Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily

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DOI: 10.1073/pnas.1112081109 · Source: PubMed
The rapid advance in genome sequencing presents substantial challenges for protein functional assignment, with half or more of new protein sequences inferred from these genomes having uncertain assignments. The assignment of enzyme function in functionally diverse superfamilies represents a particular challenge, which we address through a combination of computational predictions, enzymology, and structural biology. Here we describe the results of a focused investigation of a group of enzymes in the enolase superfamily that are involved in epimerizing dipeptides. The first members of this group to be functionally characterized were Ala-Glu epimerases in Eschericiha coli and Bacillus subtilis, based on the operon context and enzymological studies; these enzymes are presumed to be involved in peptidoglycan recycling. We have subsequently studied more than 65 related enzymes by computational methods, including homology modeling and metabolite docking, which suggested that many would have divergent specificities;, i.e., they are likely to have different (unknown) biological roles. In addition to the Ala-Phe epimerase specificity reported previously, we describe the prediction and experimental verification of: (i) a new group of presumed Ala-Glu epimerases; (ii) several enzymes with specificity for hydrophobic dipeptides, including one from Cytophaga hutchinsonii that epimerizes D-Ala-D-Ala; and (iii) a small group of enzymes that epimerize cationic dipeptides. Crystal structures for certain of these enzymes further elucidate the structural basis of the specificities. The results highlight the potential of computational methods to guide experimental characterization of enzymes in an automated, large-scale fashion.
Homology models guide discovery of diverse
enzyme specificities among dipeptide
epimerases in the enolase superfamily
Tiit Lukk
, Ayano Sakai
, Shoshana D. Brown
, Heidi J. Imker
, Alexander A.
, Elena V. Fedorov
, Rafael Toro
, Brandan Hillerich
, Yury Patskovsky
, Matthew W. Vetting
Satish K. Nair
, Patricia C. Babbitt
, and Matthew P. Jacobson
Departments of Biochemistry and Chemistry, University of Illinois at Urbana Champaign, Urbana, IL 61801;
Department of Pharmaceutical Chemistry,
School of Pharmacy and California Institute for Quantitative Biomedical Resear ch, University of California, 1700 4th Street, San Francisco, CA 94158;
Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY 10461; and
Department of Bioengineering and Therapeutic Sciences, School
of Pharmacy and California Institute for Quantitative Biomedical Research, Univer sity of California, 1700 4th Street, San Francisco, CA 94158
Edited by Barry Honig, Columbia Univer sity/Howard Hughes Medical Institute, New York, NY, and approved December 2, 2011 (received for review July
25, 2011)
The rapid advance in genome sequencing presents substantial
challenges for protein functional assignment, with half or more of
new protein sequences inferred from these genomes having uncer-
tain assignments. The assignment of enzyme function in function-
ally diverse superfamilies represents a particular challenge, which
we address through a combination of computational predictions,
enzymology, and structural biology. Here we describe the results
of a focused investigation of a group of enzymes in the enolase
superfamily that are involved in epimerizing dipeptides. The first
members of this group to be functionally characterized were
Ala-Glu epimerases in Eschericiha coli and Bacillus subtilis, based
on the operon context and enzymological studies; these enzymes
are presumed to be involved in peptidoglycan recycling. We have
subsequently studied more than 65 related enzymes by computa-
tional methods, including homology modeling and metabolite
docking, which suggested that many would have divergent speci-
ficities;, i.e., they are likely to have different (unknown) biological
roles. In addition to the Ala-Phe epimerase specificity reported pre-
viously, we describe the prediction and experimental verification
of: (i) a new group of presumed Ala-Glu epimerases; (ii) several en-
zymes with specificity for hydrophobic dipeptides, including one
from Cytophaga hutchinsonii that epimerizes D-Ala-D-Ala; and
(iii) a small group of enzymes that epimerize cationic dipeptides.
