysis and stable coordination of Zn2 by the anionic nitrogen N5 (in addition to the amino acid ligands of the protein environment), is a good indicator for catalytic efficiency. The carboxylate never lost contact to Zn1, but N5 lost contact to Zn2 in some complexes. These breakdown events were irreversible, occurred more frequently at higher simulation temperatures, and, interestingly, more frequently with enzyme/substrate combinations that exhibited relatively low catalytic efficiencies in experiment. By carrying out multiple MD simulations at different temperature steps (100 K, 200 K, and 300 K) for 200 ps each and simply adding up the number of simulations, in which the complex remained stable, we were able to create stability ranking scores that correlated with experiment (R2 = 0.82 for eight combinations of two enzymes and four substrates). A welcome side effect of this approach is that the MD simulation trajectories can be analyzed to provide insights into molecular mechanisms. In such analyses, we found that substituents with an ester group at C3 such as cephalothin and cefotaxime are held in place by the backbone and side chain of Asn233, whereas those with a pyridinium group at C3 such as cephaloridine and ceftazidime are repelled toward His263, a Zn2 ligand. Based on these observations, we proposed a domino effect model that can explain how the substituent at C3 determines whether His263 and the enzyme-substrate intermediate complex are disturbed or not and whether catalytic efficiency is low or high [50]. In general,

cephalosporins with a neutral ester moiety (cephalothin and cefotaxime) are inactivated more efficiently than those with a positively charged pyridinium moiety (cephaloridine and ceftazidime) [21,22]. Furthermore, the Ser262 side chain, which leads to increased catalytic efficiency with cephaloridine and ceftazidime [21,22], was proposed to act as a static domino and support His263 (compare IMP-1 in Fig. 5 to Fig. 4, where residue 262 is glycine as in IMP-6) and hence to stabilize the complexes between enzyme and intermediates of these antibiotics [50]. With confidence that our modeling approach provides an adequate model of reality, we went on to the next step and employed the same approach to predict novel MBL point mutants [22]. We chose a few residues in the vicinity of Ser262 in IMP-1 (Lys69, Ser121, Tyr218, and Ser262 itself) (Fig. 5) and determined all point mutations that could occur at these positions due to single nucleotide changes in the codons of the IMP-6 gene [22]. IMP-6 contains glycine at position 262, and we wanted to address the question what other evolutionary pathways aside from the G262S mutation could be followed in order to enhance its activity. This method for selection of variants resembles both directed evolution (only single nucleotide changes in a codon are allowed) and codon randomization (only a few rationally selected amino acid positions are allowed to mutate). Of the 24 possible point mutants, we excluded 12, which resulted in an alteration of charge or cysteine, because we expected these changes to affect the active site through altered interactions with the zinc ions and/or disulfide bonds with Cys221. Instead of using an experimental selection method, we screened the remaining 12 variants with potentially improved catalytic efficiency toward ceftazidime in silico by determining the stability ranking scores in MD simulations of enzyme–ceftazidime intermediate complexes. Five of the mutants exhibited higher enzyme– ceftazidime intermediate complex stability than IMP-6: G262A, G262V, S121G, F218Y, and F218I. These five variants selected by molecular modeling were then expressed, purified, and characterized experimentally, and the computational and experimental data were analyzed. For one of the variants, G262A, the computational prediction was in agreement with the experimental validation: it converted ceftazidime (as well as benzylpenicillin, ampicillin, and imipenem) more efficiently than IMP-6 in vitro [22]. The other four variants did not convert ceftazidime more efficiently, as predicted; however, they inactivated other antibiotics (nitrocefin, cephalothin, and cefotaxime) more efficiently. Thus, all predicted variants are enzymes that might cause problems if they evolved naturally. It has to be noted, however, that the expression level of soluble pro-

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8 7 6 5 4 3 2 1 1

2

3

4

5

6

7

8

9

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experimental ranking Fig. 7. Comparison of rankings derived from experiment and computer modeling. Rankings are taken from Ref. [82]. Data points with an experimental ranking of 2.5 and 9.5 were indistinguishable and labeled 2–3 and 9–10, respectively, in Ref. [82].

