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Genomics-based discovery and engineering of biocatalysts for conversion of aminesHeberling, Matthew Michael

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149

6Transaminases

Matthew M. Heberling1,2, Christiaan Postema1, Marcelo F. Mas-man1, Wenjun Wang1, Thomas J. Meijer1, and Dick B. Janssen1

1 Biotransformation and Biocatalysis, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands2 Peptone – The Protein Intelligence Company, Hullenbergweg 280, 1101BV Amsterdam, The Netherlands

Author contributions:MMH performed experiments with ATAs and β-TAs, managed project, and wrote chapter. CP and TJM performed experiments with β-TAs. WW performed initial experiments with ATAs and created substrate summaries of ATAs. MFM performed computational analyses and managed project. DBJ supervised project and edited the chapter text.


150 | Chapter 6

Substrate profiling of ω-transaminases to

rationalize substrate specificities

Abstract

The goal of this work is to determine if can we predict transaminase (TA) selectivity on the basis of the structural inspections; using crystal structures, homology models, and computational tools. Using a rapid spectrophotometric assay, we have determined extensive substrate profiles of the following class II TAs: five homologous β-phenylalanine TAs (β-TAs) and three homologous amine TAs (ATAs). In agreement with literature data, the enzymes showed high stereoselectivity and a broad substrate scope. Yet, there was essentially no overlap in substrate range, as illustrated by the lack of activity of the β-TAs with amines and the lack of activity of ATAs with β-phenylalanine. As all TAs studied here are fold-type I, pyridoxal-5’-phosphate (PLP)-dependent enzymes, they share the same structural fold and orientation of catalytic residues. Molecular dynamics (MD) simulations with scoring for reactive conformations could explain the enantioselectivity of the β-TAs in deamination reactions, but not the substrate range of the TAs. Sequence analysis identified motifs and crystal structures with differences that discriminate ATAs and β-TAs. The results are discussed in the context of the aim to develop computational protocols for predicting TA selectivity.


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1 | Introduction

While Chapter 2 focused on the discovery of enzymes through microbial enrichments from soil, this chapter examines the possibility to predict aminotransferase specificity from amino acid sequences and structural models. This effort is important for two reasons. First, as Chapter 1 highlights, the use of sequence databases requires computational methods to automatically predict gene function and protein (catalytic) properties to gain maximal value from sequencing data. Second, the discovery of enzymes that are more suitable for a certain biocatalytic conversion than a reference enzyme is often based on laboratory evolution, which would also benefit from improved predictability of specificity [1,2]. Sequence-activity correlations, homology modeling, docking, and molecular dynamics simulations can be used for such predictions [3-6]. For example, Seo and coworkers predicted ω-aminotransferase activity for 10 sequences based on sequence analysis, verified the activity in all 10 candidates, and identified specificity determinants near the active site entrance using structural inspection [7]. Determinants of substrate specificity have also been examined through protein engineering, guided by structural inspection and molecular docking [8-10]. As an initial step towards the development of a tool for predicting substrate profiles of homologous enzyme variants with different degrees of sequence identity, we examine if activity differences between transaminases can be explained through inspection and computational analysis of protein crystal structures. Transaminases (E.C. 2.6.1.X), using the PLP cofactor, catalyze the transfer of an amino group from a primary amine to the carbonyl group of an acceptor molecule (Figure 1). In this study, we included ATAs because of their industrial applicability in asymmetric synthesis of chiral amines, which occur in 75% of chiral pharmaceuticals [11]. ATAs primarily convert amines and do not require a carboxyl function in the substrate. This group includes several well studied enzymes, such as Vibrio fluvialis AT (VfAT) and Chromobacterium violaceum AT (CvAT). We also examine β-TAs, such as Variovorax paradoxus TA (VpTA) that acts on β-amino acids (β-aa’s). These enzyme can be used for asymmetric synthesis of β-aa’s or for kinetic resolution of racemic mixtures [12,13].


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Figure 1 | Reaction cycle of β-TA. First half-reaction shown with (S)-β-Phe and second half-reaction shown with α-ketoglutarate. Enzyme binding pockets indicated at internal aldimine step. E = enzyme.


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Figure 2 | Coupled enzyme assay for measuring TA activity. The β-TA and ω-TA activities were indirectly measured by spectrophotometrically monitoring NADH formation at 340 nm from the coupled reactions with glutamate dehydrogenase (GDH) and alanine dehydrogenase (ADH), respectively.

In this work, we explore the substrate profiles of ATAs and β-TAs using rapid enzyme assays (Figure 2), which yielded substrate profiles that we sought to explain through structure and sequence comparisons and partial computational analyses. We also examined if, within the family of β-TAs, differences in selectivity can be explained using homology models and MD simulations. Initial MD simulations showed that enantioselectivity of β-TAs can be explained by differences in proton abstraction, the external aldimines of non-preferred (R)-enantiomers of β-aa’s being unreactive. Further selectivity differences likely are related to other steps in the catalytic cycle and were correlated only to sequence profiles and motifs in the active site, of which the structural implications require further studies. Both ATAs and β-TAs are classified as ω-TAs, which are within the class II TAs of fold-

type I PLP-dependent enzymes. Class II TAs catalyze transamination of the amino group beyond the α-carbon in the case of non-proteinogenic amino acids, whereas class I TAs perform transamination at the α-position of amino acids. This classification was established by Mehta et al. [14] and recently reviewed by Schiroli and Peracchi [15], although the class II TAs chosen in our study are sometimes confusingly called class III TAs [4,16]. Substrate groups connected to the amine-substituted carbon atom are accommodated in a large (L) and small (S) binding pocket within their active site [9]. The former (also called the O-pocket) is positioned near the planar ketimine intermediate, closer to the hydroxyl of the PLP ring, whereas the S pocket (or P pocket) is closer to the PLP phosphate group (Figure 1). Substrate discrimination may occur at different stages of the catalytic cycle, i.e. during substrate binding, formation of the Schiff base with the PLP (external aldimine), or during proton abstraction leading to formation of the ketimine [3].

2 | Results & Discussion

2.1 | Model enzymes and bioinformatics analyses Fold-type I PLP-dependent class II TAs were chosen as template models to examine activity differences (Table 1).


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Table 1 | Summary of ω-transaminases investigated in this study

β-TAs Origin PDB Code

Vp-TA Variovorax paradoxus str. CBF3 4AO9

VarM7V-TA V. paradoxus str. M7V none*

VpS110-TA V. paradoxus str. S110 none**

VpEPS-TA V. paradoxus str. EPS none**

Mes-TA Mesorhizobium sp. str. LUK 2YKU

ATAs Origin PDB Code

CvATA Chromobacterium violaceum 4A6T

PdATA Paracoccus denitrificans 4GRX

VfATA Vibrio fluvialis str. JS17 4E3Q

* discovered in Chapter 2

** discovered in this study

This class comprises diverse activities in a single fold with similar main chain conformations around the active site, regardless of bound substrate [4,17]. The selected ATAs originate from Chromobacterium violaceum (CvATA), Paracoccus denitrificans (PdATA), and Vibrio fluvialis (VfATA). The chosen β-TAs originate from Variovorax strains M7V (VarM7V-TA), EPS (VpEPS-TA), and S110 (VpS110-TA). The latter two were genome-mined based on the query sequence of VarM7V-TA. Characterized β-TAs from V. paradoxus strain CBF3 (Vp-TA) and Mesorhizobium sp. strain LUK (Mes-TA) are examined alongside to represent ‘high’ and ‘low’ activity β-TAs, respectively [12,13,18]. Aside from their amino donor specificities with aryl β-aa’s, the dual specificity towards amine acceptors pyruvate and α-ketoglutarate also differentiate β-TAs from ATAs [4,19]. Based on the sequence identity comparisons in Table 2, the ATAs have low similarity with β-TAs and cluster in a different phylogenetic group. CvATA and VarM7V-TA share the highest identity of 27% between the two groups. All three ATAs are homologous with seq. IDs ranging from 36–91%. For β-TAs, the Variovorax TAs all share at least 82% seq. ID, while Mes-TA shares no more than 51% seq. ID with them.

