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Predicting the Functions and Specificity of Triterpenoid Synthases: A Mechanism-Based Multi-intermediate Docking Approach

  • Bo-Xue Tian,

    Affiliations Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, San Francisco, California, United States of America, California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, California, United States of America

  • Frank H. Wallrapp,

    Affiliations Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, San Francisco, California, United States of America, California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, California, United States of America

  • Gemma L. Holiday,

    Affiliations California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, California, United States of America, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, United States of America

  • Jeng-Yeong Chow,

    Affiliation Department of Chemistry, University of Utah, Salt Lake City, Utah, United States of America

  • Patricia C. Babbitt,

    Affiliations California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, California, United States of America, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, United States of America

  • C. Dale Poulter,

    Affiliation Department of Chemistry, University of Utah, Salt Lake City, Utah, United States of America

  • Matthew P. Jacobson

    matt.jacobson@ucsf.edu

    Affiliations Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco, San Francisco, California, United States of America, California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, California, United States of America

Predicting the Functions and Specificity of Triterpenoid Synthases: A Mechanism-Based Multi-intermediate Docking Approach

  • Bo-Xue Tian, 
  • Frank H. Wallrapp, 
  • Gemma L. Holiday, 
  • Jeng-Yeong Chow, 
  • Patricia C. Babbitt, 
  • C. Dale Poulter, 
  • Matthew P. Jacobson
PLOS
x

Abstract

Terpenoid synthases construct the carbon skeletons of tens of thousands of natural products. To predict functions and specificity of triterpenoid synthases, a mechanism-based, multi-intermediate docking approach is proposed. In addition to enzyme function prediction, other potential applications of the current approach, such as enzyme mechanistic studies and enzyme redesign by mutagenesis, are discussed.

Author Summary

The rapid growth in the number of protein sequences presents challenges for enzyme function assignment. Computational methods, such as bioinformatics, homology modeling and docking, are becoming increasingly important for predicting of enzyme functions from protein sequences. Terpenoids are one of largest classes of natural products, and many drugs (e.g. taxol) consist of terpenoids or terpenoid derivatives. Understanding the biosynthesis of the terpenoids is of great interest. Terpenoid synthases catalyze the key cyclization steps of the biosynthesis of terpenoids via carbocation rearrangements, generating numerous multiple-ring carbon skeletons. Triterpenoid synthases, as an important class of terpenoid synthases, catalyze the cyclization of either squalene or oxido-squalene into cyclized products such as sterols (e.g. lanosterol). In this work, we propose a computational approach that can be used to predict product specificity of the triterpenoid synthases. Our approach provides insight into the ‘design principles’ of these fascinating enzymes, and may become a practical approach for function prediction and enzyme engineering.

Introduction

The terpenoids, also called isoprenoids, are one of the largest and most structurally diverse classes of natural products, and play vital roles in almost all life forms [1], [2]. In the biosynthesis of terpenoids, the isoprene units (C5) are assembled by polyprenyl transferases to give long chain terpenes such as geranyl diphosphate, farnesyl diphosphate, geranylgeranyl diphosphate, and squalene, which can then be converted into diverse carbon skeletons by the terpenoid synthases (TPSs) [3], [4]. Understanding the specificity of TPSs is of great significance to biochemistry, organic chemistry and medicinal chemistry.

According to the number of isoprene units (C5) of the substrates, TPSs can be classified into hemiterpenoid (C5), monoterpenoid (C10), sesquiterpenoid (C15), diterpenoid (C20), sesterterpenoid (C25), triterpenoid (C30) and sesquarterpenoid (C35) synthases. Most TPSs have one of two distinct protein folds [5][7], an α fold (class-I) and a βγ fold (class-II). For “class I” enzymes, the reaction is initiated by Mg2+-assisted removal of the diphosphate group, e.g., in limonene synthase [8] (Figure 1a and Figure 2a), while for “class II” enzymes, an acidic residue (normally Asp) initiates protonation of a double bond or an epoxy oxygen, e.g., in squalene-hopene cyclase [9], [10] (Figure 1b and Figure 2b). Both reaction types produce carbocation-olefin intermediates that undergo diverse cyclizations (rearrangements), followed by quenching of the carbocations via deprotonation or hydroxylation [5], [11], [12]. Some diterpenoid synthases that have the αβγ fusion fold can sequentially use both class I and II active sites to catalyze even more complicated reactions, e.g., the abietadiene synthase [13].

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Figure 1. Example structures of TPSs: a) limonene synthase (PDB: 2ONH) [8]; b) squalene-hopene cyclase (PDB: 1SQC) [9], [10].

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Figure 2. Example reactions of TPSs: a) limonene synthase; b) squalene-hopene cyclase.

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Some TPSs are promiscuous, e.g. the baruol synthase from Arabidopsis thaliana converts oxido-squalene into baruol (90%) as well as 22 other minor products [14]. Other TPSs are highly specific, e.g. the human lanosterol synthase generates only lanosterol, which has 7 chiral carbons [15]. Sometimes, even a single mutation in the TPSs can completely alter their product specificity, e.g. the H234S and H234T mutants of the lanosterol synthase from Saccharomyces cerevisiae produce 100% protosta-12,24-dien-3β-ol and 100% parkeol, respectively [16].

Crystal structures of TPSs [5], [6], [8][10], [13], [15], [17][30] provide a basis for understanding reaction mechanisms and specificity. As carbocations are short-lived, trapping the enzyme-bound intermediates is experimentally difficult. Therefore, high level quantum mechanics (QM) [31][35] and quantum mechanics/molecular mechanics (QM/MM) [36][40] calculations have been performed in order to understand the mechanisms of TPSs. Some in silico predicted catalytic mechanisms have been confirmed by experiments, e.g. a recent kinetic isotope effect (KIE) study on the mechanism of pentalenene synthase confirmed the QM-derived mechanism [41]. Hong et al. studied the catalytic mechanisms of a series of mono-, sesqui- and di-terpenoid synthases using QM methods, which have been summarized in a review article [32]. Based on QM/MM calculations, Rajamani et al. proposed that the product specificity of squalene-hopene cyclase is achieved by balancing thermodynamics and kinetic properties [39].

The aim of this and predecessor studies [42][53] is the development of robust methods for enzyme function prediction, using available sequence and structural information. In a recent work [50] involving a combination of bioinformatics, docking, homology modeling and enzymology, we have successfully predicted and experimentally validated the functions of 79 diverse members of the trans-polyprenyl transferase subgroup, which produces substrates for TPSs. Our long-term goal is essentially the same for the TPSs, i.e. building models to predict function of unknown enzymes [43]. However, due to the diversity of possible products, the TPSs present a more difficult problem than the polyprenyl transferases.

Both the polyprenyltransferases and the TPSs create challenges for purely sequence-based function prediction, because small sequence changes (including single point mutations) may result in a different product profile [16]. We thus believe, and have demonstrated for the polyprenyltransferases, that structure-based modeling approaches can provide important information about function. In the case of the polyprenyltransferases, product specificity is determined, to a large extent, by the depth of the cavity in which the growing polyisoprenoid chain binds. The situation for TPSs is considerably more complicated, in that the size and shape of the binding site, as well as the ability to differentially stabilize multiple carbocationic intermediates (and the transition states connecting them) all contribute to product specificity [54].