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tein of most of these enzymes when expressed in the cytoplasm of E. coli was lower than that of the wild-type enzymes, especially that of F218I. We will investigate the expression level of functional enzymes when targeted into the periplasm and the conferred resistance levels in future studies. Overall, the computational predictions with ceftazidime were only accurate in one out of 5 cases (20%), so that refinement of the molecular modeling approach was necessary. In a subsequent study [82] that included additional variants [23] (see below), we developed a different approach for obtaining stability rankings: instead of simulating at different constant temperatures, we continuously increased the simulation temperature. This resulted in shorter MD simulations and allowed us to simulate each complex 10 times instead of three times, thus improving the sampling. The stability ranking was then established by determining the average temperature at which each complex broke down in these 10 simulations. The complex with the highest average temperature of breakdown was ranked number one, followed by the one with the second highest and so on. Following this procedure, we obtained a computational (in silico) ranking that was in fair agreement with the experimental (in vitro) ranking based on catalytic efficiencies [82] (Fig. 7). The agreement is not yet perfect, though, and further improvements of the molecular modeling approach will have to be made. We also explored computational protein design [93] to select IMP-1 variants that may exhibit beneficial traits [23]. For technical reasons, we excluded the zinc ions and the substrate intermediate in these calculations. Thus, rather than selecting for enzyme variants with enhanced catalytic efficiency, we selected for variants that had an ideal amino acid sequence for the given protein fold. Residues 69, 121, 218, and 262 were allowed to change their identity. Apart from the IMP-1 point mutants S262A and F218Y, which had already been characterized in our previous study [22], these calculations yielded the double mutant F218Y–S262A, which was then characterized experimentally along with two control enzymess, IMP-1-F218Y and IMP-1-S262A [23] (the latter is identical to IMP-6-G262A described above) (see Fig. 5 for the positions of the mutated residues). Toward a few cephalosporins (nitrocefin, cephalothin, and cefotaxime) the designed double mutant F218Y–S262A was actually more efficient than IMP-1, however, toward other antibiotics (ceftazidime and imipenem) it was less efficient. Unexpectedly, the control enzyme IMP-1-F218Y was more efficient than wild-type IMP-1 toward all tested substrates in vitro, mostly due to decreased KM values. Also, the expression level of soluble IMP-1-F218Y was comparable to that of the wildtype enzymes IMP-6 and IMP-1. It is therefore possible that this variant will evolve naturally. In MD simulations, we discovered that the combination of Tyr218 and Ser262 alters the hydrogen bonding network at the interface of the two b sheets and pulls down the b hairpin loop covering the active site onto the substrate intermediate, which may account for the decreased KM values and increased catalytic efficiencies [82]. 6. De novo design and evolution of MBL activity Apart from existing MBLs evolving into more efficient enzymes, MBL activity could also arise in other proteins that currently have other or no enzymatic activities. Consequently, it would be desirable to be able to predict such de novo evolution events of MBL activity. While significant advances have been made in the computational de novo design of enzyme activity in inert protein scaffolds [94–96], this has not yet been achieved for MBL activity. This may be due to the complexity of the active site components including two zinc ions, at least six zinc ligands, and the substrate (or an intermediate or a transition state model), which would have to be considered in a computational protein design approach. How-