Table 2 | Amino acid sequence identity matrix of ω-TAs

β-TA

s

Vp-TA 100%

VarM7V-TA 84% 100%

VpS110-TA 87% 82% 100%

VpEPS-TA 87% 84% 90% 100%

Mes-TA 49 % 51% 51% 51% 100%

ATA

s CvATA 23% 27% 20% 23% 12% 100%

VfATA 8% 8% 4% 12% 5% 39% 100%

PdATA 8% 8% 4% 12% 8% 36% 91 100%

Vp-TA VarM7V-TA VpS110-TA VpEPS-TA Mes-TA CvATA VfATA PdATA


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A phylogenetic tree (Figure 3) of ATAs and β-TAs shows distinct clades [19].

Figure 3 | Sequence analysis of fold-type I, class II ω-TAs. Phylogenetic tree with sequence motifs from (S)-β-TAs (green) and (S)-ω-TAs (orange). ATA-117 designated as out-group for tree construction. Transaminases in this study marked with asterisk (*) in the tree. High and low activity β-TAs indicated within clade 1. Clade 1 sequence alignments include the β-TA signature motif residues (black boxes) proposed by Crismaru et al. [18]. Full motif (R-X-[AVI]-X(6)-P-X(14)-D-G-X(8)-[EDNQ]-[YFW] shown above the alignment. The ‘flipping arginine’ responsible for dual substrate recognition in β-TA occurs at position 398. Clade 2 sequence regions represent fingerprint residues for ‘high’ and ‘low’ activity (S)-ω-TAs defined by Steffen-Munsberg and coworkers [4]. Residue numbering and sequence IDs (%) after entry name are relative to reference (ref.) sequence. See Table S1 for entry details.

A determinant that separates (S)-β-TAs from other (S)-ω-TAs is the aforementioned signature motif, (R-X-[AVI]-X(6)-P-X(14)-D-G-X(8)-[EDNQ]-[YFW], shown in Figure 3 [18]. This motif contains an arginine (Arg41 in Vp-TA) necessary to coordinate the α-carboxylate of (S)-β-Phe ((S)-B11) for catalysis. Clade 1 members contain this arginine and six other defining residues, as well as the ‘flipping’ arginine. However, β-TA from Sphaerobacter thermophilus (‘ST’) does not contain any of the three accepted residues (Ala, Val, Ile) at a position 3 of the motif. ST showed activity in a previous study with rac-β-Phe (3.3 U/mg) and even retained 50% relative activity when Arg36 (Arg41 in Vp-TA) was mutated to an alanine [20]. Interestingly, the R41A mutant of Vp-TA showed no activity with (S)-β-Phe [18]. ST also showed activity with both amino acceptors pyruvate and α-ketoglutarate,


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which is a common feature among β-TAs [20]. Molecular docking and kinetic resolution experiments implied ST to be (S)-enantioselective [20]. Altogether, it seems that ST is a peculiar (S)-β-TA that debunks some of the presumed determinants for aromatic β-aa activity by not requiring Arg36 for such activity. ST represents the earliest characterized ancestor of aromatic β-TA, and the activity levels toward (S)-β-Phe ((S)-B11) evolved from ‘low’ to ‘high.’ This suggests that the Arg41 and Arg348 of Vp-TA are partial selectivity determinants that likely promoted activity enhancements over time. Notably, the two (S)-β-TAs from Polaromonas sp. (PO) and Burkholderia graminis (BG) convert amines as well [19,21,22]. The fingerprint residues that generate ‘high’ or ‘low’ activities for (S)-ω-TAs in clade 2 were recently proposed by Steffen-Munsberg and coworkers [4]. Position 70 (PdATA residue numbering) binds to the carboxylate of the amino donor substrate. ‘High’ activity TAs usually contain a tryptophan at this position, resulting in weaker hydrogen-bonding compared to that of a tyrosine harbored by ‘low activity’ TAs. An exception to this pattern is found in the ‘high’ activity (S)-ω-TA from Bacillus megaterium (BM), which contains a tyrosine [4,23]. The presence of the other ‘high’ activity residue, Ala241, was implied to enable such activity based on mutational and structural studies [23] ‘flipping arginine’ responsible for dual-substrate recognition is present in all clade 2 members (it is shifted by eight residues in the TA from B. megaterium [23]. This arginine ‘flips in’ to bind the α-carboxylate of amino acceptor substrates and ‘flips out’ to accommodate side chains of β-aa’s [13,18]. Interestingly, the (S)-ω-TA from Alcaligenes denitrificans (AD) has activity with β-aa, 3-aminobutyric acid (B7) that was 10-fold higher (77 U/mg) than its activity with (S)-α-MBA (Am1) [24]. Opposite to what is observed for β-TA phylogenetic analyses, ATAs seem not to have evolved into ‘low’ to ‘high’ activity sequences.

2.2 | Substrate profiling Amines and amino acids with diverse steric and electronic properties were chosen for substrate profiling with the ω-TAs (Figure 4). All enzymes were produced in E. coli with sufficient yields and purities. Below, general trends are summarized for a selection of TAs investigated here. Full activity (Figures S1 & S2) and kinetics (Figures S3 & S4) profiles of the TAs are provided in Supplementary Information.


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Figure 4 | Substrates tested in this study. (a) Amines (‘Am’), (b) β-aa’s and other ω-aa’s (‘B’), and (c) α-aa’s (‘A’).

2.3 | Substrate profile of ATAs The activities of the three ATAs were examined with a series of α- and β-aa’s and aliphatic and aromatic amines (Table 3, Figure S1). CvATA with piperonyl amine Am6 generated the highest activity (0.71 U/mg) among these ATA-substrate combinations. Other substrates rapidly converted by all three ATAs were the benzyl amines Am1, Am2, and Am6, and the aliphatic α-aa’s A1 and A2. Conversion of the latter two substrates by PdATA was previously observed [9], while activities of the three ATAs with aromatic (Am3, Am6) and aliphatic (Am9) amines are first reported here. Low activities were found with the short-chain aliphatic amines Am7 and Am8. Since isopropylamine Am7 is often used at high concentrations as an aminating agent in TA reactions [1,25], this may be due to low affinity of the enzyme for this substrate, in agreement with the activity being strongly concentration-dependent. Thus, bulky aromatic and elongated aliphatic α-aa’s are preferred by the three ATAs. Since pyruvate is used as the amino acceptor and aminotransferase reactions are (mechanistically) reversible, the L-alanine should also be a substrate, leading to the conclusion that the enzymes have a very restricted substrate spectrum with respect to α-aa’s. None of the ATAs are active with the β-aa’s tested here.