In principle, QM/MM methods [55][62] are ideal for studying these complex sequence-structure-function relationships, as has been demonstrated in focused studies of the mechanisms of certain TPS enzymes [37][40]. However, these methods are computationally too expensive to be used in large-scale function prediction of uncharacterized enzymes. Even for a single TPS, studying all known reaction channels by QM/MM is time consuming (to our knowledge, no such study has yet been reported). We hypothesize that molecular-mechanics-based “docking” methods, although they have a number of well-documented limitations, can nonetheless provide useful guidance concerning product specificity of TPS enzymes, with a throughput that is suitable for prospective investigations of large numbers of enzymes, as we have demonstrated for other classes of enzymes. The goal of our approach is not to eliminate experimental studies, which will be needed (for the foreseeable future) to test predictions, but rather to guide and focus the experimental studies. For TPS enzymes, long-term goals include the prediction of when/how changes in the binding sites impact specificity, and identification of TPS enzymes that may have novel activity (or conversely, guide the design of such enzymes).

We now describe a mechanism-based carbocation docking approach to predict function, and use the triterpenoid synthases [12], [63][66] (a subgroup of the class II TPS, proton initiated) to illustrate this approach. Triterpenoid synthases, which are found in a wide variety species including bacteria, archaea, plants, fungi, and animals, are involved in the biosynthesis of multicyclic metabolites such as sterols and saponins [64]. In this work, we dock against crystal structures and homology models for a wide variety of experimentally characterized triterpenoid synthases, in order to test the mechanism-based carbocation docking approach. Previous enzyme function prediction studies using intermediate docking [42], [44], [67], [68] have been conceptually simpler in that a single intermediate maps to one or a small number of possible substrates and products. In the case of TPSs, the number of possible substrates is small, but the number of potential products is enormous, and the generation of most products involves multiple carbocationic intermediates. Thus, instead of docking a single intermediate per reaction, we dock multiple intermediates along diverse reaction channels, in order to capture the mechanistic diversity (reaction channels) and product diversity of TPSs.

Results

Protein sequence similarity network of triterpenoid synthases

Triterpenoid synthases (also called triterpene cyclases) catalyze the cyclization of squalene or oxido-squalene into hundreds of natural products [63], most of which are tetra- or pentacyclic structures such as lanosterol [15] and hopene [9], [10]. Triterpenoid synthases utilize one of three distinct reaction channels (Figure 3) [54]: 1) the hopene channel (Channel A); 2) the lupeol channel (Channel B); and the lanosterol channel (Channel C). In this work, we used the two known crystal structures for triterpenoid synthases, squalene-hopene cyclase from Alicyclobacillus acidocaldarius (PDB: 1SQC) [9], [10] and human lanosterol synthase (PDB: 1W6K) [15], for docking and building homology models, both of which are wild-type and have ligand bound (inhibitor for 1SQC and product for 1W6K).

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Figure 3. Reaction channels for triterpenoid synthase and triterpenoid synthase-like enzymes [54], [71].

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Figure 4 and Figure S1 show protein sequence similarity networks summarizing the known functions of the triterpenoid synthases, a bioinformatics tool that we have used extensively in the context of enzyme function prediction (for details of network generation, see Methods). Enzyme functions can be defined by the Enzyme Commission (EC) numbers, which describe the overall reaction being performed by an enzyme. The EC number consists of four levels, where the first three levels broadly describe the types of reaction being performed, and the fourth level generally describes the substrate specificity of the enzyme's overall chemical transformation. EC numbers and other related chemical information (e.g., reaction channels) can be mapped onto the sequence similarity networks (Figure 4 and Figure S1). To study enzyme functions with sequence similarity networks, different BLAST E-values [69] are scanned to gradually break the sequence similarity networks into smaller clusters until known enzyme functions are well segregated.

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Figure 4. Sequence similarity network of triterpenoid synthase and triterpenoid synthase-like proteins colored by reaction channels.

Each node represents a protein sequence, and nodes are connected when the Blast E-value for the pair of sequences is more significant than 10−60 (panel a) or 10−220/10−300 (panel b). Gray nodes represent enzymes lacking annotations in the manually curated portion of UniProtKB (Swiss-Prot), i.e., likely to be experimentally uncharacterized.

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At an E-value of 1E−60 (an average sequence identity of 40%; obtained from the quartile plot see Figure S2), the sequences are separated into two major clusters, each of which contains the structure of one enzyme; for this reason, we label them as the 1SQC cluster and the 1W6K cluster (Figure 4a and Figure S1a). As the products of triterpenoid synthases are diverse, it is difficult to identify trends if we color the nodes according to EC numbers (Figure S1a). Even at an E-value of 1E−220 or 1E−300 (the average sequence identities are 50% and 70%, respectively; Figure S2), enzymes with different EC numbers still do not segregate well (Figure S1b), implying that it will be challenging to precisely predict function (full EC number) based on sequence alone. It is worth noting that the EC number generally only describes a single overall chemical transformation, thus is not well suited to categorizing promiscuous enzymes, which will catalyze several different EC numbers.

However, the products of triterpenoid synthases group into a few classes based on their carbon skeletons, which are related to the “reaction channels” (i.e. the series of carbocationic intermediates leading to various classes of products). Most of the reaction channels for the experimentally characterized enzymes can be separated at an E-value of 1E−300 in the sequence network (Figure 4), with only a few exceptions in cluster 1 (Figure 4b). Thus, functional relationships that are obscured by EC numbers, based on the exact products, are revealed by focusing instead on the nature of the carbocationic intermediates (and by implication the transition states connecting them) that are, presumably, differentially stabilized by the various classes of enzymes. It should be noted that in Figure 4, besides the three major reaction channels (Channel A–C) mentioned above, we also include a fourth Channel D (cyan; Figure 3), representing a recently discovered sesquarterpenoid (C35) synthase [70], [71]. As the crystal structure for this enzyme is not available and the sequence identity between this enzyme and 1SQC is low (∼25%), we cannot create a high quality model for this enzyme. In addition, the C35 intermediates corresponding to Channel D are predicted to bind poorly for most of our models (in comparison to the other three channels; Table S1), because the intermediates along Channel D are significantly different from those along Channel A–C in terms of size and shape [70], [71]. Hence, we do not consider Channel D further, and focus only on C30 carbocationic intermediates corresponding to Channels A–C.

