Combination of computational prescreening and experimental library construction can accelerate enzyme optimization by directed evolution

Susanne Aileen Funke1,3, Nikolaj Otte2,3, Thorsten Eggert1,4, Marco Bocola2, Karl-Erich Jaeger1 and Walter Thiel2,4

1Institut für Molekulare Enzymtechnologie, Heinrich-Heine-Universität Düsseldorf, Forschungszentrum Jülich, D-52426 Jülich and 2Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, D-45470 Mülheim an der Ruhr, Germany

4 To whom correspondence should be addressed. E-mail: t.eggert{at}fz-juelich.de; thiel{at}mpi-muelheim.mpg.de


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Computational results
 Experimental results
 Discussion and conclusion
 Acknowledgements
 References
 
Chiral compounds can be produced efficiently by using biocatalysts. However, wild-type enzymes often do not meet the requirements of a production process, making optimization by rational design or directed evolution necessary. Here, we studied the lipase-catalyzed hydrolysis of the model substrate 1-(2-naphthyl)ethyl acetate both theoretically and experimentally. We found that a computational equivalent of alanine scanning mutagenesis based on QM/MM methodology can be applied to identify amino acid positions important for the activity of the enzyme. The theoretical results are consistent with concomitant experimental work using complete saturation mutagenesis and high-throughput screening of the target biocatalyst, a lipase from Bacillus subtilis. Both QM/MM-based calculations and molecular biology experiments identify histidine 76 as a residue that strongly affects the catalytic activity. The experiments demonstrate its important influence on enantioselectivity.

Keywords: directed evolution/enantioselectivity/molecular modeling/QM/MM calculation/saturation mutagenesis


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Computational results
 Experimental results
 Discussion and conclusion
 Acknowledgements
 References
 
The use of enzymes as natural catalysts for chemical processes, also referred to as ‘white biotechnology’, is a rapidly expanding field (Liese et al., 2000Go; Patel, 2003Go; Jaeger, 2004Go; Panke et al., 2004Go). The increasing demand to find useful biocatalysts has prompted the development of novel methods to identify new genes and isolate the corresponding biocatalyst proteins (Lorenz et al., 2002Go; Eggert et al., 2004aGo; Streit et al., 2004Go). However, enzyme properties normally do not fit the needs of a chemical process and, therefore, an array of molecular biological methods have been developed for enzyme optimization, with the most successful being directed evolution, which allows for the improvement of enzyme properties such as specific activity, substrate specificity and stability (Petrounia and Arnold, 2000Go; Cherry and Fidantsef, 2003Go; Robertson and Steer, 2004Go). Importantly, directed evolution can also be used to create enantioselective biocatalysts starting from non-selective wild-type enzymes (Jaeger and Eggert, 2004Go; Reetz, 2004Go). An effective directed evolution strategy requires the combination of different mutagenesis methods with efficient high-throughput screening or selection techniques (Reetz and Jaeger, 2002Go; Jaeger and Eggert, 2004Go). In particular, the quality of the first-generation mutagenesis library is of key importance, because its variants usually parent all subsequent generations. Therefore, molecular biology methods generating mutant libraries of high diversity such as error-prone PCR or DNA shuffling are widely used, but, unfortunately, such techniques produce libraries which may consist of up to 1012 individual clones, far exceeding current screening capacities. Complete saturation mutagenesis represents an alternative and targeted strategy which generates a library containing all possible single amino acid exchanges of a target enzyme. This method creates mutant libraries of high diversity consisting of 103–104 individual variants, allowing scans of the entire sequence space of a given protein for important amino acid positions (Eggert et al., 2004bGo). Complete saturation mutagenesis, also referred to as gene site saturation mutagenesis (GSSM) (Short, 2001Go), has proved to be useful in the evolution of a dehalogenase from Rhodococcus (Gray et al., 2001Go), of a nitrilase isolated from the metagenome (DeSantis et al., 2003Go) and of a lipase from the Gram-positive bacterium Bacillus subtilis (Funke et al., 2003Go). Alternatively, alanine scanning mutagenesis can be used to identify ‘hot spot’ positions in a given enzyme (Cunningham and Wells, 1989Go; Weiss et al., 2000Go). Here, single alanine substitutions are introduced at each amino acid position of the respective enzyme to investigate the contribution of every side chain to a particular property. Still, complete saturation and alanine scanning mutagenesis remain labor- and cost-intensive strategies and alternative but complementary methods are urgently needed to allow the prediction of important amino acids which would narrow down the number of positions to be saturated.