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Table 3 | Specific activity profiles of transaminases

Type Entry Substrate Pd Cv Vf Mes VpVarM7V

Aminesaromatic Am1 (S)-a -MBA 0.58 0.47 0.53 < < <

Am2 Benzylamine 0.30 0.39 0.22 < < <

Am3 4-MBA 0.15 0.39 0.11

Am4 Phenethylamine 0.22 0.10 0.27 < < <

Am5 4-phenylbutylamine 0.06 0.13 0.05

Am6 Piperonylamine 0.45 0.71 0.26 < < <

aliphatic Am7 Isopropylamine 0.04 0.02 0.06

Am8 3-Aminopentane < 0.01 0.02 < < <

Am9 2-Amino-5-methylhexane 0.17 0.02 0.08

Am10 Amylamine 0.11 0.12 0.14 < < <

a -aa'saliphatic A1 L-norvaline 0.36 0.39 0.36 < < <

A2 L-norleucine 0.32 0.53 0.20 < < <

polar A3 L-Asp < < < + − −A4 L-Glu < < < +A5 2,6-DAPA 0.01 < <

A6 L-Cys < < 0.31 < < <

A7 L-Ser 0.01 + < − − −A8 L-Thr < < < < < <

aromatic A9 L-His < < < < < <

A10 D-2-phenylglycine < < < < < <

b-aa'saromatic B11 (S )-β-Phe 1.80 6.30 8.50

B14 (S )-β-Tyr 3.20 13.40 24.60

aliphatic B1 (S )-β-Leu 2.10 4.40 6.00

B2 (R )-3-amino-5-methylhexanoicacid 7.00 4.90 8.00

B6 β-Ala < < < < < <

B7 3-aminobutyricacid + + 7.80 2.40 2.40

B4 3-A-3-CPPA 2.80 4.10 7.00

w -aa B9 4-aminobutryicacid 0.23 0.33 0.17

B10 6-aminocaproicacid 0.07 0.36 0.06 < < <

AS.A.=specifcactivity

'+'and'−'signsindicatepreviouslyreportedactivityorinactivity,respectivelyBlank=substrateactivitynottestednorfoundinliteratureThinboxes:qualitativeagreementinliterature(seeTablesS2andS3forreferences)Boldboxes:qualitativedisagreementinliterature

ATAs b -TAs

'<'indicatesnodetectableactivityabove0.003U/mg

Numbersindicatehighestactivitiesmeasuredinthisstudywith2,8,or16mMsubstrateandcosubstratepyruvate(ATAs)ora -ketoglutarate(b -TAs)

S.A.(U/mg)A


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Overall, the three ATAs show similar specificity ranges with only a few pronounced differences. The first difference is that cysteine A6 is only converted by VfATA, which has not been mentioned in prior studies [17,26-32]. Second, long-chain ω-aa B10 gives a 6-fold higher activity with CvATA compared to the rest, whereas ω-aa B9 that is shorter by two carbon atoms produced less disparate activities. Thus, CvATA converts bulky and elongated substrates best, which is further supported by its high activity with the elongated aliphatic α-aa A2, aryl amine Am5, and ω-aa B10. CvATA is also more active with aromatic and long-chain aliphatic amines than PdATA [17,29], which is more active with small phenyl amines Am1 and Am2 [17]. For L-Ser, no activity was found with VfATA, while Shin et al. previously reported activity at a 10°C higher reaction temperature using HPLC analysis, which could possibly explain this difference [27]. Substrate profiling reported in the literature for CvATA [17,26,29,33], VfATA [17,26-32], and PdATA [9,34-37] so far identified only 3-aminobutyric acid (B7) as a β-aa accepted by these ATAs (Tables 3 and S3). This appears to be the only overlapping activity between the class II β-TAs and ATAs, in addition to aforementioned PO and BG β-TAs that have converted amines [19,38,39]. Thus, amines are converted by TAs with a broad substrate scope that mostly excludes β-aa’s [15].

2.4 | β-TA substrate profiles The β-TAs show activity with a range of β-aa’s (Table 3). Common trends are their activities with both aromatic and aliphatic substrates. Additionally, they show selectivities toward (S)-enantiomers of aromatic and (R)-enantiomers of aliphatic β-aa’s that share similar chiral configurations. The selectivities towards the (S)-enantiomers of aliphatic β-aa B1 by all β-TAs conform to this since there is a change in the Cahn-Ingold-Prelog priority at the chiral center. A prominent difference among the group is that the Variovorax β-TAs prefer both bulky aromatic and aliphatic β-aa’s, whereas Mes-TA prefers bulky aliphatics. Kim et al. [12] confirms this trend for Mes-TA by reporting up to a 2-fold higher activity with racemates of bulky aliphatic β-aa’s, B2 and B7, relative to its activity with a racemate of aromatic substrate B11 (Table S2). For Vp-TA, Crismaru et al. [18] showed up to an 87-fold higher activity with aromatic β-aa’s (S)-B11 and (S)-B14 than with aliphatic substrates (S)-B1, (R)-B2, and B7 (Table S2). The bulky aliphatic β-aa B4 represents a new substrate converted by Vp-TA (4.1 U/mg) and Mes-TA (2.8 U/mg). Additionally, both aromatic (S)-B14 (3.2 U/mg) and aliphatic (S)-B1 (2.1 U/mg) substrates give new conversions by Mes-TA. The highest activities with aromatic ((S)-B14) and aliphatic (B4) β-aa’s were generated by VarM7V-TA (24.6 U/mg) and VpS110-TA (Table 3, Figure S2, 12.9 U/mg), respectively. In agreement with prior studies [12,13,18], our results confirm Mes-TA as a ‘low activity’ aromatic β-TA with a preference towards aliphatic β-aa’s (Table 3 and Figure S2, activity; Figure S4, kinetics). In general, all β-TA activities are confined to β-aa’s (Tables 3 and S2) with the exception of alanine. This trend was also found by Crismaru et al. [18], who showed that Vp-TA is not active with (S)-α-phenylalanine and (S)-α-tyrosine, based on HPLC analyses. On the contrary, Kim et al. [12] showed activity by Mes-TA with aliphatic (L-valine, L-leucine, L-lysine) and aromatic (rac-phenylalanine and L-tyrosine) α-aa’s using HPLC as well. In our assays, these α-aa’s generated no activity with Mes-TA. Crismaru et al. also reported Mes-TA to be inactive with α-phenylalanine using HPLC analysis, which leaves the perplexing activity reported by Kim and coworkers unexplained.


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The β-TAs are more effective catalysts than the ATAs, as the slowest β-TA (Mes-TA) is still about 100-fold more efficient (~53 s1•mM-1) than the fastest ATA (PdATA) with the preferred substrates. By comparing the activities reported here for Vp-TA with literature data, some differences are apparent (Table S2). Vp-TA shows an ~2-fold higher activity with (S)-B14 than with (S)-B11 (6.3 U/mg), which is opposite to the trend observed by Crismaru et al., who reported an ~1.6-fold lower activity with the former [18]. The lower reaction temperature (27˚C vs. 30˚C) could partially explain this difference, but it could also be due to differences in reaction setup (i.e., coupled assay vs. HPLC used in the prior study [18]. The data obtained with the β-TAs confirm that their activity profiles are well separated from ATA profiles, which do not act on β-aa’s (except β-aminobutyrate B7). The well-studied class I α-TAs also lack activity with β-aa’s [37,40-43], indicating pronounced clustering of substrate selectivities among enzymes within the fold type I, class II transaminase family, and triggering a search for a structure-based explanation of the observed trends. 2.5 | Structural comparisons of ATAs and β-TAs Class II TAs are catalytically active as homodimers, where each subunit folds into a PLP-binding domain and an NC-domain (comprises N- and C-termini) that form one active site [44,45]. PLP is positioned in a cleft between these domains. As seen in Figure 5a that depicts structures of an ATA (CvATA, PDB: 4AH3) and β-TA (Vp-TA, PDB: 4AO9), major differences include a larger distance between the N- and C-termini in ATA, longer α-helices in ATA that line the back-side of the cleft (Figure 5b), and longer α-helices in the NC-domain of β-TA. Although specificity determinants are not obvious from these differences, Deszcz and coworkers identified four structural regions (A, B, C, D) of class I and II TAs presumed to be specificity determinants [8]. These were the least conserved active site regions based on crystal structure alignments of CvATA and 28 other diversely related TAs. Interestingly, they observed that structural changes in three regions (A, B, D) can discern between class I α-TAs and class II ω-TAs [8]. However, we found changes within these regions that discriminate between β-TA and ATA members of class II TAs. Based on the sequence and 3D structural comparisons (Figure 6a & b) of CvATA, Vp-TA, and PdATA (PDB: 4GRX), the most prominent differences occur in regions A and D. Region A is the largest region, comprising two α-helices and three β-strands in ATAs (stretch of 53-56 residues) and two additional α-helices in β-TA (73 residues). Thus, region A in β-TA seems to restrict access to the active site more effectively than in ATAs by protruding extensively into solvent (Figure 6b). However, region D is likely more flexible in β-TA with a nine-residue loop, whereas ATA harbors a 21-residue stretch encapsulating a loop and two α-helices. As seen in the Figure 7 of the zoomed-in active sites, this region contains a Tyr residue (Tyr153 in CvATA) conserved across both TA groups that potentially serves as a gateway for substrate access and indirectly binds the phosphate moiety of PLP via hydrogen bonding with water [8]. The catalytic lysines and arginines do not fall within any of the regions. Overall, a balance of structure and flexibility between regions A and D may represent a novel discriminant between β-TA and ATA specificity. Interactions that influence this balance are not known.