Hypotheses for docking

As classical molecular mechanics methods do not correctly describe transition states, docking transition states is impractical. Invoking assumptions similar to those in the “high-energy intermediate” approach of Shoichet and co-workers [67], we dock carbocationic intermediates. The primary difference is that, in this case, there is only one plausible substrate, but multiple possible intermediates that lead to different products. We hypothesized that by docking multiple intermediates (and ranking the results hierarchically), we could predict the dominant reaction channels for triterpenoid synthases, and then predict the likely product/precursor intermediates along the predicted reaction channel (rather than precise structures for the final products). At a minimum, we expected that we could at least exclude some implausible reaction channels, which have intermediates that are poorly stabilized by the enzyme, due to either steric clashes or electrostatic incompatibility. We do not dock every possible carbocation intermediate but only those that help distinguish the different reaction channels and product precursors.

Docking to crystal structures of triterpenoid synthases

We first discuss the docking results for the two crystal structures mentioned above, i.e., 1SQC and 1W6K, as an important test of the methodology. The key difference between the three major reaction channels (Channels A–C) is the stereochemistry of the 6,6-bicyclic and 6,6,6,5-tetracyclic carbocationic intermediates I1 and I2, respectively (Figure 3). It should be noted that A-I1 and B-I1 are chemically identical but are represented by different conformations which can convert to chemically different intermediates A-I2 and B-I2 (Figure 5). The rule of configuration transmission in triterpenoid synthases has been extensively discussed [54]; the key concept is that, with limited rotational freedom in the active site cavity, conformational differences in the upstream intermediates will be transferred to the downstream intermediates. As a practical matter, docking different conformations of the same intermediate (e.g. A-I1 and B-I1) results in different docking scores (see Methods for details), which we interpret in terms of the predicted reaction channel.

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Figure 5. Illustration of the key dihedral angle C16-C17-C18-H18 that determines the conversion of I1 to I2: a) A-I1; b) B-I1.

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In order to take active site flexibility into account, an induced fit docking protocol is used for all docking calculations. Receptor flexibility is important to the current work because rearrangements of the carbocationic intermediates may slightly change the conformations of the active site residues. (In addition, when using homology models, as described below, receptor flexibility can compensate for small errors in the models.) To ensure the ligands are docked into a catalytically-relevant position, constraints were applied during the docking, which are essential for maintaining consistent poses of the carbocationic intermediates along the same reaction channel. Detailed procedures and parameters are provided in Methods.

According to previous QM/MM studies on squalene-hopene cyclase [39] and lanosterol synthase [38], there is only one transition state between I1 and I2, whose reaction barrier is significant (>10 kcal/mol). Therefore, we suggest that the transition state between I1 and I2 is a key specificity determinant for the three reaction channels defined above, and that stabilization of intermediates I1 and I2 can be used to distinguish reaction channels. However, we are aware that for some cases in which the binding affinities of the intermediates along different channels are very similar, this assumption may be insufficient.

Figure 6a shows the docking scores for intermediates along three major reaction channels docked to the squalene-hopene cyclase crystal structure (1SQC). Intermediates along reaction channel A (blue), which leads to the correct product hopene, clearly receive the most favorable docking scores. At present, we are unable to predict the specific products based simply on the docking scores. That is, the product precursor hopanyl cation (A-I4) is only the third best binder, implying that the current docking approach is not able to accurately predict the correct precursor cation of the major product; quantum mechanical methods may be necessary to achieve such a goal. However, as the TPSs are often promiscuous, carbocation docking can at least identify several possible intermediates that could lead to the final products, e.g. the second best binder A-I2 is the precursor of 6,6,6,5-tetracyclic byproducts of squalene-hopene cyclase. In a previous QM/MM study on 1SQC [39], the free energy barriers for the formation of the A-I2 and A-I4 intermediates were determined to be very similar (1.8 kcal/mol difference), but A-I4 is thermodynamically more stable (>10 kcal/mol difference). One possible way to improve the prediction results is to run further QM/MM calculations to evaluate the most likely intermediates from our docking hits, as well as transition states between the intermediates, but this approach is computationally expensive and beyond the scope of the current work.

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Figure 6. Carbocationic intermediate docking scores (MM/GBSA) along the reaction coordinates of a) 1SQC and b) 1W6K.

We arbitrarily assigned a score of +100 kcal/mol to intermediates that could not be successfully docked.

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Figure 6b shows the carbocation intermediate docking results for the lanosterol synthase crystal structure (1W6K). The sequence identity between 1SQC and 1W6K is only 25%, and most of the active site residues are different. In this case, the intermediates I1 and I2 for reaction channel C receive the most favorable docking scores (I1 and I2 in Figure 6b). We also find that the product precursor C-I6 is the best binder among the intermediates along channel C (from C-I1 to C-I9; Figure 6b). Figure 7a shows the docking pose of the product precursor intermediate C-I6 (orange), which is in good agreement with the product lanosterol in the crystal structure (grey; RMSD 0.23 Å). Figure 7b shows the docking poses of the intermediates A-I2, B-I2, and C-I2. The pose of the correct intermediate C-I2 (lime; RMSD 0.42 Å) is more similar to that of lanosterol in the crystal structure (grey) than the poses of A-I2 and B-I2 (RMSD 0.63 Å and 0.86 Å), which differ from the crystal structure in the orientation of the 6,6,6,5-tetracyclic core (Figure 7b). Interestingly, C-I8, which can form the product cycloartenol (EC 5.4.99.8), is a non-binder, suggesting that the reaction will terminate at C-I6 or C-I7, both of which are precursors of lanosterol (C-I7 can also form other products such as parkeol and cycloartenol). These results suggest that the intermediates after C-I8 (e.g. C-I9, which is the product precursor of cucurbitadienol; EC 5.4.99.33) will be unlikely to occur. Hence, the docking results for 1W6K suggest that the carbocation docking approach could make qualitative, but meaningful, predictions concerning the end point of a reaction channel in some favorable cases. That is, the inability of a given binding site to significantly stabilize certain intermediates can, at a minimum, rule out downstream products. We explore this concept further below, using homology models to create a much larger test set.

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Figure 7. a) Superimposed view of the product lanosterol in the 1W6K crystal structure (grey) and the docking pose of C-I6 (the product precursor carbocation, c.f. Figure 6b; in orange); b) The docking poses of the second representative intermediates: A-I2 (blue), B-I2 (red) and C-I2 (lime), as well as lanosterol in the 1W6K crystal structure (grey, c.f. Figure 6b).

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Docking against homology models

We further tested our approach by docking carbocationic intermediates against homology models of 54 triterpenoid synthases with annotations in Swiss-Prot (human-curated annotations). We exclude from consideration one triterpenoid synthase-like enzyme with a reported preference for a C35 substrate, both because it is not a triterpenoid synthase, and because it cannot be modeled reliably (only 25% sequence identity to 1SQC).

Guided by the results from docking carbocationic intermediates against the two available crystal structures, we use the docking scores for intermediates I1 and I2 to predict the reaction channel (see Methods for details). The overall success rate for reaction channel prediction of these sequences is 80% (Table 1). Details for each test case, including sequence alignments and docking scores, can be found in Table S1, S2 and S3. Three of the test cases are close homologs of 1W6K (88% sequence identity), and unsurprisingly, these are correctly predicted to follow Channel C, as does 1W6K. The remaining test cases have sequence identity to either 1SQC or 1W6K ranging between 33–49%, and thus are much more challenging.