Combined quantum mechanical and molecular mechanical (QM/MM) methods provide a realistic approach to compute the influence of individual amino acids on a given enzymatic reaction. These methods allow the study of chemical reactions in their native surroundings, where the reacting groups in the active site are treated at the QM level and the protein environment is simulated at the MM level (Sherwood et al., 2003Go). We chose this approach to estimate the electrostatic influence of all amino acid side chains in Bacillus subtilis lipase A (BSLA) on the rate-determining reaction barrier for ester hydrolysis.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Computational results
 Experimental results
 Discussion and conclusion
 Acknowledgements
 References
 
1-(2-Naphthyl)ethyl acetate (NEA) agar plate assay

The indicator agar plates were prepared as described previously for detecting cutin hydrolysis (Kolattukudy et al., 1981Go). The enantiomerically pure substrates (R)- and (S)-NEA (0.25 mg), kindly provided by Professor M.T.Reetz (Max-Planck-Institut für Kohlenforschung, Mülheim a.d. Ruhr, Germany) were dissolved in 5 ml of dichloromethane containing the detergent Triton X-100 (300 mg). After evaporating the organic solvent (12 h at room temperature), the substrate mixture was emulsified in 6 ml of distilled water using ultrasonication, mixed with 50 ml of sterilized LB-Agar (10 g/l tryptone, 10 g/l NaCl, 5 g/l yeast extract) and vigorously homogenized using a high-speed mixer (Ultra-Turrax). The addition to the agar of Solvent Blue 38 (100 mg of dye per 50 ml of agar) significantly increased the contrast of clear halos formed by substrate hydrolysis.

Gas chromatographic (GC) analysis

For substrate conversion, 10 mM rac-NEA in 100 mM Tris–HCl buffer (pH 7.5) was incubated for 48 h at 20°C after adding lipase-containing cell extracts. The reaction products were isolated by extraction using ethyl acetate. GC analysis was performed on a Shimadzu GC-17A gas chromatograph. To separate both enantiomers of NEA, the following conditions were used: column, CP-Chirasil-DEX CB, 25 m x 0.25 mm i.d.; carrier gas, helium; temperature program, 5 min at 60°C, increased from 60 to 195°C at 5°C/min.

Computational QM/MM strategy

All computational studies were based on the crystal structure of BSLA (van Pouderoyen et al., 2001Go). We started from a structure with an isopropylidene glycol phosphonate inhibitor bound to the active serine (Ser77) (PDB accession code 1R4Z, 1R5O). We replaced the inhibitor with (R)-NEA in a tetrahedral configuration and hydrated the active site with a sphere of water (radius 25 Å). An iterative procedure of relaxation, rehydration and molecular dynamics was performed at the MM level to obtain a sensible model for our QM/MM calculations (Bocola et al., 2004Go). All pure MM calculations were done with the CHARMM27 (MacKerell et al., 1998Go) force field as implemented in the Charmm program (Brooks et al., 1983Go) (version c28b2). The QM/MM calculations employed Chemshell (Sherwood et al., 2003Go), which is a modular package that allows the combination of several QM and MM codes. Here we used Turbomole (Ahlrichs et al., 1989Go) at the BLYP (Becke, 1988Go; Lee et al., 1988Go)/6–31+G* (Hehre et al., 1972Go; Hariharan and Pople, 1973Go; Clark et al., 1983Go) level for the QM part and DL_POLY (Smith and Forester, 1996Go) as driver of the CHARMM27 force field for the MM part. The QM region contained 34 atoms (colored atoms in Figure 1, excluding the naphthyl ring and H76) and open valencies at the QM/MM boundary were satisfied with hydrogen link atoms. We performed geometry optimizations with the HDLC optimizer (Billeter et al., 2001Go) and located educt, transition state and product geometries for the first step of the ester hydrolysis reaction, the nucleophilic attack of the serine side chain (Ser77) on the planar ester carbon of NEA, yielding the tetrahedral intermediate (Scheme 1). The electrostatic impact on reaction barriers was estimated by a scan over all amino acid residues. In this procedure, we successively deleted the MM partial charges on the side chains of individual amino acids. Each of the charge sets thus obtained was used to re-evaluate the electronic energies of the tetrahedral intermediate, the transition state and the Michaelis complex. The electron densities were allowed to relax in the modified charge field. The scan was done on all 175 amino acid side chains resolved in the X-ray structure of the enzyme, excluding Ser77, Asp133 and His156, which belong to the catalytic triad and are within the QM region. Further computational details are given in the Supplementary data, available at PEDS Online.