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ever, Park et al. were able to create MBL activity in another enzyme scaffold, glyoxalase II, through a combined rational and directed evolution approach [97]. Glyoxalase II is architecturally similar to MBLs but hydrolyzes the thioester bond in S-D-lactoylglutathione instead of the amide bond of b-lactams. MBL activity was generated by insertion, deletion, and substitution of several active site loops, followed by directed evolution to fine-tune the activity toward hydrolysis of the cephalosporin cefotaxime [97]. However, the catalytic efficiency of this designed enzyme toward cefotaxime was very low with 180 M 1 s 1. For comparison, the catalytic efficiency of IMP-1 toward the same antibiotic is more than four orders of magnitude higher [22,58]. Nevertheless, these results are impressive from a protein engineering point of view, but it is not clear how relevant they are to the emergence and evolution of antibiotic resistance. Certainly, in addition to the possibility that already existing MBLs may evolve into more efficient enzymes, we should also be concerned about the possibility that MBL activity may arise in related enzymes. The steps in the study by Park et al. [97] may seem too manifold that they would seem likely to take place naturally in the foreseeable future. On the other hand, it is entirely possible (maybe even likely) that enzymes exist that have not yet been characterized but that are much closer to having MBL activity than glyoxalase II and that could obtain MBL activity through relatively simple insertions or deletions. However, even if that was the case, in the absence of any biochemical or structural data on such hypothetical enzymes, it would be extremely challenging to make any predictions about their evolution. Maybe as more DNA sequences become available and information from bioinformatics, biochemistry, and structural biology becomes more interconnected, it will become possible to anticipate such events of de novo enzyme activity evolution in the future. 7. Future perspectives Progress has been made toward improving our ability to understand and predict the evolution of MBLs. However, natural evolution is usually more complex than the evolution that we mimic in the laboratory or in the computer. For instance, certain mutations may initially not confer any benefit to an enzyme and will therefore not be selected with our assays, whereas a ‘‘dormant” mutation may persist in nature as long as it does not significantly affect the performance of the enzyme. It is plausible that such a mutation does not develop a significant advantage for the enzyme until it is combined with a second mutation to yield an improved enzyme. An example for such a scenario follows. Let us assume mutation B enhances catalytic efficiency by stabilizing the transition state and decreasing the activation free energy. However, mutation B also has a detrimental effect on the stability of the enzyme by destroying a structurally important hydrogen bond. Therefore, the variant with mutation B alone would not be selected. Another mutation A might have no effect on structure or catalytic efficiency and would be propagated along with the wild-type enzyme. If mutation B occurred in the variant with mutation A, both mutations together could restore the structurally important hydrogen bond, and at the same time mutation B would enhance catalytic efficiency. The double mutant with both mutations A and B would then be superior to the wild-type enzyme. To learn more about the effects of such double (and more generally, multiple) mutations, we need to develop a better understanding of how mutations separated in sequence may cooperate in the three-dimensional structure through short-range interactions such as hydrogen bonds, salt bridges, and hydrophobic packing, or through long-range, allosteric effects. Such effects seem to be at work in the evolution of both serine [51–53] and metallo-b-lactamases (see Section 2). Bioinformatics may be a useful discipline to

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advance our understanding in that direction. Until now, bioinformatics has been used in the MBL field mostly to establish the phylogenetic relationship of the different subclasses of MBLs [98–101] and the phylogenetic relationship of MBLs with other enzymes of the zinc metallo-hydrolase family of the b-lactamase fold [10]. The usefulness of bioinformatics in predicting the future evolution of MBLs remains to be demonstrated. How may all these studies help improve antimicrobial chemotherapy? Being able to accurately predict the evolutionary pathways that lead to enhanced activity toward specific antibiotics could inform clinicians and pharmacologists in two valuable ways. It would allow us (1) to identify antibiotics toward which the evolution of high resistance levels is unlikely and which would therefore be preferable drugs, and (2) to identify antibiotics toward which the evolution of high resistance levels is likely. The latter would not only loose their efficacy in the long term, even worse, they might also render other, currently effective, antibiotics useless. For instance, our studies indicate that the use of cefotaxime, a commonly used cephalosporin antibiotic, may induce the evolution of IMP-1 and IMP-6 into more efficient enzymes toward this drug, which would then also be more efficient toward cephalothin. These drugs have relatively short and neutral polar side chains at C3. The C3 sidechain in cephalexin, which was used in Tomatis et al.’s directed evolution study, is even shorter, just a methyl group. The evolved BcII-M5 variant not only exhibited significantly enhanced catalytic efficiency toward this drug, but also toward other drugs, such as cefaclor with a chlorine atom and cephadroxyl with a methyl group at this position. The logical consequence seems to be to make the side chain at C3 large and positively charged. Ceftazidime has a pyridinium methylene group at C3 and is overall inactivated much more poorly than cefotaxime or cephalothin [21]. However, its inactivation is improved by enzymes in the evolutationary sequence IMP-6 ? IMP-1 ? IMP-1F218Y [21–23], and it is conceivable that this trend will continue. Actually, the use of drugs like ceftazidime, imipenem, and penicillins may have triggered the evolution of IMP-6 to IMP-1 in the first place. Some new anti-MRSA (methicillin-resistant Staphylococcus aureus) cephalosporins, namely ceftobiprole (Fig. 2) and ceftaroline (PPI-0903) now at or close to the clinic also pursue the strategy of adding large neutral or positively charged side chains to C3 [102]. Also an anti-MRSA carbapenem with an extended side chain at C2 (RO-4908463) is currently in clinical trials [102]. While ceftobiprole has completed phase III clinical trials for complicated skin and skin structure infections and has proven to be as effective as vancomycin [103], it is quite efficiently inactivated by MBLs [102,104], for instance eight times more efficiently than ceftazidime by IMP-1 and 200 times more efficiently than ceftazidime by VIM-2 [104]. It can be expected that the F218Y mutation in IMP-1 will further increase the catalytic efficiency toward ceftobiprole. In addition, this new drug may induce further evolution of MBLs. If Staphylococcus aureus were to acquire such MBL genes, which is not unlikely because they are frequently encoded in plasmids and integrons, ceftobiprole might loose its efficacy against this pathogen. At any rate, it seems advisable to avoid the exposure of bacteria expressing MBLs to b-lactam antibiotics as much as possible. This applies especially to the unnecessary use of antibiotics against viral infections or in animal production. In the clinic, it may be useful to interrupt the selective pressure imposed by antibiotics and therefore the driving force for MBL evolution in one direction by altering the type of antibiotics at regular intervals. For instance, the constant use of cefotaxime may induce the evolution of IMP-6 into IMP-6-G262V, which can convert this antibiotic more efficiently [22]. This variant, however, is less efficient toward, for instance, ceftazidime and imipenem. Switching from cefotaxime to ceftazi-