All 4 of the test cases in the 1SQC cluster were correctly predicted. Of these, 3 of 4 are squalene-hopene cyclases, i.e., the same function as 1SQC, upon which the homology models are based. However, the remaining case is correctly predicted to follow channel B (dammara-20,24-diene synthase). Note that sequence identity alone does not distinguish these cases; the dammara-20,24-diene synthase actually has slightly higher sequence identity to 1SQC than the hopene synthases.

Fifty of the test cases were in the 1W6K cluster, and thus their homology models were based on this structure (lanosterol synthase, channel C). The products of these enzymes correspond to a mix of channel B (27 cases) and channel C (23 cases). The overall accuracy of channel prediction is 78%; nine of the 11 incorrect predictions are based on homology models with 40% or lower sequence identity to 1W6K.

Reaction channel prediction for 21 out of 23 triterpenoid synthases in the 1W6K cluster that follow Channel C are successful (Table S1d). For these 21 triterpenoid synthases, we further docked the downstream intermediates (Table S2, Figure 8 and Figure 9). The binding energy profiles, on average, follow a characteristic pattern where the docking scores are highly favorable for I1 in all cases, and much less so for I2, followed by gradually more favorable scores, on average, from I3 to I9. It should be kept in mind that these scores do not, at present, take into account the intrinsic (gas phase) relative energies of the carbocations (I2 being more stable than I1, for example). Nonetheless, the profiles for enzymes that generate different products show qualitative differences that correlate well in most cases with the product specificity.

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Figure 9. Docking score (MM/GBSA) of 9 carbocationic intermediates for 22 triterpenoid synthase homology models that follow channel C.

Compounds that could not be successfully docked at all are arbitrarily assigned a docking score of −10 kcal/mol. Figure legend shows the UniProtKB IDs for the triterpenoid synthases. Panel a shows the docking scores against 8 lanosterol synthases (in red); panel b shows the docking scores against 10 cycloartenol synthases (in lime green); and panel c shows the docking scores against a cucurbitadienol synthase (in cyan), a parkeol synthase (in magenta) and 2 protostadienol synthases (in blue). Details c.f. Table S2.

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For the triterpenoid synthases that produce lanosterol, the most favorable docking score (other than for I1) in 6 of 7 cases is either C-I6 or C-I7, both of which are product precursors for lanosterol (Figure 8 and Figure 9a). Moreover, in all cases, one or more of the intermediates subsequent to the intermediate with the most favorable docking score cannot be docked successfully into the binding site. Similarly, for the triterpenoid synthases that produce cycloartenol, 7 out of 10 models predict precursors C-I7 or C-I8 to have the most favorable docking scores (Figure 8 and Figure 9b). However, in 3 cases, C-I9 is predicted to have the most favorable docking score, and in 2 of these cases, there is no energy increase at C-I8. Thus, even in our very simple qualitative interpretation of these results, we consider these cases to be failures. The remaining 4 cases—enzymes that produce cucurbitadienol, parkeol, and protostadienol—are more ambiguous. One of the two protostadienol cases shows a strikingly different profile that is broadly consistent with being unable to proceed beyond C-I2 or C-I3, while the other case does not (Figure 9c). Overall, we conclude that carbocationic intermediate docking against homology models may be useful to make qualitative predictions concerning product specificity, but further improvements to the methodology are likely needed to provide robust predictions.

Beyond enzyme function prediction: Guiding mutagenesis and studying enzyme mechanisms

Beyond enzyme function prediction, the current approach may have two other potential applications: 1) guiding mutagenesis experiments to alter the product specificity of an enzyme; and 2) exploring the catalytic mechanisms of enzymes. Although high-level quantum mechanical calculations are no doubt needed to make quantitative predictions, we illustrate here how the much simpler qualitative predictions from carbocation docking can nonetheless provide useful insights.

Specifically, we examine 3 mutants of 1SQC. The experimental data for these mutants were obtained from an earlier study [72], and our docking results are summarized in Table 2. The Y609C, Y609L and Y609S mutants generate aborted product A-P1 as the major product, and minor amounts of A-P2 and A-P4 (Table 2). The much lower yield of product A-P4 for the Y609X mutants suggests that the reaction channel leading to A-I4 is affected by Y609X mutations. We thus compared the MM/GBSA scores of intermediates of the Y609X mutants to those of wild type. As with all of the docking results, the scores should be interpreted qualitatively. In this case, the scores of A-I1, A-I2 and A-I4 do not vary significantly between wild-type and the mutants, while A-I3 becomes a much weaker binder for all three Y609X mutants. A comparison of the docking poses of A-I3 in the wild-type and the Y609C mutant Figure S6 also suggest that the Y609X mutants affect the binding of A-I3.

We interpret these results as follows (Figure 10). In a previous QM/MM study [39], the barrier height from A-I2 to A-I4 was computed to be 27.8 kcal/mol, while for the A-I3 like transition state that directly links A-I1 and A-I4, the barrier height was only 9.1 kcal/mol. Thus, for wild type, most A-I4 is likely generated through A-I3. In the mutants, binding of A-I3 is greatly destabilized, and we speculate that formation of A-I4 proceeds, much more slowly, through A-I2, and product formation from A-I1 and A-I2 competes with conversion to A-I4. Hence, our mechanistic findings from docking calculations are qualitatively consistent with the QM/MM results that the direct conversion from A-I1 to A-I4 is the major productive channel for 1SQC. The docking results are not accurate enough, however, to make any quantitative predictions concerning product distributions.

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Figure 10. Key intermediates involved in the reaction channel leading to the hopanyl cation (A-I4), and products derived from these.

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We also considered the L607K mutation of 1SQC, which generates gamma-polypodatetraene as the major product, presumably from A-I1. Consistent with this observation, only the A-I1 intermediate could be docked successfully. This appears to result from the strong repulsion between the positive charge on K607 and the carbocation on A-I2, A-I3 and A-I4.

Discussion

Although the results obtained with the current methodology are more qualitative when compared to more rigorous methods such as QM/MM, the major advantage of docking carbocationic intermediates is its computational efficiency, which enables its application to large numbers of protein structures or models (over 50 in this proof-of-concept study). In the foreseeable future, these calculations will not replace experiments in providing reliable assignments of function, but as with other computational prediction methods, they can motivate experiments, or help to interpret the results. As in our prior work on enzyme function prediction, we anticipate that one of the most important uses will be identifying cases that are interesting or unusual, and thus high priorities for time- and resource-intensive in vitro or in vivo experiments (e.g., cyclases predicted to have novel specificity, or cases of convergent evolution).

Docking studies with carbocationic intermediates may also complement more accurate, but computationally intensive, QM/MM methods. For example, in cases where the reaction mechanism is poorly understood, the docking results may suggest plausible pathways that can be further explored by quantum mechanical methods (or perhaps more importantly, reject implausible pathways). Similarly, docking of carbocationic intermediates can be used to evaluate large numbers of possible mutations to identify ones more likely to modify product specificity in a desired manner.