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Fig. 1. Structural view of BSLA (A) and its active site pocket (B). Amino acids of the proposed catalytic triad and His76 are displayed as stick models. Ser77 and (R)-NEA form a tetrahedral intermediate (see 2 in Scheme 1).

 


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Scheme 1. Reaction scheme for substrate hydrolysis by wild-type BSLA which shows an enantioselectivity of E > 140 towards (R)-NEA.

 

    Computational results
 Top
 Abstract
 Introduction
 Materials and methods
 Computational results
 Experimental results
 Discussion and conclusion
 Acknowledgements
 References
 
Bacterial lipases play an important role in biotechnology, mainly owing to their potential for catalyzing ester hydrolysis and also synthesis reactions, which often proceed with high specificity and enantioselectivity (Jaeger and Eggert, 2004Go). Among bacterial lipases, BSLA is a unique enzyme because it represents a minimal {alpha}/ß-hydrolase (van Pouderoyen et al., 2001Go). Furthermore, this enzyme hydrolyzes several acetic acid esters of secondary alcohols with high enantioselectivities, namely menthyl acetate, 1-phenylethyl acetate and 1-(2-naphthyl)ethyl acetate (NEA) giving E values of 22 (for the 1R,2S,5R-enantiomer), >100 (R) and >140 (R), respectively. In the hydrolysis of NEA, this lipase accepts virtually no (S)-enantiomer, resulting in an ee value of >99%. Therefore, we chose to study by QM/MM calculations the ester hydrolysis of NEA catalyzed by BSLA.

The mechanism of this reaction involves as the rate-determining catalytic step (Scheme 1) a nucleophilic attack of the Ser77 side-chain oxygen on the carbonyl carbon under general base catalysis of His156, which transforms the ester 1 into the tetrahedral intermediate 2, as also proposed for other serine hydrolases.

Geometry optimization at the QM/MM level leads to a reactant conformation which resembles a possible Michaelis complex, with the ester oriented in such a way that facilitates the nucleophilic attack. The carbonyl oxygen is preoriented to enter the oxyanion hole formed by the backbone of Ile12 and Met78. We located a transition state connecting the Michaelis complex 1 and the tetrahedral intermediate 2 (Figure 1) and found a barrier of about 10 kcal/mol (for details see Supporting Information). The impact of a given mutation in the enzyme on the reaction barrier was estimated by setting to zero the MM charges on the corresponding side chain and recalculation of the energy of the three structures (Michaelis–Menten complex, transition state and tetrahedral intermediate) corresponding to stationary points (Bash et al., 1991Go; Dinner et al., 2001Go). This procedure was repeated for all residues of the enzyme. In this sense, we performed an in silico electrostatic equivalent of an alanine scanning mutagenesis (Morrison and Weiss, 2001Go).

The calculations identified five amino acid positions that have a pronounced effect (>1 kcal/mol) on the reaction barrier (Figure 2). Four of these (Lys44, Asp43, Asp40 and Arg142) represent ionizable groups located on the protein surface. Shielding of these charges, e.g. by counterions from the surrounding solution under physiological conditions, should diminish the influence of those residues. To test this hypothesis we added counterions close to the charged sites of the groups above and re-evaluated the barrier. We found that the contributions drop below 1 kcal/mol for each group and, consequently, we do not consider them as hot spots.



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Fig. 2. Modulation of the reaction barrier height by electrostatic perturbation of the environment (deletion of charges on side chains). The distance is measured from the tetrahedral carbon in the tetrahedral intermediate (C14 in Figure 1) to the geometric center of the individual amino acid side chains. Large contributions are labeled.

 
The remaining position identified in the QM/MM scan was residue His76, which is located below the active serine (Ser77) (Figure 1) and can form a hydrogen bond to the backbone oxygen of the active-site histidine (His156). This histidine is singly protonated in our model setup and its overall charge is therefore zero. Owing to its position, orientation and polarity, it may exert an important role during ester hydrolysis. According to the QM/MM calculations, the electronic effect of His76 is to raise the energy barrier for the nucleophilic attack.