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Regions B and C provide less clues as structural determinants of specificity. Region B is a 10-residue loop that lines the P-pocket and is slightly more extended in β-TA. However, its effect on specificity is not evident from structural inspection alone. Region C lines the central entrance channel via a 12-residue loop in the ATAs with a β-strand present only in the structure of PdTA. In β-TA, region C is five residues shorter. This region includes conserved Phe’s (Phe88/89 in CvATA) in ATAs and forms the O-pocket for both TA groups. Although Deszcz and coworkers could not correlate specificity with structural changes in region C [8], loop flexibility differences within this region were implicated as a specificity determinant between CvATA and a homolog from Pseudomonas [46]. Thus, MD simulations may help gauge the roles of regions B and C in dictating specificity.

.

Figure 5 |Structural comparisons of class II ω-transaminases. (a) Dimers of CvATA (PDB: 4AH3) and Vp-TA (PDB: 4AO9) with PLP (magenta) bound in each monomer. One monomer depicted as thin backbone trace. Secondary structures are colored grey (loops), yellow (β-strands), and blue (α-helices). N- and C-termini are labeled. (b) Monomers of each TA with PLP positioned in the cleft between the PLP-binding domain (left-half)


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and the NC-domain (right-half). CvATA has a more open NC-domain, whereas the same domain in Vp-TA is more closed. Prominent length differences reside in the α-helices (*) that line the back-side of the active sites and periphery of the NC-domains.

Figure 6 | Structural analysis of class II ω-transaminases. (a) Sequence alignment of regions A (red), B (green), C (yellow), and D (cyan) defined as specificity determinants [8]. Secondary structure assignments are indicated above respective sequence as an α-helix (black box), β-strand (grey arrow), or turn (light grey arrow). Original residue numbers indicated for each sequence. (b) Top: Top-view of a single active site from CvATA (PDB: 4AH3), PdATA (PDB: 4GRX), and Vp-TA (PDB: 4AO9) with highlighted regions and substrate entrance channels. Peptide backbone outside of regions displayed as thin chain traces (light grey). Internal aldimine (pink) indicated in each structure. Bottom: Side-view of active sites.


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Figure 7 | Active-site residues in specificity regions. Zoomed view of active sites for (a) CvATA (PDB: 4AH3), (b) PdATA (PDB: 4GRX), and (c) Vp-TA (PDB: 4AO9). Specificity regions defined by Deszcz et al. [8] are color-coded for regions A (red), B (green), C (yellow), and D (cyan). O- and P-pocket residues are shown as sticks. White sticks indicate residues not contained within specificity regions. Notably, the conserved active site lysines (forms Schiff base with PLP to generate internal aldimine) and arginines, along with the PLP-binding aspartates do not reside within substrate recognition regions.

2.5 | Active site comparisons of ATAs CvATA shows higher activities with aromatics and long-chain aliphatic substrates compared to PdATA. Rausch and coworkers concluded that the larger, more hydrophobic active site of CvATA causes this trend. In particular, the active site overlay in Figure 8a shows more hydrophobic residues at two positions in CvATA (Met56, Met166) compared to the respective positions in PdATA (Asn53, Lys163). Also, there are conformational differences of three conserved residues in CvATA (Phe88/89, Arg416) and PdATA (Phe85/86, Arg415). These conformational and hydrophobicity differences are proposed to influence the specificity differences between CvATA and PdATA [17]. Upon inspecting the hydrophobicity surface of the active site pockets for both enzymes in Figure 8b, a higher degree of hydrophobicity is subtly apparent near Met positions 56 and 166 in CvATA relative to the respective Asn53 and Lys163 in PdATA. The conformational differences of the ‘flipping arginines’ (positions 416 in CvATA and 415 in PdATA) cause prominent differences in accessibility of the active sites. This arginine ‘flips in’ to bind the α-carboxylate of substrates and ‘flips out’ to accommodate large side chains of ω-amines [15]. Thus, judging specificity effects from the arginine switches is difficult. An apparent difference among the two active sites is the compression between the O- and P-pockets in PdATA that transpires from the conformation of Phe86, which is more distant to Arg415. The juxtaposition of Phe89 and Arg416 in CvATA ablates such compression to enlarge the O-pocket. As mentioned before, region C harbors this Phe in both ATAs. Overall, mobility differences of the flipping arginine and flexibility of the loop in region C could play an important role in specificity differences among ATAs, which has already been proposed to allow conversion of α-aa’s by CvATA [29,46]. However, it is difficult to discern concrete specificity determinants from the above active site inspections alone. For example, backbone rigidity differences and active-site lysine mobility were proposed as specificity determinants of β-alanine conversion by an ATA from Pseudomonas aeruginosa in comparison to the inactivity by CvATA [46].


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Figure 8 | Active site comparison between ATAs. (a) Overlay of active sites from CvATA (light orange, PDB: 4AH3) and PdATA (cyan, PDB: 4GRX). Positions indicated that contain the “flipping arginine” (#) and conserved residues proposed to influence specificity through substitutions (underlined) or conformational changes (positions 416, 88, and 89 in CvATA) [17]. Apostrophe (‘) indicates residues from another monomer. Residue pairs within regions defined in Figure 5 indicated with a prefix (A, B, C, or D). (b) Structural pockets calculated by the CASTp server to compare surface hydrophobicity [47].


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2.6 | Active site comparisons of β-TAs Vp-TA and VarM7V-TA prefer both bulky aromatic and aliphatic β-aa’s [18], whereas Mes-TA prefers bulky aliphatic β-aa’s [12,13]. After a homology model of VarM7V-TA was generated using Vp-TA as a template structure, an overlay of β-TA active sites was examined (Figure 9a). The only major structural differences between the these β-TAs reside in the O-pockets, where residues vary at only three positions: Val43 (Vp-TA)/Ile56 (Mes-TA), Ser298/Ala312, and Phe400/Met414. Both Variovorax TAs have identical residues. It has been shown that a triple mutant of Mes-TA harboring the Vp-TA residues at these positions improves activity with (S)-β-Phe ((S)-B11) by two-fold, confirming their role as a specificity determinant [18]. Thus, the higher degree of aliphatics at these positions within Mes-TA likely explains its preference towards aliphatic substrates. Potential edge-to-face or π-interactions between Phe400 and aromatic substrates within the active site of Vp-TA and VarM7V-TA would be a key factor in its faster conversion of these substrates compared to Mes-TA. Another specificity determinant is the ‘flipping’ arginine, which enables dual-substrate recognition in β-TA. It flips in to bind the α-carboxylate of α-keto acids and flips out to accommodate sidechains of β-aa’s [13,18]. Whether its conformational flexibility affects specificity by controlling access to the active site remains elusive, given that different conformations have been observed in different monomers of the same enzyme structure [15].