We are aware of limitations of the current approach: 1) our carbocation library currently only considers the naturally occurring reaction channels, which cannot cover the complete chemical space of possible carbocationic rearrangements; 2) as our calculations are based on classical molecular mechanics and docking, the common limitations of MM and docking exist in all our calculations, e.g. the atomic charges are not polarizable (although we have used the QM-derived atomic charges); 3) other limitations such as neglecting the dynamics of the enzymes and the role of waters bound in the active site, which may also affect the final results; 4) the final deprotonation or hydration steps are not modeled. For the first limitation, we are developing an algorithm that can automatically generate all possible reaction channels, which will be published in due course. However, from our preliminary results, such efforts will dramatically increase the computational cost, due to the much larger size of the carbocation library.

Methods

Protein sequence similarity network

The sequence set of triterpenoid synthases were downloaded (October 2013) from Structure-Function Linkage Database [73] through the link http://sfld.rbvi.ucsf.edu/django/subgroup/1016/. The procedure for generating sequence similarity networks for these sequences follows our previous work [50]. Briefly, all pairwise BLAST E-values [69] were computed, and the sequence similarity networks were then generated by using Pythoscape [74]. A “quartile plot” is used to relate the average sequence similarity to the BLAST E-values (Figure S2). Cytoscape [75] is used for the visualization of the sequence similarity networks. In this visual representation, nodes represent sequences, and edges correspond to BLAST E-values that are smaller than a specified cutoff.

Protein structure preparation and homology modeling

Crystal structures of triterpenoid synthases (PDB codes 1SQC [9], [10] and 1W6K [15]) were downloaded from the RCSB Protein Data Bank and processed using Schrödinger Protein Preparation Wizard [76], followed by restrained energy minimizations (RMSD tolerance 0.35 Å, in the presence of the co-crystallized ligand). All crystal water molecules were removed after the minimizations. Homology modeling procedures are similar to our previous work on the polyprenyl transferases [50]. Query sequences were aligned to the templates (1SQC or 1W6K, depending on sequence similarity) using PROMALS3D [77], and models were created by Schrödinger Prime [76], [78], [79]. In brief, the homology modeling procedure closes chain breaks associated with gaps in the sequence alignment by iterative application of the PLOP loop prediction algorithm, followed by side chain optimization (for all residues that are not identical between target and template in the sequence alignment), and complete energy minimization on all portions of the protein whose coordinates were either not taken from the template at all, or were modified during the model building procedure. All the homology models are then processed by using constrained minimizations (RMSD tolerance 0.35 Å, in the presence of the co-crystallized ligands) with Schrödinger Protein Preparation Wizard. The quality of the homology models is assessed by using the discrete optimized protein energy score (a statistical potential score for evaluating protein models) in MODELLER (Table S4) [80]. The OPLS 2005 force field [81], [82] was used throughout this study.

Intermediate docking

The carbocationic intermediates were manually created and atomic charges were assigned using Jaguar [76], [83] quantum mechanical calculations (HF/6-31G*; geometry optimization in gas phase; electrostatic potential fitting). The carbocation library used in the current work is online available through the link www.jacobsonlab.org/carbocation/triterpene_docking_ligands.tar.gz (in ‘mol2’ format). The Schrödinger induced fit docking (IFD) protocol [84], [85] is used for all the docking calculations, with small modifications of default procedures and parameters. The IFD protocol consists of three stages: 1) Schrödinger Glide docking [86][89] with a reduced van der Waals scaling factor (0.5 for both receptor and ligand; top 5 poses are retained for the following steps); 2) minimization of the ligand as well as a conserved set of active site residues within 5 Å of the ligands defined by crystal structures (using the ‘RESIDUES_TO_ADD’ option of IFD; Table 3); 3) computation of MM/GBSA [78], [79] docking scores. To ensure the ligands are docked into the correct position, we applied constraints and core restraints during the initial Glide docking stage, which are essential for maintaining consistent poses of the carbocationic intermediates along the same reaction channel. For example, in the 1W6K crystal structure, we add a hydrogen bond constraint between the ligand and the key aspartate that protonates the oxido-squalene (D455 for 1W6K; c.f. Figure 11). In addition, we use a Glide core restraint (Figure 11 in red, 13 atoms, defined by ‘SMARTS’ pattern, i.e. “[#1][C-0X4]([#1])([#1])[C-0X4]([C-0X4]([#1])([#1])[#1])[C-0X4]([#1])([C-0X4]([#1])[#1])[O-0X2]”; 1.0 Å RMSD tolerance) to ensure that all the docked poses have the same orientation as the lanosterol ligand in the crystal structure (Figure 11). We also changed the Coulomb and van der Waals cutoff parameter during initial docking to a large positive number (‘CV_CUTOFF’ = 999999999.9 vs default 0.0), to retain more poses for the next stage. Both the IFD and MM/GBSA steps use ligand partial charges derived from quantum mechanics, as described above, for all energy calculations and minimizations. MM/GBSA, which is a force field-based scoring function (as opposed to empirical/knowledge-based scoring functions commonly used in docking), is used to accommodate the unusual carbocations studied in this work. That is, empirical or knowledge-based scoring functions will not have been trained on carbocation intermediates.

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Figure 11. Example of constraints and restraints used during docking (residue numbering is for 1W6K).

http://dx.doi.org/10.1371/journal.pcbi.1003874.g011

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Table 3. Active site side chains minimized during the induced fit docking.a

http://dx.doi.org/10.1371/journal.pcbi.1003874.t003

To ensure maximal consistence between the binding modes of I1 and I2, we first dock I2, and then copy the coordinates of I2 to I1, followed by energy minimization. We then check the key dihedral angle Φ[C16-C17-C18-H18] (shown in Figure 5) of all the poses to ensure that the dihedral angles are consistent with those before energy minimization (Φ[C16-C17-C18-H18]>0 for A-I1, and Φ[C16-C17-C18-H18]<0 for B-I1 and C-I1).

Hierarchical ranking

A hierarchical ranking strategy is used to rank different reaction channels and carbocationic intermediates (Figure 12). Figure 12 shows a hypothetical relative binding affinity (MM/GBSA score) profile obtained from carbocation docking along three different reaction channels. In Figure 12, the x-axis is a reaction coordinate (e.g. the conversion SubstrateA→A1→A2→A3→ProductA in Channel A), and the y-axis is the docking score. A1, B1, C1, A2, B2 and C2 are the first and second representative intermediates of reaction channels A, B and C, respectively. In this hypothetical example, the binding affinities of A1 and B1 are similar (<1 kcal/mol), and both are higher than that of C1; thus, the channel ranking in the first round is A = B>C. As for second representative intermediates, the docking score of A2 is more favorable than that of B2, and thus the final channel ranking is A>B>C. After the second representative intermediates, we are able to select the best reaction channel. All the intermediates along the best channel are then ranked by MM/GBSA (without considering further branching points).

Supporting Information

Figure S1.