Crystal structures for certain of these enzymes further elucidate
the structural basis of the specificities. The results highlight the
potential of computational methods to guide experimental charac-
terization of enzymes in an automated, large-scale fashion.
computational biology enzymology protein function
he number of sequences in the protein databases continues
to expand, with almost 18 million entries in the nonredundant
TrEMBL database (October 2011; www.ebi.ac.uk/trembl/). De-
spite this abundance of data, an increasingly large proportion
of these sequences have uncertain, unknown, or incorrectly an-
notated functions (1). Without reliable functional assignments,
the promise contained in Natures repertoire of enzymes and
metabolic pathways for advances in medicine, chemistry, and in-
dustry cannot be fully realized. Because the number of protein
sequences is large, a computation-guided strategy for discovering
the functions of proteins discovered in genome projects will be
required to address this challenge. Predicting a proteins function
from sequence remains challenging, in part because the bound-
aries between functions in sequence space can be difficult to
define; that is, closely related sequences (e.g., 60% sequence
identity) can have different functions, e.g., ref. (2), while highly
divergent sequences with no detectable sequence similarity can
have identical functions. e.g., refs. (3, 4).
Protein function can be characterized at many different levels;
here we focus exclusively on the substrate specificity of enzymes,
as determined by in vitro biochemistry. Assigning biochemical
function is challenging in functionally diverse enzyme super-
families. A recent survey by Babbitt and coworkers estimates that
there are approximately 275 superfamilies encompassing two or
more distinct biochemical functions, representing approximately
one-third of the known enzyme universe (5). The functionally
diverse enolase superfamily has provided a particularly challen-
ging model system for developing methods for predicting and
characterizing enzyme specificity (6). To date, more than 20 dis-
tinct substrates have been identified for members of the enolase
superfamily [see the Structure-Function Linkage Database
(SFLD; http://sfld.rbvi.ucsf.edu )]. The active sites of the members
of this superfamily are located at the interface between a ðβαÞ
barrel domain that contains functional groups involved in cataly-
sis and an N-terminal (α þ β) capping domain that contains side
chains that determine substrate specificity. These enzymes cata-
lyze unimolecular reactions initiated by abstraction of the α-pro-
ton of a carboxylate substrate by an active site base catalyst to
form a Mg
stabilized enediolate intermediate that is directed
to products by the participation of an acid catalyst (7). Therefore,
prediction of the substrate specificity is sufficient to enable func-
tional assignment.
A subset of proteins in the superfamily shares a pair of Lys
acid/base catalysts at the ends of the second and sixth β-strands
as well as an Asp-x-Asp (DxD) motif at the end of the eighth
β-strand of the ðβαÞ
β barrel domain. The L-Ala-D/L-Glu epi-
merase (AEE) from Bacillus subtilis is the structure-function
paradigm for these enzymes: the conserved Lys catalysts are the
acid/base catalysts for the 1,1 proton transfer (epimerization)
reaction (SI Appendix, Fig. S1), the α-ammonium group of the
Author contributions: T.L., A.S., C.K., H.J.I., L.S., A.A.F., E.V.F., S.K.N., P.C.B., S.C.A., J.A.G.,
and M.P.J. designed research; T.L., A.S., C.K., S.D.B., H.J.I., L.S., A.A.F., E.V.F., R.T., B.H., R.S.I.,
Y.P., and M.W.V. performed research; T.L., A.S., C.K., S.B., H.J.I., L.S., A.A.F., E.V.F., Y.P.,
M.W.V., and M.P.J. analyzed data; and T.L., A.S., C.K., S.B., H.J.I., A.A.F., Y.P., M.W.V.,
S.K.N., P.C.B., S.C.A., J.A.G., and M.P.J. wrote the paper.
The authors declare a conflict of interest. M.P.J. is a consultant to Schrodinger LLC, which
developed or licensed some of the software used in this study.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
Data deposition: The atomic coordinates and structure factors have been deposited in the
Protein Data Bank, www.pdb.org (PDB ID codes 3JVA, 3JW7, 3JZU, 3K1G, 3KUM, 3IJI, 3IJL,
3IJQ, 3Q4D, 3Q45, 3RO6, 3RIT, 3R10, 3R11, 3R1Z, 3R0U, 3R0K, and 3IK4).
T.L., A.S., and C.K. contributed equally to this work.
To whom correspondence may be addressed: E-mail: babbitt@cgl.ucsf.edu, almo@
aecom.yu.edu, j-gerlt@uiuc.edu, or matt.jacobson@ucsf.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/
41224127 PNAS March 13, 2012 vol. 109 no. 11 www.pnas.org/cgi/doi/10.1073/pnas.1112081109
dipeptide substrate is hydrogen bonded to the DxD motif, and the
γ-carboxylate group of the Glu residue is hydrogen bonded to
Arg24 in the capping domain (8). Because the sequences of
the recognition loops in the capping domains are not conserved
in homologs, our expectation was that these are dipeptide epi-
merases that utilize different dipeptide substrates.