dime or imipenem may render IMP-6-G262V useless, thus interrupting the evolutionary pathway in that direction. Instead, IMP6-G262A and IMP-6-G262S (=IMP-1) [22] may now evolve, and IMP-1 may evolve into IMP-1-F218Y [23]. If we had a drug that was not converted more efficiently by IMP-1-F218Y than by IMP1, switching to that drug would interrupt the evolutionary pathway in that direction and so on. Knowing more about these potential evolutionary pathways could significantly facilitate the design of such drugs and their application. Another way in which the ability to predict the evolution of MBLs may assist antimicrobial chemotherapy is by guiding the design of MBL inhibitors. Designing general MBL inhibitors has proven difficult due to the structural and functional diversity between the MBL subclasses [7]. In addition, inhibitors that target the zinc ions may exhibit the side effect of inhibiting vital human zinc enzymes. A promising inhibitor design explored the combination of interactions between an inhibitor thiolate and the zinc ions with a covalent bond between the inhibitor and Lys224, which is conserved in many B1 and B2 enzymes [12], thus providing more specific and irreversible inhibitor binding to IMP-1 [105]. The activity of this inhibitor against other MBLs remains to be established. Knowing which other residues are unlikely to mutate in natural evolution might help identify other residues for such specific interactions between inhibitors and MBLs that do not have Lys224, such as VIM-2 and B3 enzymes. It might even be possible to design a broadband MBL inhibitor that interacts with the zinc ions and backbone atoms that have a unique orientation in the active site of MBLs but that will not change upon mutation. The evolution of MBLs into more efficient enzymes is an immediate and urgent threat to public health. The different advances to predict this process are encouraging and may help in the design of more effective antibiotics, more specific MBL inhibitors, and the design of more prudent strategies for the application of these drugs. However, these goals are not trivial and we will have to unite all our experience and expertise from different disciplines, such as genetics, microbiology, biochemistry, molecular modeling, bioinformatics, and clinical research in order to effectively combat the emergence of enhanced MBL-conferred antibiotic resistance. Acknowledgments I thank Timothy Palzkill, Barry G. Hall, Pablo E. Tomatis, Alejandro J. Vila, and James Spencer for helpful discussions and P.E.T. and A.J.V. for sharing their interesting results prior to publication. The very constructive comments of the reviewers are also highly appreciated. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