Protein sequence similarity networks colored by EC number. Each node represents a protein sequence, and nodes are connected when the Blast E-value between the sequences is more significant than 10−60 (panel a) or 10−220/10−300 (panel b). Enzymes lacking SwissProt annotations are colored grey. Note that certain enzymes producing multiple products have been annotated by multiple EC numbers.

doi:10.1371/journal.pcbi.1003874.s001

(TIF)

Figure S2.

Quartile plots resulting from the all-by-all Blast of sequences in the triterpenoid synthase subgroup (in SFLD, it is called ‘Prenyltransferase Like 2’ subgroup, under the ‘IS-II superfamily’; available at http://sfld.rbvi.ucsf.edu/django/subgroup/1016/). Panel a shows the alignment length for different E values; Panel b shows the sequence identity for different E values; and Panel c shows the number of edges for different E values. More information about quartile plots can be found at http://efi.igb.illinois.edu/efi-est/tutorial_analysis.php

doi:10.1371/journal.pcbi.1003874.s002

(TIF)

Figure S3.

A comparison of the docking poses of A-I3 in the wild-type squalene-hopene cyclase (in blue) and its Y609C mutant (in red).

doi:10.1371/journal.pcbi.1003874.s003

(TIF)

Figure S4.

Chemical structures of the carbocationic intermediates of Channel B.

doi:10.1371/journal.pcbi.1003874.s004

(TIF)

Table S1.

MM/GBSA docking scores of I1 and I2 intermediates docked to crystal structures and homology models.

doi:10.1371/journal.pcbi.1003874.s005

(DOCX)

Table S2.

MM/GBSA docking scores of intermediates in channel C.

doi:10.1371/journal.pcbi.1003874.s006

(DOCX)

Table S3.

Sequence alignments used to generate homology models.

doi:10.1371/journal.pcbi.1003874.s007

(DOCX)

Table S4.

Quality assessment of homology models by using discrete optimized protein energy (DOPE) score.

doi:10.1371/journal.pcbi.1003874.s008

(DOCX)

Table S5.

RMSD for the active site residues of crystal structures and those in the IFD.

doi:10.1371/journal.pcbi.1003874.s009

(DOCX)

Author Contributions

Conceived and designed the experiments: BXT CDP MPJ. Performed the experiments: BXT FHW. Analyzed the data: BXT FHW. Contributed reagents/materials/analysis tools: BXT FHW GLH PCB CDP MPJ. Wrote the paper: BXT FHW GLH JYC PCB CDP MPJ.