    Experimental results
 Top
 Abstract
 Introduction
 Materials and methods
 Computational results
 Experimental results
 Discussion and conclusion
 Acknowledgements
 References
 
Parallel to the in silico scanning experiments shown in Figure 2, we scanned experimentally the complete BSLA sequence space by saturation mutagenesis and performed an activity screening using enantiomerically pure (R)- and (S)-NEA as the model substrates. A total of 181 saturation mutagenesis experiments were carried out, covering all residues from Ala1 to Asn181. Accordingly, 181 mutagenesis primers were synthesized which contained one randomized codon (NNS; N = all nucleobases, S = guanine or cytosine) (see Supporting Information). Thus, a library consisting of 32 different mutant genes at every single codon position was generated using the mega-primer PCR method for site-specific mutagenesis (Barettino et al., 1994Go; Funke et al., 2003Go). Subsequent cloning of these genes into expression vectors and transformation into the expression host Escherichia coli BL21 (DE3) resulted in a library containing all possible single-site enzyme variants of BSLA, which represents a diversity of 5792 different mutant genes corresponding to 3439 variant proteins.

This library was screened for the enantioselective hydrolysis of NEA by using a high-throughput assay which allowed us to identify visually clones producing active lipases by clearing zones surrounding the bacterial colonies (Figure 3). Escherichia coli transformands expressing the BSLA saturation variants were plated out directly onto indicator plates which contained either (R)- or (S)-NEA as the substrate and the plates were incubated at 37°C for 48 h. A total of 21 000 clones were screened with 10 500 variants plated on each indicator medium containing one enantiomer, which represents a theoretical oversampling by a factor of three, thereby ensuring a complete coverage of the entire saturation library. As expected from previous experiments, about one-third of the BSLA variants were found to be inactive towards (R)-NEA, presumably because of deleterious mutations. The remaining variants were enzymatically active, as indicated by the formation of clearing zones of various sizes. In contrast, most of those colonies growing on agar plates with (S)-NEA as the substrate did not show any enzymatic activity, essentially as observed for wild-type BSLA. However, five colonies were identified which formed clear halos on (S)-NEA (Figure 3), indicating that they produced BSLA variants which had acquired the ability to convert the (S)-enantiomer of the substrate, indicating a changed enantioselectivity of the enzyme. All these variants were mapped at position His76, which had also been identified by computational scanning. At least three different BSLA mutants were identified by DNA sequencing (Table I), named NEA1–NEA3, for further biochemical analysis.



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Fig. 3. High-throughput screening on indicator agar plates for the hydrolysis of (S)-NEA. Activity is indicated by clear halos surrounding the bacterial colonies (marked by arrows).

 

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Table I. Enantioselectivities of Bacillus subtilis lipase variants identified by screening a complete saturation mutagenesis library for the hydrolysis of (S)-NEA

 
The enzymatic activity and enantioselectivity of these BSLA variants were confirmed by chiral GC. Variant NEA1 (carrying the amino acid substitution His76Leu) showed a high conversion of (S)-NEA; however, it still hydrolyzed the (R)-enantiomer with an E value of 6.8. Interestingly, variant NEA2 (His76Ala) showed an inverted enantioselectivity of ee = 80% for the (S)-enantiomer of NEA, corresponding to an E value of 8.5 (Table I). Much to our surprise, we found hardly any activity for variant NEA3, although it contained the same amino acid exchange as variant NEA2, namely His76Ala. Closer inspection revealed that the two variants differed at the DNA level. In NEA2, the histidine to alanine exchange at position 76 was encoded by the codon ‘GCC’, whereas it was encoded by codon ‘GCG’ in NEA3. Consequently, we investigated the amounts of variant proteins produced by the respective clones using SDS–PAGE analysis and found that only a small amount of BSLA variant protein NEA3 was produced (Figure 4). This clearly indicates that codon usage can significantly influence the outcome of site saturation mutagenesis experiments. Previously published methods to saturate amino acid positions have used NNK- (DeSantis et al., 2003Go), NNS- (Funke et al., 2003Go) or MAX-codon mixtures (Hughes et al., 2003Go), but our results clearly support saturation mutagenesis with substitution of a given codon triplet by all possible 64 codons (NNN).



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Fig. 4. SDS-PAGE to analyze the amount of wild-type (wt) or variant (NEA1–3) Bacillus subtilis lipase A (BSLA) produced by E.coli. The gel was stained with Coomassie Brilliant Blue; the band representing BSLA is marked by an arrow.