J. Petrosino, C. Cantu 3rd, T. Palzkill, Trends Microbiol. 6 (1998) 323–327. B.G. Hall, Nat. Rev. Microbiol. 2 (2004) 430–435. K. Poole, Symp. Ser. Soc. Appl. Microbiol. (2002) 55S–64S. G.D. Wright, Adv. Drug Deliv. Rev. 57 (2005) 1451–1470. J.F. Fisher, S.O. Meroueh, S. Mobashery, Chem. Rev. 105 (2005) 395–424. T.R. Walsh, M.A. Toleman, L. Poirel, P. Nordmann, Clin. Microbiol. Rev. 18 (2005) 306–325. M.W. Crowder, J. Spencer, A.J. Vila, Acc. Chem. Res. 39 (2006) 721–728. C. Bebrone, Biochem. Pharmacol. 74 (2007) 1686–1701. A. Carfi, S. Pares, E. Duee, M. Galleni, C. Duez, J.M. Frere, O. Dideberg, Embo J. 14 (1995) 4914–4921. H. Daiyasu, K. Osaka, Y. Ishino, H. Toh, FEBS Lett. 503 (2001) 1–6. M. Galleni, J. Lamotte-Brasseur, G.M. Rossolini, J. Spencer, O. Dideberg, J.M. Frere, Antimicrob. Agents Chemother. 45 (2001) 660–663. G. Garau, I. Garcia-Saez, C. Bebrone, C. Anne, P. Mercuri, M. Galleni, J.M. Frere, O. Dideberg, Antimicrob. Agents Chemother. 48 (2004) 2347–2349. G. Garau, C. Bebrone, C. Anne, M. Galleni, J.M. Frere, O. Dideberg, J. Mol. Biol. 345 (2005) 785–795. J.H. Ullah, T.R. Walsh, I.A. Taylor, D.C. Emery, C.S. Verma, S.J. Gamblin, J. Spencer, J. Mol. Biol. 284 (1998) 125–136. Z. Wang, W. Fast, S.J. Benkovic, Biochemistry 38 (1999) 10013–10023.