References

  1. 1. Birch AJ (1957) The Chemistry of Terpenoid Compounds. Nature 180: 470–471.
  2. 2. Sacchettini JC, Poulter CD (1997) Biochemistry - Creating isoprenoid diversity. Science 277: 1788–1789.
  3. 3. Christianson DW (2008) Unearthing the roots of the terpenome. Curr Opin Chem Biol 12: 141–150.
  4. 4. Bohlmann J, Meyer-Gauen G, Croteau R (1998) Plant terpenoid synthases: molecular biology and phylogenetic analysis. Proc Natl Acad Sci U S A 95: 4126–4133.
  5. 5. Christianson DW (2006) Structural biology and chemistry of the terpenoid cyclases. Chem Rev 106: 3412–3142.
  6. 6. Gao Y, Honzatko RB, Peters RJ (2012) Terpenoid synthase structures: a so far incomplete view of complex catalysis. Nat Prod Rep 29: 1153–1175.
  7. 7. Oldfield E, Lin FY (2012) Terpene biosynthesis: modularity rules. Angew Chem Int Ed Engl 51: 1124–1137.
  8. 8. Hyatt DC, Youn B, Zhao Y, Santhamma B, Coates RM, et al. (2007) Structure of limonene synthase, a simple model for terpenoid cyclase catalysis. Proc Natl Acad Sci U S A 104: 5360–5365.
  9. 9. Wendt KU, Lenhart A, Schulz GE (1999) The structure of the membrane protein squalene-hopene cyclase at 2.0 A resolution. J Mol Biol 286: 175–187.
  10. 10. Wendt KU, Poralla K, Schulz GE (1997) Structure and function of a squalene cyclase. Science 277: 1811–1815.
  11. 11. Miller DJ, Allemann RK (2012) Sesquiterpene synthases: passive catalysts or active players? Nat Prod Rep 29: 60–71.
  12. 12. Wendt KU, Schulz GE, Corey EJ, Liu DR (2000) Enzyme Mechanisms for Polycyclic Triterpene Formation. Angew Chem Int Ed Engl 39: 2812–2833.
  13. 13. Zhou K, Gao Y, Hoy JA, Mann FM, Honzatko RB, et al. (2012) Insights into diterpene cyclization from structure of bifunctional abietadiene synthase from Abies grandis. J Biol Chem 287: 6840–6850.
  14. 14. Lodeiro S, Xiong Q, Wilson WK, Kolesnikova MD, Onak CS, et al. (2007) An oxidosqualene cyclase makes numerous products by diverse mechanisms: a challenge to prevailing concepts of triterpene biosynthesis. J Am Chem Soc 129: 11213–11222.
  15. 15. Thoma R, Schulz-Gasch T, D'Arcy B, Benz J, Aebi J, et al. (2004) Insight into steroid scaffold formation from the structure of human oxidosqualene cyclase. Nature 432: 118–122.
  16. 16. Wu TK, Liu YT, Chang CH, Yu MT, Wang HJ (2006) Site-saturated mutagenesis of histidine 234 of Saccharomyces cerevisiae oxidosqualene-lanosterol cyclase demonstrates dual functions in cyclization and rearrangement reactions. J Am Chem Soc 128: 6414–6419.
  17. 17. Lesburg CA, Zhai G, Cane DE, Christianson DW (1997) Crystal structure of pentalenene synthase: mechanistic insights on terpenoid cyclization reactions in biology. Science 277: 1820–1824.
  18. 18. Starks CM, Back K, Chappell J, Noel JP (1997) Structural basis for cyclic terpene biosynthesis by tobacco 5-epi-aristolochene synthase. Science 277: 1815–1820.
  19. 19. Whittington DA, Wise ML, Urbansky M, Coates RM, Croteau RB, et al. (2002) Bornyl diphosphate synthase: structure and strategy for carbocation manipulation by a terpenoid cyclase. Proc Natl Acad Sci U S A 99: 15375–15380.
  20. 20. Vedula LS, Cane DE, Christianson DW (2005) Role of arginine-304 in the diphosphate-triggered active site closure mechanism of trichodiene synthase. Biochemistry 44: 12719–12727.
  21. 21. Kampranis SC, Ioannidis D, Purvis A, Mahrez W, Ninga E, et al. (2007) Rational conversion of substrate and product specificity in a Salvia monoterpene synthase: structural insights into the evolution of terpene synthase function. Plant Cell 19: 1994–2005.
  22. 22. Shishova EY, Yu F, Miller DJ, Faraldos JA, Zhao Y, et al. (2008) X-ray crystallographic studies of substrate binding to aristolochene synthase suggest a metal ion binding sequence for catalysis. J Biol Chem 283: 15431–15439.
  23. 23. Gennadios HA, Gonzalez V, Di Costanzo L, Li A, Yu F, et al. (2009) Crystal structure of (+)-delta-cadinene synthase from Gossypium arboreum and evolutionary divergence of metal binding motifs for catalysis. Biochemistry 48: 6175–6183.
  24. 24. Aaron JA, Lin X, Cane DE, Christianson DW (2010) Structure of epi-isozizaene synthase from Streptomyces coelicolor A3(2), a platform for new terpenoid cyclization templates. Biochemistry 49: 1787–1797.
  25. 25. Noel JP, Dellas N, Faraldos JA, Zhao M, Hess BA Jr, et al. (2010) Structural elucidation of cisoid and transoid cyclization pathways of a sesquiterpene synthase using 2-fluorofarnesyl diphosphates. ACS Chem Biol 5: 377–392.
  26. 26. Koksal M, Jin YH, Coates RM, Croteau R, Christianson DW (2011) Taxadiene synthase structure and evolution of modular architecture in terpene biosynthesis. Nature 469: 116–U138.
  27. 27. McAndrew RP, Peralta-Yahya PP, DeGiovanni A, Pereira JH, Hadi MZ, et al. (2011) Structure of a three-domain sesquiterpene synthase: a prospective target for advanced biofuels production. Structure 19: 1876–1884.
  28. 28. Chen M, Al-lami N, Janvier M, D'Antonio EL, Faraldos JA, et al. (2013) Mechanistic insights from the binding of substrate and carbocation intermediate analogues to aristolochene synthase. Biochemistry 52: 5441–5453.
  29. 29. Baer P, Rabe P, Citron CA, de Oliveira Mann CC, Kaufmann N, et al. (2014) Hedycaryol synthase in complex with nerolidol reveals terpene cyclase mechanism. Chembiochem 15: 213–216.
  30. 30. Li R, Chou WK, Himmelberger JA, Litwin KM, Harris GG, et al. (2014) Reprogramming the chemodiversity of terpenoid cyclization by remolding the active site contour of epi-isozizaene synthase. Biochemistry 53: 1155–1168.
  31. 31. Tantillo DJ (2010) The carbocation continuum in terpene biosynthesis–where are the secondary cations? Chem Soc Rev 39: 2847–2854.
  32. 32. Tantillo DJ (2011) Biosynthesis via carbocations: theoretical studies on terpene formation. Nat Prod Rep 28: 1035–1053.
  33. 33. Isegawa M, Maeda S, Tantillo DJ, Morokuma K (2014) Predicting pathways for terpene formation from first principles - routes to known and new sesquiterpenes. Chem Sci 5: 1555–1560.
  34. 34. Hong YJ, Tantillo DJ (2014) Branching out from the bisabolyl cation. Unifying mechanistic pathways to barbatene, bazzanene, chamigrene, chamipinene, cumacrene, cuprenene, dunniene, isobazzanene, iso-gamma-bisabolene, isochamigrene, laurene, microbiotene, sesquithujene, sesquisabinene, thujopsene, trichodiene, and widdradiene sesquiterpenes. J Am Chem Soc 136: 2450–2463.
  35. 35. Hong YJ, Tantillo DJ (2014) Biosynthetic consequences of multiple sequential post-transition-state bifurcations. Nat Chem 6: 104–111.
  36. 36. Major DT, Freud Y, Weitman M (2014) Catalytic control in terpenoid cyclases: multiscale modeling of thermodynamic, kinetic, and dynamic effects. Curr Opin Chem Biol 21C: 25–33.
  37. 37. Weitman M, Major DT (2010) Challenges posed to bornyl diphosphate synthase: diverging reaction mechanisms in monoterpenes. J Am Chem Soc 132: 6349–6360.
  38. 38. Tian BX, Eriksson LA (2012) Catalytic mechanism and product specificity of oxidosqualene-lanosterol cyclase: a QM/MM study. J Phys Chem B 116: 13857–13862.
  39. 39. Rajamani R, Gao J (2003) Balancing kinetic and thermodynamic control: the mechanism of carbocation cyclization by squalene cyclase. J Am Chem Soc 125: 12768–12781.
  40. 40. Major DT, Weitman M (2012) Electrostatically guided dynamics–the root of fidelity in a promiscuous terpene synthase? J Am Chem Soc 134: 19454–19462.
  41. 41. Zu L, Xu M, Lodewyk MW, Cane DE, Peters RJ, et al. (2012) Effect of isotopically sensitive branching on product distribution for pentalenene synthase: support for a mechanism predicted by quantum chemistry. J Am Chem Soc 134: 11369–11371.
  42. 42. Fan H, Hitchcock DS, Seidel RD 2nd, Hillerich B, Lin H, et al. (2013) Assignment of pterin deaminase activity to an enzyme of unknown function guided by homology modeling and docking. J Am Chem Soc 135: 795–803.
  43. 43. Gerlt JA, Allen KN, Almo SC, Armstrong RN, Babbitt PC, et al. (2011) The Enzyme Function Initiative. Biochemistry 50: 9950–9962.