Figure 9 | Active site comparison between β-transaminases. (a) Overlay of active sites from Vp-TA (green, PDB: 4AO9), VarM7V-TA (cyan, homology model), and Mes-TA (pink, PDB: 2YKU) showing positions of the “flipping arginine” (#), portion of the β-TA signature motif (*), and O-pocket differences (underlined) that influence reaction rates [18]. Apostrophe (‘) indicates residues from another monomer. Residue positions that map to one of the specificity regions have a prefix indicating the region. Residue labelling corresponds to legend order (b) Overlay of four structural regions (A, B, C, D) in β-transaminases that influence specificity [8]. Overall, β-TA specificity is likely influenced by differences in active-site residues and mobility of the arginine switch, although their exact modes of influence remain elusive.


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With regard to the defined structural regions as specificity determinants originally described to discriminate between class I and II TAs [8], Figure 6b shows that a discrimination among class II members (ATAs vs. β-TAs) is possible upon global inspection. However, rationalizing specificity differences among β-TAs studied here, using the same approach is not possible, as seen in the overlay of the respective regions in Figure 9b. Five out of the seven key active site residue positions (Figure 9a) map to regions A, B, and D, which underscores the importance of these regions in controlling substrate specificity (Figure 9a). However, because these regions show similar backbone structures among β-TAs and no major structural changes were observed for Mes-TA upon substrate binding [13], the influence of these regions on specificity should be related to side chain variations.

2.7 | Active site comparisons of β-TAs and ATAs The β-TAs show higher reaction rates and less substrate promiscuity than the (S)-ATAs, which could be due to global structural differences, as evaluated in section 2.5 (Figure 6). Further selectivity determinants become apparent upon comparing active sites. Based on a comparison with the β-TA motif sequence from Vp-TA in Figure 10a, five of the seven corresponding positions in PdATA harbor different residues. Also, positions 1 and 2 in both enzymes have disparate 3D coordinates (Figure 10b). Position 1 in Vp-TA contains Arg41 that pairs with Glu75 (position 6) to coordinate the α-carboxyl group of β-aa substrates. PdATA lacks this residue pair, which explains its general inactivity with β-aa’s. All β-TAs in clade 1 of Figure 3 contain the Arg-Glu pair, making it the most prominent structural determinant of β-TA activity. Positions 8 and 9 dictate ‘high’ or ‘low’ activities with amines (Am1, Am2, and Am4) by (S)-ATAs [4]. As mentioned in Section 2.1, an alanine at position 9 is crucial for ‘high’ amine activity, while tryptophan or tyrosine are preferred at position 8. Although Vp-TA contains an alanine at position 9, a threonine at position 8 is not found in (S)-ATAs [4]. Lastly, the conserved ‘flipping arginine’ at position 10 is crucial for both enzymes to enable dual substrate recognition. The arginine at position 1 within β-TA promotes higher reaction rates with less promiscuity compared to (S)-ATAs [15]. The presence of two arginines seems crucial for β-aa conversion, which is generally not possible with ATAs (3-aminobutyric acid B7 as an exception [27,29]).


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6Figure 10 | Active site comparisons between β-TA and ATA. (a) Partial sequence alignment containing motif residues that define β-TA activity and ‘high’/’low’ ATA activities (*). ‘Arg switch’ represents location of flipping arginine responsible for dual-substrate recognition. (b) Three-dimensional overlay of motif residues from Vp-TA (magenta, PDB: 4AO9) and PdATA (cyan, PDB: 4GRX) aligned structures, which correlate with numbered residues in (a). Positions 1 and 2 which influence β-TA specificity show the most drastic locational differences among β-TA and ATA.

2.8 | Enantioselectivity prediction of a β-TA We examined if MD simulations could be used to rationalize the enantioselectivities of β-TAs using the crystal structure of Mes-TA as a model. As shown in section 2.4, all tested β-TAs are selective towards the (S)-enantiomer of aromatic substrate β-Phe (B11) and the (R)-enantiomer of


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aliphatic substrate β-homoleucine (B2). Through Schiff-base formation between substrate and the PLP cofactor, β-TAs liberate the catalytic lysine (Lys280 in Mes-TA) from the internal aldimine adduct (step 1, Figure 1). This lysine then abstracts the Hβ (step 2), for which its bond to the Cα atom of the external aldimine intermediate should be perpendicular to the plane of the pyridoxal imine system of PLP [48]. The formation of reactive conformations was examined in MD trajectories that were obtained during simulations of external aldimine enzyme complexes. To evaluate stereoselectivities of Mes-TA, two parameters were measured: (1) the distance between the catalytic residue (Nζ of Lys280) and substrate (Hβ); and (2) the angle (θ) between Hβ and the plane of PLP (Figure 11a). We define a contact between enzyme and substrate when the distance in (1) is less than or equal to 2.75 Å, which is the sum of the van der Waal radii of N [1.55 Å] and H [1.2 Å]. Additionally, we define a valid contact when θ is near 90°. During 40 independent MD simulations (25 ps each with different starting velocities) of the external aldimine (covalent substrate-PLP complex) docked in different poses, the number of contacts were tallied every 100 fs and averaged over the 40 simulations. Both active sites in the homo-dimer were simulated to enhance statistical significance. In Figure 11b, the number of average contacts relative to the native substrate, (S)-β-Phe ((S)-B11), are given for a variety of (S)- and (R)-enantiomers of aromatic and aliphatic substrates, along with the corresponding θ-angle averages (Figure 11c). The results generally agree with the observed enantioselectivities of Mes-TA in Table 3. For example, the number of favorable contact distances exceeded 60% of the sampled simulations for (R)-B2, (S)-B1, (S)-B11, (S)-B12, and (S)-B14. The corresponding θ-values were at least ~90°, whereas the inactive substrates (opposite enantiomers) showed predominately 0% valid contact distances and negative θ-values between ~0 to -60°. That active substrates produced θ-values substantially greater than 90° and the inactive substrate ((R)-B12) produced a marginal number of valid contacts (~5%) suggest further improvements are needed to the in silico method. A culprit of error stems from the docking protocol, which produces different binding modes for both monomers of the enzyme. Therefore, the starting structures of the MD simulations are significantly different and it apparently required additional MD steps to find the “real” conformation.


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6Figure 11 | Enantioselectivity prediction of β-TA. (a) Active site geometry of Mes-TA with modeled substrates that are active (left) and inactive (right). Contact distances (Å) between enzyme (Nζ of Lys280) and substrate (Hβ) and the angle (θ) between Hβ and the plane of PLP are indicated in red. Bottom panels show evaluations of the number of valid enzyme-substrate contacts (b) and angles (c) during MD simulations.