P. Oelschlaeger / Journal of Inorganic Biochemistry 102 (2008) 2043–2051 [16] S. Haruta, H. Yamaguchi, E.T. Yamamoto, Y. Eriguchi, M. Nukaga, K. O’Hara, T. Sawai, Antimicrob. Agents Chemother. 44 (2000) 2304–2309. [17] D. de Seny, C. Prosperi-Meys, C. Bebrone, G.M. Rossolini, M.I. Page, P. Noel, J.M. Frere, M. Galleni, Biochem. J. 363 (2002) 687–696. [18] M.P. Yanchak, R.A. Taylor, M.W. Crowder, Biochemistry 39 (2000) 11330– 11339. [19] S. Haruta, E.T. Yamamoto, Y. Eriguchi, T. Sawai, FEMS Microbiol. Lett. 197 (2001) 85–89. [20] I.C. Materon, T. Palzkill, Protein Sci. 10 (2001) 2556–2565. [21] S. Iyobe, H. Kusadokoro, J. Ozaki, N. Matsumura, S. Minami, S. Haruta, T. Sawai, K. O’Hara, Antimicrob. Agents Chemother. 44 (2000) 2023–2027. [22] P. Oelschlaeger, S.L. Mayo, J. Pleiss, Protein Sci. 14 (2005) 765–774. [23] P. Oelschlaeger, S.L. Mayo, J. Mol. Biol. 350 (2005) 395–401. [24] P.E. Tomatis, R.M. Rasia, L. Segovia, A.J. Vila, Proc. Natl. Acad. Sci. USA 102 (2005) 13761–13766. [25] N. Laraki, N. Franceschini, G.M. Rossolini, P. Santucci, C. Meunier, E. de Pauw, G. Amicosante, J.M. Frere, M. Galleni, Antimicrob. Agents Chemother. 43 (1999) 902–906. [26] H. Yano, A. Kuga, R. Okamoto, H. Kitasato, T. Kobayashi, M. Inoue, Antimicrob. Agents Chemother. 45 (2001) 1343–1348. [27] J.J. Yan, W.C. Ko, J.J. Wu, Antimicrob. Agents Chemother. 45 (2001) 2368– 2371. [28] Y.W. Chu, M. Afzal-Shah, E.T. Houang, M.I. Palepou, D.J. Lyon, N. Woodford, D.M. Livermore, Antimicrob. Agents Chemother. 45 (2001) 710–714. [29] G.J. Da Silva, M. Correia, C. Vital, G. Ribeiro, J.C. Sousa, R. Leitao, L. Peixe, A. Duarte, FEMS Microbiol. Lett. 215 (2002) 33–39. [30] J.D. Docquier, M.L. Riccio, C. Mugnaioli, F. Luzzaro, A. Endimiani, A. Toniolo, G. Amicosante, G.M. Rossolini, Antimicrob. Agents Chemother. 47 (2003) 1522– 1528. [31] M.A. Toleman, D. Biedenbach, D. Bennett, R.N. Jones, T.R. Walsh, J. Antimicrob. Chemother. 52 (2003) 583–590. [32] N.D. Hanson, A. Hossain, L. Buck, E.S. Moland, K.S. Thomson, Antimicrob. Agents Chemother. 50 (2006) 2272–2273. [33] C. Neuwirth, E. Siebor, F. Robin, R. Bonnet, Antimicrob. Agents Chemother. 51 (2007) 4486–4488. [34] L. Lauretti, M.L. Riccio, A. Mazzariol, G. Cornaglia, G. Amicosante, R. Fontana, G.M. Rossolini, Antimicrob. Agents Chemother. 43 (1999) 1584–1590. [35] L. Poirel, T. Naas, D. Nicolas, L. Collet, S. Bellais, J.D. Cavallo, P. Nordmann, Antimicrob. Agents Chemother. 44 (2000) 891–897. [36] J.J. Yan, P.R. Hsueh, W.C. Ko, K.T. Luh, S.H. Tsai, H.M. Wu, J.J. Wu, Antimicrob. Agents Chemother. 45 (2001) 2224–2228. [37] C.G. Giske, M. Rylander, G. Kronvall, Antimicrob. Agents Chemother. 47 (2003) 3034–3035. [38] T.H. Koh, G.C. Wang, L.H. Sng, Antimicrob. Agents Chemother. 48 (2004) 2334–2336. [39] M.A. Toleman, K. Rolston, R.N. Jones, T.R. Walsh, Antimicrob. Agents Chemother. 48 (2004) 329–332. [40] M. Kontou, S. Pournaras, I. Kristo, A. Ikonomidis, A.N. Maniatis, C. Stathopoulos, Biochemistry 46 (2007) 13170–13178. [41] A. Crameri, S. Cwirla, W.P. Stemmer, Nat. Med. 2 (1996) 100–102. [42] H.R. Hoogenboom, Nat. Biotechnol. 23 (2005) 1105–1116. [43] K.A. Powell, S.W. Ramer, S.B. Del Cardayre, W.P. Stemmer, M.B. Tobin, P.F. Longchamp, G.W. Huisman, Angew. Chem. Int. Ed. Engl. 40 (2001) 3948– 3959. [44] U.T. Bornscheuer, Adv. Biochem. Eng. Biotechnol. 100 (2005) 181–203. [45] K.L. Tee, U. Schwaneberg, Comb. Chem. High Throughput Screen. 10 (2007) 197–217. [46] W.P. Stemmer, Nature 370 (1994) 389–391. [47] M. Barlow, B.G. Hall, Genetics 160 (2002) 823–832. [48] B.G. Hall, Antimicrob. Agents Chemother. 48 (2004) 1032–1033. [49] W.P. Stemmer, Proc. Natl. Acad. Sci. USA 91 (1994) 10747–10751. [50] P. Oelschlaeger, R.D. Schmid, J. Pleiss, Biochemistry 42 (2003) 8945–8956. [51] M.C. Orencia, J.S. Yoon, J.E. Ness, W.P. Stemmer, R.C. Stevens, Nat. Struct. Biol. 8 (2001) 238–242. [52] B.M. Beadle, B.K. Shoichet, J. Mol. Biol. 321 (2002) 285–296. [53] M.A. DePristo, D.M. Weinreich, D.L. Hartl, Nat. Rev. Genet. 6 (2005) 678–687. [54] B.P. Cormack, K. Struhl, Science 262 (1993) 244–248. [55] C. Cantu III, W. Huang, T. Palzkill, J. Biol. Chem. 271 (1996) 22538–22545. [56] C. Cantu III, W. Huang, T. Palzkill, J. Biol. Chem. 272 (1997) 29144–29150. [57] W. Huang, J. Petrosino, M. Hirsch, P.S. Shenkin, T. Palzkill, J. Mol. Biol. 258 (1996) 688–703. [58] I.C. Materon, Z. Beharry, W. Huang, C. Perez, T. Palzkill, J. Mol. Biol. 344 (2004) 653–663. [59] N.O. Concha, B.A. Rasmussen, K. Bush, O. Herzberg, Structure 4 (1996) 823– 836. [60] W. Fast, Z. Wang, S.J. Benkovic, Biochemistry 40 (2001) 1640–1650. [61] R.M. Rasia, A.J. Vila, Biochemistry 41 (2002) 1853–1860.