  44. 44. Hermann JC, Marti-Arbona R, Fedorov AA, Fedorov E, Almo SC, et al. (2007) Structure-based activity prediction for an enzyme of unknown function. Nature 448: 775–779.
  45. 45. Kalyanaraman C, Imker HJ, Fedorov AA, Fedorov EV, Glasner ME, et al. (2008) Discovery of a dipeptide epimerase enzymatic function guided by homology modeling and virtual screening. Structure 16: 1668–1677.
  46. 46. Kalyanaraman C, Jacobson MP (2010) Studying enzyme-substrate specificity in silico: a case study of the Escherichia coli glycolysis pathway. Biochemistry 49: 4003–4005.
  47. 47. Lukk T, Sakai A, Kalyanaraman C, Brown SD, Imker HJ, et al. (2012) Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily. Proc Natl Acad Sci U S A 109: 4122–4127.
  48. 48. Rakus JF, Kalyanaraman C, Fedorov AA, Fedorov EV, Mills-Groninger FP, et al. (2009) Computation-Facilitated Assignment of the Function in the Enolase Superfamily: A Regiochemically Distinct Galactarate Dehydratase from Oceanobacillus iheyensis. Biochemistry 48: 11546–11558.
  49. 49. Song L, Kalyanaraman C, Fedorov AA, Fedorov EV, Glasner ME, et al. (2007) Prediction and assignment of function for a divergent N-succinyl amino acid racemase. Nat Chem Biol 3: 486–491.
  50. 50. Wallrapp FH, Pan JJ, Ramamoorthy G, Almonacid DE, Hillerich BS, et al. (2013) Prediction of function for the polyprenyl transferase subgroup in the isoprenoid synthase superfamily. Proc Natl Acad Sci U S A 110: E1196–E1202.
  51. 51. Tian B, Wallrapp F, Kalyanaraman C, Zhao S, Eriksson LA, et al. (2013) Predicting enzyme-substrate specificity with QM/MM methods: a case study of the stereospecificity of (D)-glucarate dehydratase. Biochemistry 52: 5511–5513.
  52. 52. Zhao S, Kumar R, Sakai A, Vetting MW, Wood BM, et al. (2013) Discovery of new enzymes and metabolic pathways by using structure and genome context. Nature 502: 698–702.
  53. 53. Jacobson MP, Kalyanaraman C, Zhao S, Tian B (2014) Leveraging structure for enzyme function prediction: methods, opportunities, and challenges. Trends Biochem Sci 39: 363–371.
  54. 54. Xiong Q, Rocco F, Wilson WK, Xu R, Ceruti M, et al. (2005) Structure and reactivity of the dammarenyl cation: configurational transmission in triterpene synthesis. J Org Chem 70: 5362–5375.
  55. 55. Rosta E, Klahn M, Warshel A (2006) Towards accurate ab initio QM/MM calculations of free-energy profiles of enzymatic reactions. J Phys Chem B 110: 2934–2941.
  56. 56. Garcia-Viloca M, Gao J, Karplus M, Truhlar DG (2004) How enzymes work: Analysis by modern rate theory and computer simulations. Science 303: 186–195.
  57. 57. Gao J, Ma S, Major DT, Nam K, Pu J, et al. (2006) Mechanisms and free energies of enzymatic reactions. Chem Rev 106: 3188–3209.
  58. 58. Senn HM, Thiel W (2007) QM/MM studies of enzymes. Curr Opin Chem Biol 11: 182–187.
  59. 59. van der Kamp MW, Mulholland AJ (2013) Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology. Biochemistry 52: 2708–2728.
  60. 60. Schwartz SD, Schramm VL (2009) Enzymatic transition states and dynamic motion in barrier crossing. Nat Chem Biol 5: 551–558.
  61. 61. Chu YZ, Yao JZ, Guo H (2012) QM/MM MD and Free Energy Simulations of G9a-Like Protein (GLP) and Its Mutants: Understanding the Factors that Determine the Product Specificity. Plos One 7: e37674.
  62. 62. Liao RZ, Thiel W (2013) Convergence in the QM-only and QM/MM modeling of enzymatic reactions: A case study for acetylene hydratase. J Comput Chem 34: 2389–2397.
  63. 63. Xu R, Fazio GC, Matsuda SP (2004) On the origins of triterpenoid skeletal diversity. Phytochemistry 65: 261–291.
  64. 64. Nes WD (2011) Biosynthesis of cholesterol and other sterols. Chem Rev 111: 6423–6451.
  65. 65. Hoshino T, Sato T (2002) Squalene-hopene cyclase: catalytic mechanism and substrate recognition. Chem Commun (Camb) 291–301.
  66. 66. Racolta S, Juhl PB, Sirim D, Pleiss J (2012) The triterpene cyclase protein family: A systematic analysis. Proteins-Structure Function and Bioinformatics 80: 2009–2019.
  67. 67. Hermann JC, Ghanem E, Li Y, Raushel FM, Irwin JJ, et al. (2006) Predicting substrates by docking high-energy intermediates to enzyme structures. J Am Chem Soc 128: 15882–15891.
  68. 68. Xiang DF, Kolb P, Fedorov AA, Xu C, Fedorov EV, et al. (2012) Structure-based function discovery of an enzyme for the hydrolysis of phosphorylated sugar lactones. Biochemistry 51: 1762–1773.
  69. 69. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389–3402.
  70. 70. Sato T, Hoshino H, Yoshida S, Nakajima M, Hoshino T (2011) Bifunctional triterpene/sesquarterpene cyclase: tetraprenyl-beta-curcumene cyclase is also squalene cyclase in Bacillus megaterium. J Am Chem Soc 133: 17540–17543.
  71. 71. Sato T, Yoshida S, Hoshino H, Tanno M, Nakajima M, et al. (2011) Sesquarterpenes (C35 terpenes) biosynthesized via the cyclization of a linear C35 isoprenoid by a tetraprenyl-beta-curcumene synthase and a tetraprenyl-beta-curcumene cyclase: identification of a new terpene cyclase. J Am Chem Soc 133: 9734–9737.
  72. 72. Full C (2001) Bicyclic triterpenes as new main products of squalene-hopene cyclase by mutation at conserved tyrosine residues. Febs Letters 509: 361–364.
  73. 73. Pegg SCH, Brown SD, Ojha S, Seffernick J, Meng EC, et al. (2006) Leveraging enzyme structure-function relationships for functional inference and experimental design: The structure-function linkage database. Biochemistry 45: 2545–2555.
  74. 74. Barber AE 2nd, Babbitt PC (2012) Pythoscape: a framework for generation of large protein similarity networks. Bioinformatics 28: 2845–2846.
  75. 75. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2: 2366–2382.
  76. 76. Schrödinger Suite 2011 Protein Preparation Wizard; Epik version 2.2; Impact version 5.7; Prime version 3.0; Jaguar, version 7.9; LigPrep, version 2.5; Glide, version 5.7; Induced Fit Docking protocol.
  77. 77. Pei J, Kim BH, Grishin NV (2008) PROMALS3D: a tool for multiple protein sequence and structure alignments. Nucleic Acids Res 36: 2295–2300.
  78. 78. Jacobson MP, Friesner RA, Xiang Z, Honig B (2002) On the role of the crystal environment in determining protein side-chain conformations. J Mol Biol 320: 597–608.
  79. 79. Jacobson MP, Pincus DL, Rapp CS, Day TJ, Honig B, et al. (2004) A hierarchical approach to all-atom protein loop prediction. Proteins 55: 351–367.
  80. 80. Shen MY, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15: 2507–2524.
  81. 81. Jorgensen WL, Tiradorives J (1988) The Opls Potential Functions for Proteins - Energy Minimizations for Crystals of Cyclic-Peptides and Crambin. J Am Chem Soc 110: 1657–1666.
  82. 82. Banks JL, Beard HS, Cao Y, Cho AE, Damm W, et al. (2005) Integrated Modeling Program, Applied Chemical Theory (IMPACT). J Comput Chem 26: 1752–1780.
  83. 83. Bochevarov AD, Harder E, Hughes TF, Greenwood JR, Braden DA, et al. (2013) Jaguar: A high-performance quantum chemistry software program with strengths in life and materials sciences. Int J Quantum Chem 113: 2110–2142.
  84. 84. Farid R, Day T, Friesner RA, Pearlstein RA (2006) New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg Med Chem 14: 3160–3173.
  85. 85. Sherman W, Day T, Jacobson MP, Friesner RA, Farid R (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49: 534–553.
  86. 86. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, et al. (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47: 1739–1749.
  87. 87. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, et al. (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49: 6177–6196.
  88. 88. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, et al. (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47: 1750–1759.
  89. 89. Park MS, Gao C, Stern HA (2011) Estimating binding affinities by docking/scoring methods using variable protonation states. Proteins 79: 304–314.