2.9 | Prospects for computational rationalization of transaminase selectivity The intricate catalytic cycle of TAs complicates the development of generally applicable tools for selectivity prediction. First, substrate discrimination may happen at different steps in the catalytic cycle (Figure 1), internal aldimine and PMP-ketone formations. This limitation may be mitigated by a recent bioinformatics analysis of class II TAs that revealed a correlated network of 13 active site residues that influence reaction rates and substrate specificity [4]. Second, conformational changes also play a role in specificity [46,49]; these may be too slow to model by molecular dynamics and


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even escape detection in flexible docking protocols (e.g., the ‘flipping arginine’ for dual substrate recognition of acidic substrates [15,18,50,51]. Wilding et al. made advances in this realm by running 200 ns MD simulations to identify 31 dispersed residues in ω-TA from a Pseudomonas sp. that likely influence substrate specificity through dynamics. Third, if a specific rate-limiting step is identified [52], it may be difficult to establish its rate using the near-attack conformer scoring method which we and others used for selectivity modeling [53-55], since its outcome is critically dependent on the accuracy of conformational sampling by molecular dynamics and the details of the reaction mechanism. Whereas quantum chemical methods may help to identify the reaction path of individual steps [52,56], they are computationally very costly and not easily implied in design and screening protocols. Nevertheless, a quantum chemical analysis was recently applied to determine the energy profile of the ω-TA half-reaction from (S)-1-phenylethylamine (Am1) to acetophenone using the partial active site of CvATA [52]. The authors tagged proton abstraction from the external aldimine as a rate-limiting step (step 2 in Figure 1) with an inferred kcat of 0.1 s-1 [52]. As a first step towards the development of a selectivity prediction tool, we examined the possibility to use MD simulations. This worked well for a small set of substrates, for which enantioselectivity could be predicted by scoring near attack conformers (NACs). This suggests that even if binding and formation of the Schiff base occurs, only the preferred enantiomer will react. The other enantiomer could be an inhibitor, which was described for VfATA [57] and implied here for β-TA (B14, Figure S2). Han et al. recently applied similar NAC analyses on unreactive ketones with ω-TA from Ochrobactrum anthropic to engineer a mutant with 105-fold improved reactivity with butyrophenone [6]. For other substrates, MD simulations did not provide clear results in the sense that both enantiomers of the external aldimine produced reactive conformations in MD simulations, even for substrates that were converted with high enantioselectivity (data not shown; Carlos Ramirez, unpublished observation). Instead, substrate binding may need to be examined. While this work was in progress, Sirin and coworkers described the use of binding energies to qualitatively predict with 60% accuracy the reactivity of 90 characterized VfATA variants with the imagabalin precursor, 3-keto-5-methyloctanoate, using Molecular Mechanics Generalized Born Surface Area (MM-GBSA) scoring [3]. The MM-GBSA is a physics-based scoring method using an implicit solvent model that accounts for desolvation, protein stability, and shape complementary of the enzyme-substrate complex [58,59]. Thus, computational design tools such as MM-GBSA scoring approach may be applied to further examine the possibility to predict substrate profiles [58]. In addition, the possibility to engineer TA selectivity by Rosetta Enzyme Design [60], which optimizes active sites for substrate binding in reactive poses, needs to be examined. Based on the structural analyses in previous sections, the link between specificity and structural region organizations among class II TAs may also be considered during the development of prediction methods [8]. However, structure-guided single point mutations converted a class II TA to a class I TA without perturbation of these structural regions, emphasizing the need to assess various potential determinants when developing prediction models [8]. The rapid enzyme assay used here is a very powerful way to obtain large experimental datasets. As long as individual predictions are not sufficiently reliable, such datasets are needed to calibrate computational tools. Given the complexity of specificity determinants within the class II TAs


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examined here, developing a generalized prediction tool for substrate specificity will be the ultimate challenge ahead.

3 | Materials & Methods

3.1 | Chemicals The suppliers of the following chemicals are: Sigma-Aldrich (DMSO); Acros Organics (PLP, DMF, NAD+); Roche (glutamate dehydrogenase from beef liver, 3000 U); and Fluka (α-KG). Amines Am3 and Am5 were provided by the Katalin Barta lab at the University of Groningen.

3.2 | Cloning of β-transaminase genes The discovery and cloning of the β-TA from Variovorax paradoxus M7V strain (VarM7V-TA) is reported in Chapter 2. The Vp strains S110 (Korean Collection for Type Cultures, KCTC) and EPS (Paul Orwin, California State University, U.S.A.) were obtained from outside sources for subsequent cloning of the respective TA genes. Except for the Mes-TA and Vp-TA genes that were already sub-cloned into a pET28b(+) expression vector previously in our lab [13,18], the remaining transaminase genes were amplified from their respective gDNA source using the designed primers (Eurofins MWG) summarized in Table 4 and sub-cloned into a pET28b(+) plasmid via NdeI/HindIII restriction enzyme sites.

Table 4 | Summary of primers used for cloning β-TA genes

Gene Target Primer label Direction Length (bp)

DNA Sequence (5’→3’)

VpEPS-TA EPSfwd_NdeI Forward 41 5’-gcgcggcagccatATGACCGACGCCGCCATCGACCAAGCC C-3’

EPSrev_HindIII Reverse 45 5’-GCGGCCGCAAGCTTTTACTACTTCGCGCTAGACAAC

AGCGCGCGGTACTCGCTG-3’

VpS110-TA S110fwd_NdeI Forward 56 5’-GCGCGGCAGCCATATGAGCGATTCCGCAATCGACCA GTCCCTTGCCCAGGCCTTCC-3’

S110rev_HindIII Reverse 45 5’-GCGGCCGCAAGCTTTTACTACTTCGCGCTAGGCAGC AGCGCGCGG-3’

VarM7V-TA M7fwd_NdeI Forward 41 5’-gcgcggcagccatATGACCCTTGCCCCCATCGACCACGCC C-3’

M7rev_HindIII Reverse 40 5’-GCGGCCGCAAGCTTTTATCAGCCCAGGGCGGGCAG CAGCG-3’

3.3 | Protein expression and purification | β-transaminases The β-TAs characterized in this study contain an N-terminal His6-affinity tag (MGSSHHHHHH) followed by a 10-amino acid linker (SSGLVPRGSH) for purification. The expression cultures (1 L TB medium, 50 μg·ml-1 kanamycin, with pET28b(+)/E. coli C41(DE3) expression system) for all five


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β-TAs were inoculated with a 1:100 (v/v) dilution of an overnight inoculum at 37°C. The cultures were induced with IPTG (0.8 mM) at an OD600 ~0.6-0.8 and then incubated according to Table 5.

Table 5 | Protein expression conditions (post-induction) of β-transaminases

Protein Origin Size (kDa) Temperature Time (h)

Vp-TA Variovorax paradoxus 48.6 30°C 16-20VpS110-TA Variovorax paradoxus S110 48.8 30°C 6VarM7V-TA Variovorax paradoxus M7V 48.3 24°C 20VpEPS-TA Variovorax paradoxus EPS 48.6 30°C 6Mes-TA Mesorhizobium sp. strain LUK 50.0 17°C 60

The expression cultures were then harvested by centrifugation (20 min, 6,500 rpm, 4°C) in 1 L centrifuge bottles. After media was decanted, each cell pellet was washed once with Buffer A (20 mM Tris-Cl pH 8, 100 mM NaCl) and then resuspended with same buffer containing 1 protease inhibitor tablet (Complete Ultra tablet, EDTA-free, Roche). The cell resuspension was disrupted via sonication and then centrifuged (45 min, 39k x G, 4°C). The cleared lysate was then combined with a Cobalt binding resin (~2 mL, TALON superflow by Clontech) and incubated on an orbital shaker (1 h, 4°C) before applying the resin-bound target protein to a gravity-flow column (25 mL limit, RT, Bio-Rad). The flow-through was collected and resin was washed with Buffer A (15 mL). Two subsequent washes were applied (15 mL) with appropriate dilutions of Buffer B (20 mM Tris-Cl pH 8, 100 mM NaCl, 100 mM imidazole) to give 5 and 10 mM imidazole final concentrations, after which each flow-through was collected. The protein was eluted with Buffer B (10 mL) and 3 mL fractions were collected and immediately followed up with a desalting step with gravity-flow columns (Bio-Rad). The desalting column was equilibrated with Buffer C (20 mL, 25 mM Tris-Cl, pH 8) before loading the respective 3 mL protein fraction. The flow-through was discarded before adding Buffer C (4 mL) to the column for target protein elution (yellow fractions were collected that indicate PLP-bound enzyme). The final purified, desalted protein solution was stored in 0.5 mL aliquots (-80°C). Mes-TA was noted to precipitate if stored at concentrations above 3 mg·mL-1.