2051

[62] A.M. Davies, R.M. Rasia, A.J. Vila, B.J. Sutton, S.M. Fabiane, Biochemistry 44 (2005) 4841–4849. [63] T.A. Murphy, L.E. Catto, S.E. Halford, A.T. Hadfield, W. Minor, T.R. Walsh, J. Spencer, J. Mol. Biol. 357 (2006) 890–903. [64] N.O. Concha, C.A. Janson, P. Rowling, S. Pearson, C.A. Cheever, B.P. Clarke, C. Lewis, M. Galleni, J.M. Frere, D.J. Payne, J.H. Bateson, S.S. Abdel-Meguid, Biochemistry 39 (2000) 4288–4298. [65] D. Suarez, K.M. Merz Jr., J. Am. Chem. Soc. 123 (2001) 3759–3770. [66] N. Diaz, D. Suarez, K.M. Merz Jr., J. Am. Chem. Soc. 123 (2001) 9867–9879. [67] M. Dal Peraro, A.J. Vila, P. Carloni, J. Biol. Inorg. Chem. 7 (2002) 704–712. [68] M. Dal Peraro, A.J. Vila, P. Carloni, Proteins 54 (2004) 412–423. [69] M. Dal Peraro, L.I. Llarrull, U. Rothlisberger, A.J. Vila, P. Carloni, J. Am. Chem. Soc. 126 (2004) 12661–12668. [70] F.R. Salsbury Jr., M.F. Crowley, C.L. Brooks III, Proteins 44 (2001) 448–459. [71] D. Suarez, E.N. Brothers, K.M. Merz Jr., Biochemistry 41 (2002) 6615–6630. [72] D. Suarez, N. Diaz, K.M. Merz Jr., J. Comput. Chem. 23 (2002) 1587–1600. [73] M. Dal Peraro, A.J. Vila, P. Carloni, Inorg. Chem. 42 (2003) 4245–4247. [74] H. Park, K.M. Merz Jr., J. Med. Chem. 48 (2005) 1630–1637. [75] H. Park, E.N. Brothers, K.M. Merz Jr., J. Am. Chem. Soc. 127 (2005) 4232–4241. [76] M. Dal Peraro, A.J. Vila, P. Carloni, M.L. Klein, J. Am. Chem. Soc. 129 (2007) 2808–2816. [77] J. Antony, N. Gresh, L. Olsen, L. Hemmingsen, C.J. Schofield, R. Bauer, J. Comput. Chem. 23 (2002) 1281–1296. [78] P. Oelschlaeger, R.D. Schmid, J. Pleiss, Protein Eng. 16 (2003) 341–350. [79] L. Olsen, I. Pettersson, L. Hemmingsen, H.W. Adolph, F.S. Jorgensen, J. Comput. Aided Mol. Des. 18 (2004) 287–302. [80] J. Antony, J.P. Piquemal, N. Gresh, J. Comput. Chem. 26 (2005) 1131–1147. [81] L. Olsen, S. Jost, H.W. Adolph, I. Pettersson, L. Hemmingsen, F.S. Jorgensen, Bioorg. Med. Chem. 14 (2006) 2627–2635. [82] P. Oelschlaeger, J. Pleiss, J. Mol. Biol. 366 (2007) 316–329. [83] S. McManus-Munoz, M.W. Crowder, Biochemistry 38 (1999) 1547–1553. [84] Y. Yamaguchi, T. Kuroki, H. Yasuzawa, T. Higashi, W. Jin, A. Kawanami, Y. Yamagata, Y. Arakawa, M. Goto, H. Kurosaki, J. Biol. Chem. 280 (2005) 20824– 20832. [85] J. Crisp, R. Conners, J.D. Garrity, A.L. Carenbauer, M.W. Crowder, J. Spencer, Biochemistry 46 (2007) 10664–10674. [86] L.I. Llarrull, S.M. Fabiane, J.M. Kowalski, B. Bennett, B.J. Sutton, A.J. Vila, J. Biol. Chem. 282 (2007) 