 

    Discussion and conclusion
 Top
 Abstract
 Introduction
 Materials and methods
 Computational results
 Experimental results
 Discussion and conclusion
 Acknowledgements
 References
 
Our computational procedure uses QM/MM-optimized geometries for the Michaelis complex, the transition state and the tetrahedral intermediate of the substrate (R)-NEA in the wild-type enzyme to estimate the electrostatic influence of each residue on the relevant barriers. This approach is motivated by the commonly accepted view that electrostatics is a dominant factor for biocatalytic activity in general (Warshel, 2003Go; Garcia-Viloca et al., 2004Go); this should be a sensible assumption also in the present case since the calculated (QM/MM) transition state is a negatively charged oxyanion (see Supporting Information for details). The chosen procedure is fast enough for a qualitatitive prescreening of all residues in the enzyme.

It should be stressed that our simple electrostatic approach has several important limitations. First, it does not capture steric effects, which are assumed to be of minor importance; this is not strictly true, of course, as can already be seen from the present experimental result that the replacements His76Leu and His76Ala lead to different changes in enantioselectivity (see Table I). Second, it does not take into account the structural relaxations and rearrangements that occur after a mutation; it is intuitively clear that these will affect both activity and enantioselectivity and we have indeed confirmed in a recent molecular dynamics study (Bocola et al., 2004Go) that such structural changes can rationalize remote and cooperative effects of mutations on the enantioselectivity observed for lipase-catalyzed ester hydrolysis in Pseudomonas aerigunosa. Third, our simple approach does not differentiate properly between (R)- and (S)-substrates: test calculations show that the electrostatic influence of His76 on the barriers is almost the same when using (S)-NEA rather than (R)-NEA as substrate in our procedure (–2.3 vs –2.1 kcal/mol, respectively), which implies that structural relaxations and possibly also non-electrostatic interactions need to be considered for proper prediction of enantioselectivity. Finally, entropic effects are also neglected.

A more quantitative theoretical modeling would involve the initial replacement of a given amino acid in the wild-type structure followed by classical molecular dynamics runs to re-equilibrate the resulting mutant structure, which can already give detailed insight into the structural consequences of the mutation (Bocola et al., 2004Go). QM/MM geometry optimizations of educt, transition state and product are then required to determine the barriers for a given mutant and substrate, while QM/MM molecular dynamics runs along the reaction path need to be performed to include entropic effects and derive free energy barriers (Ottosson et al., 2001Go; Senn et al., 2005Go). Following this protocol for all possible mutations and both enantiomeric substrates would, however, constitute an immense computational effort that is far beyond current capabilities.

Given this situation, we view our computational procedure as a simple and practical QM/MM-based tool that may identify promising sites of mutation by locating residues that exert a strong electrostatic influence on the computed barrier. A replacement of such a residue should then change the barrier appreciably and there should be a reasonable chance that this change may be different for the two enantiomeric substrates (more so than in cases where the barrier remains unaffected by the replacement). In this manner, promising sites of mutation to generate more enantioselective mutants may be suggested without actually addressing the demanding task of predicting enantioselectivities theoretically.

This strategy has been successful in the present case study. The QM/MM-based analysis shows strong electrostatic effects of His76 and experimental screening of the complete mutagenesis library of BSLA indicates a decisive role of this residue: only mutations involving His76 produce BSLA variants which convert the (S)-enantiomer of NEA and thus exhibit a changed enantioselectivity. These findings support the hypothesis that our simple QM/MM-based prescreening procedure may be applied as a tool to find amino acid positions important for enantioselectivity. This raises the prospect that enzyme optimization by directed evolution may be accelerated by the combination of computational prescreening and experimental library construction.


    Notes
 
3 These authors contributed equally to this paper. Back


    Acknowledgements
 Top
 Abstract
 Introduction
 Materials and methods
 Computational results
 Experimental results
 Discussion and conclusion
 Acknowledgements
 References
 
We are grateful to Professor Dr M.T.Reetz, Dr C.Rüggeberg and Dr W.Wiesenhöfer for providing enantiomerically pure substrate and the data concerning the kinetic resolution of NEA by wild-type B. subtilis lipase A. Part of this work was supported by the European Commission in the framework of the program Biotechnology (project No. QLK3-CT-2001-00519).


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Computational results
 Experimental results
 Discussion and conclusion
 Acknowledgements
 References
 
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Received April 19, 2005; revised August 12, 2005; accepted August 18, 2005.

Edited by Stephen Mayo





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