3.4 | Protein expression and purification | amine transaminases For the overexpression and purification of CvTA (51 kDa) or VfTA (52 kDa), the pET28b(+)/E. coli C41 (DE3) expression system was used. Each enzyme contained an N-terminal His6-tag (MGSSHHHHHH) followed by a 10-amino acid linker region (SSGLVPRGSH). Pre-cultures grown in TB medium (10 mL, 100 μg·mL-1 kanamycin) were used to inoculate 1L TB medium (100 μg/mL kanamycin) in a 5 L baffled shake flask and incubated (37°C, 135 rpm) until reaching OD600 ~0.6, which was then induced with IPTG (1 mM). The induced cultures incubated overnight (30°C, 135 rpm) and were harvested by centrifugation (6,000 rpm, 4°C, 20 min). Pellets were re-suspended with buffer A (20 mM Tris, 0.5 M NaCl, 20 mM imidazole, pH 8.0) and then subjected to sonication at 4°C (20 min total, 5 s on/off, 70% amplitude). The lysate was centrifuged (45 min, 15,000 rpm, 4°C) and resulting


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supernatant was loaded into a 25 mL column (Bio-Rad) with 2 mL of cobalt resin (Talon) that was pre-equilibrated with Buffer A for gravity-flow IMAC affinity purification at 4°C. The loaded column was incubated on an orbital shaker for 1 h and then flow-through (FT) was collected. The bound column was washed with Buffer A (24 mL) before eluting target protein with 4 mL of Buffer B (20 mM Tris, 0.5 M NaCl, 500 mM imidazole, pH 8.0) while 0.5–1.0 mL fractions were collected. The fractions were analyzed via a 12% SDS-PAGE. The relevant fractions were combined and loaded on a HiPrep Desalting 26/10 column (GE Healthcare) that was pre-equilibrated with storage buffer (50 mM KPi, 10% Glycerol, pH 8.0) and attached to an ÄKTA purifier FPLC (Amersham Pharmacia Biotech) with a flow rate of 10–15 mL·min-1 in order to remove imidazole. The relevant fractions were collected and analyzed with the aforementioned SDS-PAGE analysis. The final samples were aliquoted and stored at -20°C. Protein concentration was determined using the Bradford assay. Purity of all enzymes were judged using 12% SDS-PAGE analyses. For the overexpression and purification of PdTA (47 KDa), the pASK/E. coli C41 (DE3) expression system was used under tight transcriptional control of the tetracycline promoter/operator. The expression construct (pASK-IBA35+-PCR6) was kindly provided by Prof. Arne Skerra [17]and contains an N-terminal His6-tag (MASRGSHHHHHH). Enzyme was produced in the same manner as above, except that LB medium (50 μg·mL-1 ampicillin) was used for growth medium and AHT (anhydrotetracycline-2 mg·mL, Clonetech) at 0.2 μg·L culture was used to induce expression. Perfusion IMAC using the above ÄKTA purifier (flow rate: 5 mL·min-1) and a HisTrap 5 mL column (GE Healthcare) that was pre-equilibrated with Buffer A (no imidazole) were used for protein purification. After washing column (5 column volumes) containing bound protein with Buffer A (no imidazole), target protein was eluted using an elution gradient protocol (100% Buffer B reached over a 12 min period with 2 mL fractions collected). Relevant fractions were collected and desalted using the aforementioned protocol. The final fractions were checked for activity before combining, aliquoting, and storing at -20°C. Over the course of the project, enzyme activity was measured for each enzyme and compared to initial measurements to ensure that no loss of activity was observed.

3.5 | Activity measurements | β-transaminases The transaminase activities were measured indirectly via a couple-enzyme assay using glutamate dehydrogenase (GluDH) as the ancillary enzyme. Before performing substrate profile screening for the β-TAs with this coupled-enzyme assay, the assay was optimized for the slowest β-TA, Mes-TA, using a quartz cuvette (Hellma) and spectrophotometric (Jasco V-660) monitoring of NADH formation at 340 nm. The optimized variables were pyruvate, GluDH, and β-TA concentrations to give these final reaction conditions for a 1 mL reaction volume: NAD+ (2 mM), PLP (0.05 mM), GlDH (15 U·mL-1), α-KG (0.1 mM), and sufficient TA. These optimized conditions were carried through to a microtiter plate (Greiner) format (MTP) using a 96-well platereader (BioTek SynergyMx) to screen β-TA activity at three different substrate concentrations (usually 2, 8, 16 mM). For the MTP reaction (300 μL), an ~8x concentrated master mix was prepared containing NAD+, PLP, GluDH, and β-TA. Substrate and KPi buffer (potassium phosphate, pH 8) were added to the relevant wells followed by the master mix aliquot. For each β-TA in every experiment, control reactions containing half and double the concentrations of β-TA used in the experimental reactions were performed to ensure


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that the β-TA activity is limiting in the coupled-enzyme assay and that valid measurements of β-TA activity are being observed. The plate was pre-heated for 10 min at 30°C, (Eppendorf Thermomixer comfort) with a pre-fit MTP plate holder. The co-substrate α-KG (preheated to 30°C, 0.2 mM stock, 150 μL) was delivered via dispenser 2 (275 μL·s-1) in each well by the plate reader machine to ensure adequate mixing before measurements began. Upon addition of α-KG, the plate was shaken for 5 min before A340 measurements were taken every 10 s for 1h. The pathlength correction was set to 977/900 and measured at the end of the protocol. Read speed was set to ‘Sweep,’ 1 measurement/data point, and no delay between measurements. The data processing protocol calculated slope values (mOD·s-1) in 1 min increments with 0.5 min overlaps with successive slope calculations up to 7 min of reaction time, followed by 2 min slope calculations for the reaction period of 7-11 min, and then the final two slope calculations (11-15 and 15-20 min reaction periods). Aberrant slopes were filtered to exclude those with fits having R2-values <0.8. To validate measurement of β-TA activity in this indirect assay, a linear correlation (R2=0.988) between Mes-TA concentration and the slope from absorbance curves was observed (Figure 12). Optimal α-KG concentration was determined to be 0.1 mM with reference substrate (S)-B11. Also, >100-fold excess of DH relative to TA was used in all reactions, which ensures validity of measuring target enzyme activity in a coupled assay according to prior studies [61].

Figure 12 | Optimization and validation of coupled enzyme assay with Mes-TA. (a) Optimization of α-ketoglutarate (α-KG) concentration in the coupled-enzyme assay with glutamate dehydrogenase. (b) Assessment of TA concentration effects on slopes derived from spectrophotometric detection at 340 nm of

NADH formation.