18276–18285. [87] J.M. Gonzalez, F.J. Medrano Martin, A.L. Costello, D.L. Tierney, A.J. Vila, J. Mol. Biol. 373 (2007) 1141–1156. [88] A. Costello, G. Periyannan, K.W. Yang, M.W. Crowder, D.L. Tierney, J. Biol. Inorg. Chem. 11 (2006) 351–358. [89] W.D. Cornell, P. Cieplak, C.I. Bayly, I.R. Gould, K.M. Merz, D.M. Ferguson, D.C. Spellmeyer, T. Fox, J.W. Caldwell, P.A. Kollman, J. Am. Chem. Soc. 117 (1995) 5179–5197. [90] Y.P. Pang, J. Mol. Model. 5 (1999) 196–202. [91] Y.P. Pang, K. Xu, J.E. Yazal, F.G. Prendergast, Protein Sci. 9 (2000) 1857–1865. [92] Y.P. Pang, Proteins 45 (2001) 183–189. [93] B.I. Dahiyat, S.L. Mayo, Science 278 (1997) 82–87. [94] D.N. Bolon, S.L. Mayo, Proc. Natl. Acad. Sci. USA 98 (2001) 14274–14279. [95] L. Jiang, E.A. Althoff, F.R. Clemente, L. Doyle, D. Rothlisberger, A. Zanghellini, J.L. Gallaher, J.L. Betker, F. Tanaka, C.F. Barbas III, D. Hilvert, K.N. Houk, B.L. Stoddard, D. Baker, Science 319 (2008) 1387–1391. [96] D. Rothlisberger, O. Khersonsky, A.M. Wollacott, L. Jiang, J. DeChancie, J. Betker, J.L. Gallaher, E.A. Althoff, A. Zanghellini, O. Dym, S. Albeck, K.N. Houk, D.S. Tawfik, D. Baker, Nature 453 (2008) 190–195. [97] H.S. Park, S.H. Nam, J.K. Lee, C.N. Yoon, B. Mannervik, S.J. Benkovic, H.S. Kim, Science 311 (2006) 535–538. [98] B.G. Hall, S.J. Salipante, M. Barlow, J. Mol. Evol. 57 (2003) 249–254. [99] B.G. Hall, S.J. Salipante, M. Barlow, J. Mol. Evol. 59 (2004) 133–141. [100] B.G. Hall, M. Barlow, J. Antimicrob. Chemother. 55 (2005) 1050–1051. [101] G. Garau, A.M. Di Guilmi, B.G. Hall, Antimicrob. Agents Chemother. 49 (2005) 2778–2784. [102] M.G. Page, Curr. Opin. Pharmacol. 6 (2006) 480–485. [103] K. Bush, M. Heep, M.J. Macielag, G.J. Noel, Expert Opin. Investig. Drugs 16 (2007) 419–429. [104] A.M. Queenan, W. Shang, M. Kania, M.G. Page, K. Bush, Antimicrob. Agents Chemother. 51 (2007) 3089–3095. [105] H. Kurosaki, Y. Yamaguchi, T. Higashi, K. Soga, S. Matsueda, H. Yumoto, S. Misumi, Y. Yamagata, Y. Arakawa, M. Goto, Angew. Chem. Int. Ed. Engl. 44 (2005) 3861–3864. [106] R. Koradi, M. Billeter, K. Wuthrich, J. Mol. Graph. 14 (1996) 51–55. 29–32. [107] W. Humphrey, A. Dalke, K. Schulten, J. Mol. Graph. 14 (1996) 33–38. 27–38. [108] S.M. Fabiane, M.K. Sohi, T. Wan, D.J. Payne, J.H. Bateson, T. Mitchell, B.J. Sutton, Biochemistry 37 (1998) 12404–12411. [109] I. Schneider, E. Keuleyan, R. Rasshofer, R. Markovska, A.M. Queenan, A. Bauernfeind, Antimicrob. Agents Chemother. in press.