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
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Abstract |
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Keywords: directed evolution/enantioselectivity/molecular modeling/QM/MM calculation/saturation mutagenesis
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Introduction |
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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., 2003). 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.
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Materials and methods |
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The indicator agar plates were prepared as described previously for detecting cutin hydrolysis (Kolattukudy et al., 1981). 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 TrisHCl 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., 2001). 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., 2004
). All pure MM calculations were done with the CHARMM27 (MacKerell et al., 1998
) force field as implemented in the Charmm program (Brooks et al., 1983
) (version c28b2). The QM/MM calculations employed Chemshell (Sherwood et al., 2003
), which is a modular package that allows the combination of several QM and MM codes. Here we used Turbomole (Ahlrichs et al., 1989
) at the BLYP (Becke, 1988
; Lee et al., 1988
)/631+G* (Hehre et al., 1972
; Hariharan and Pople, 1973
; Clark et al., 1983
) level for the QM part and DL_POLY (Smith and Forester, 1996
) 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., 2001
) 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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Computational results |
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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 (MichaelisMenten complex, transition state and tetrahedral intermediate) corresponding to stationary points (Bash et al., 1991; Dinner et al., 2001
). 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, 2001
).
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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Experimental results |
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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 NEA1NEA3, for further biochemical analysis.
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Discussion and conclusion |
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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., 2004) 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., 2004). 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., 2001
; Senn et al., 2005
). 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.
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Notes |
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Acknowledgements |
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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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