3.6 | Activity measurements and kinetic analyses | Amine Transaminases The amine transamination activity was measured indirectly via a coupled-enzyme assay (Figure 2) using alanine dehydrogenase (ADH). The ADH activity was measured by monitoring NADH formation at 340 nm. After an assay optimization was performed in a multi-cell spectrophotometer (Jasco V-660), component concentrations were used in substrate screening assays as follows: ADH (5 U/mL), NAD+ (2 mM), PLP (50 μM), amino substrate (2, 8, or 16 mM), pyruvate (0.2 mM), PdTA (5.6 mg/mL), VfTA (3.3 mg/mL), and CvTA (7.9 mg/mL). Final enzyme concentrations of the


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respective TA were chosen within the linear range of the plot, reaction rate vs. enzyme concentration, to ensure that the TA activity was being measured. Activity measurements for substrate screenings and kinetic analyses were performed in a 96-well plate (Greiner) format using a Synergy Mx (SMx) Monochromator-Based Multi-Mode Microplate Reader (BioTEK). For each substrate screening reaction, all components except pyruvate were added (150 μL volume) and incubated in a plate thermomixer (27°C, 10 min), after which the plate was uncovered and placed in the SMx plate reader (27°C). The reaction was initiated with addition of pyruvate (150 μL) via syringe delivery (275 μL/sec) to give a final reaction volume of 300 μL in 50 mM potassium phosphate (KPi) buffer (pH 8). The plate was then shaken for 5 sec. Absorbance reads were recorded every 10 sec for 1 h. Pathlength corrections were taken at the end of each plate read using 900 nm and 977 nm absorbance reads. Slopes were calculated by SMx software (Gen5, v1.10, BioTEK) by calculating the tangents over pre-set time-frames (0-1, 0.5-1.5, 1-2, 1.5-2.5, 2-3, 2.5-3.5, 3-4, 3.5-4.5, 4-5, 4.5-6, 5-7, 7-9, 9-11, 11-15, and 15-20 min). A maximum of 36-40 wells were used in order to avoid long lag times between measurements. To calculate the reaction rates, an average of the slopes within the linear range of the plot, absorbance vs. time, were used. Slopes with R2-values <0.9 were filtered out. All substrate solutions were prepared in 50 mM KPi buffer (pH 8) with adjustments to pH 8 using phosphoric acid as needed.

3.7 | Kinetic measurements For kinetic analyses, the same reaction setup in the activity measurements was used, except for the following: all substrate dilutions were prepared to allow the same volume of substrate in every reaction, and the absorbance reads were taken every 4 s for 20 min. Following measuring reaction rates, the kinetic constants were determined after fitting the data to Michaelis-Menten parameters using GraphPad Prism 6.

3.8 | Phylogenic tree The phylogenetic tree, adapted from Mathew et al. [19], comprises sequences of functionally validated fold-type I, class II TAs. The sequences (Table S1) used by Mathew et al. were retrieved from the NCBI database and a multiple sequence alignment (MSA) was performed using the MUSCLE alignment within the Geneious software (www.geneious.com), which was used create the tree using the Geneious Tree Builder (neighbor joining method, bootstrapped with 1,000 replicates at a 75% support threshold).

3.9 | Structural analyses Images based on aligned structures focused on PLP in Chimera using ‘MatchMaker’ tool with default settings [62]. Volume surfaces generated from CASTp server [47].

Acknowledgements

This work was supported by EU-FP 7 project funding from Micro B3, Grant 287589.


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Supplementary Information

Table S1 | Entries in phylogenetic tree of Fold-type I, class II TAs

Tree Entry Acc. ID Organism

Clade 1: (S)-β-TAs

Low activity

ST WP_012871332.1 Sphaerobacter thermophilus

Mes-TA A3EYF7 Mesorhizobium sp. LUK

PO ABE43415.1 Polaramonas sp.

BG ZP_02885261 Burkholderia graminis

High activity

VarM7V-TA this study Variovorax sp. str. M7V

Vp_TA 4AO9 Variovorax paradoxus str. CBF3

VpEPS-TA WP_013541041.1 Variovorax paradoxus str. EPS

VpS110-TA WP_012747511 Variovorax paradoxus str. S110

Clade 2: (S)-ω-TAs

BV YP_001110355.1 Burkholderia vietnamiensis

OA YP_001368759.1 Ochrobactrum anthropi

AD AAP92672) Alcaligenes denitrificans

CC AAK25105 Caulobacter crescentus

BM 5G0A Bacillus megaterium

VfATA F2XBU9 Vibrio fluvialis str. JS17

PdATA ABL72050.1 Paracoccus denitrificans

CvATA NP_901695 Chromobacterium violaceum

SP_High 3HMU Silicibacter pomeroyi

ML_Low 3GJU Mesorhizobium loti

Out-group: (R)-ATA

ATA-117 BAK39753 Arthrobacter


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6

Table S2 | β-TA activity comparisons with literature

Type Entry Substrate Mes

S.A. Prior

[12]

Vp

S.A. Prior

[18]

Amine Am1 (S)-α-MBA

β-aa’s

aromatic B11 (S)-β-Phe 1.80 1.60 6.30 17.50

B14 (S)-β-Tyr 3.20 13.40 10.68

aliphatic B1 (S)-β-Leu 2.10 4.40 6.65

B2 (R)-3-amino-5-methylhexanoic acid 7.00 3.00 4.90 8.40

B6 β-Ala 0.00 0.00 0.00

B7 3-aminobutyric acid 7.80 3.30 2.40 0.18

+ = only relative activity reported with no reference S.A. provided

MBA, methylbenzylamine


178 | Chapter 6

Table S3 | ATA activity comparisons with literature

Type

Amines

Entry Substrate Pd S.A. prior ref.

Cv S.A. prior ref.

Vf S.A. prior ref.

aromatic Am1 (S)-α-MBA 0.58 + [9] 0.47 [17] 0.53 + [17]

+ [34] [29] + [27]

+ [35] [33] + [29]

+ [36] + [32]

+ [63]

Am2 Benzylamine 0.30 + [35] 0.39 [29] 0.22 + [29]

Am4 Phenethylamine 0.22 0.10 0.27 + [27]

+ [32]

Am5 4-phenylbutylamine 0.06 0.13 0.05 + [27]

+ [32]

aliphatic Am7 Isopropylamine 0.04 + [35] 0.02 0 [29] 0.06 0 [29]

Am10 Amylamine 0.11 0.12 0.14 + [27]

α-aa’s

aliphatic A1 L-norvaline 0.36 + [9] 0.39 0.36

A2 L-norleucine 0.32 + [9] 0.53 0.20

polar A3 L-Asp 0 0 0 [29] 0 0 [27]

0 [29]

A4 L-Glu 0 0 0 0 [27]

A7 L-Ser 0.01 + [29] 0 + [29]

aromatic A10 D-2-phenylglycine 0 0 0 0 [27]

β-aa's

aromatic B11 (S)-β-Phe 0 [27]

aliphatic B6 β-Ala 0 0 0 [29] 0 0 [29]

B7 3-aminobutyric acid + [29] + [29]

ω-aa's

B9 4-aminobutyric acid 0.23 0.33 + [29] 0.17 0 [29]

B10 6-aminocaproic acid 0.07 0.36 + [29] 0.06 0 [29]

+ = only relative activity reported with no reference S.A. provided

Substrates: MBA, methylbenzylamine; 2,6-DAPA, 2,6-diaminopimelic acid; 3-A-3-CPPA, 3-amino-3-cyclopentylpropanoic acid


179Transaminases |

6Figure S1 | Activity profiles of ATAs investigated in this study. Substrates are (a) amines and (b) amino acids.

All specific activities (S.A.) based on single measurements.


180 | Chapter 6

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181Transaminases |

6

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182 | Chapter 6

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183Transaminases |

6

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