As of June 2011, more than 700 protein sequences in the
enolase superfamily were predicted to have a dipeptide epimerase
function based on the predicted presence of the DxD motif and
Lys acid/base catalysts (e.g., examples in SI Appendix, Fig. S2).
In 2006, when the computational results were generated, only
66 putative dipeptide epimerases (and 18 additional environmen-
tal sequences) were present in the sequence databases, so we cre-
ated homology models of all of them. We set out to determine how
many of these were likely to be specific for L-Ala-D/L-Glu and
how many other specificities might be present. Even within this
relatively small group of enzymes, it would not be possible to ex-
perimentally characterize every member, in part due to challenges
and expense associated with expressing the proteins. To provide
specific predictions of substrate specificity, we used virtual library
screening against the solvent-sequestered active site, which pro-
vides a defined cavity for the substrate. We previously reported
computational predictions and in vitro screening to assign the
L-Ala-D/L-Phe epimerase activity to a protein from Thermotoga
maritima (gi:156442781; TM006) (9). In addition, the N-succinyl-
Arg racemase function was assigned to an enzyme from Bacillus
cereus that clustered phylogenetically with the dipeptide epimerases
(10). In both cases, the substrate specificities were predicted using
homology models and then experimentally verified; structures were
then determined of substrate-liganded complexes, confirming the
validity of the homology models and predicted poses for the en-
zyme/substrate complexes.
Here, we describe a large-scale computational prediction of
specificity for representative members of the dipeptide epimerase
group (all members that were available in 2006 when initial pre-
dictions were made), in vitro testing of these predictions for 17
members of the group, and determination of a 18 crystal struc-
tures for six members of the group, including 11 structures of sub-
strate-liganded complexes. The homology models and docking
results led to predictions of a rich diversity of substrate specificity
for several previously uncharacterized groups of dipeptide epi-
merases. Subsequent biochemical and structural studies confirmed
the key predictions, including (i) a group of AEEs, phylogeneti-
cally distinct from the two previously characterized groups; (ii)
several groups of epimerases with specificity for hydrophobic di-
peptides, including a group dominated by sequences from plants;
and (iii) a small group of epimerases with specificity for positively
charged dipeptides. The sequences with divergent (i.e., non-AEE)
specificity are very likely to have distinct in vivo functions, which
remain unknown, although one particular member with specificity
for D-Ala-D-Ala is likely to be involved in processing peptidogly-
can, which is also the assumed function of the AEEs.
Sequence Analysis and Clustering. Relationships among the se-
quences are depicted in the sequence similarity networks shown
in Figs. 1 and 2, where each edge (line) in the Cytoscape-gener-
ated representation indicates a pair of proteins (nodes) that have
a Blast e-value more stringent than a certain cutoff value. Advan-
tages of sequence similarity networks include the ability to visua-
lize relationships among large numbers of sequences, such as the
735 represented here, with modest computational expense (11).
As shown elsewhere, this type of clustering analysis tracks closely
with phylogenetic trees generated using more rigorous methods,
and we have confirmed this finding by comparing the networks
to trees for representative members of this group [e.g., Fig. 4
in ref. (9)]. Representing the networks using different e-value
cutoffs makes it possible to visualize sequence and functional
relationships at different levels of granularity.
Fig. 1 indicates the two previously characterized AEEs from
B. subtilis and Escherichia coli (12), which represent distinct clus-
ters and use different constellations of amino acids in the binding
sites to achieve a common specificity for L-Ala-L-Glu. As shown
in SI Appendix, Fig. S3, although the putative dipeptide epi-
merases are strongly dominated by sequences from bacteria, a
relatively small number of the sequences are found in archaea
and eukaryotes, specifically plants and protozoa. With few excep-
tions, the archaeal and eukaryotic sequences do not cluster tightly
with the characterized AEEs, suggesting potentially divergent
function, since only prokaryotes have peptidoglycan that contains
L-Ala-D-Glu. (We note, however, that some eukaryotic sequences
are from organisms such as Dictyostelium, which eat bacteria;
we predict that these sequences do in fact have specificity for
L-Ala-D/L-Glu (SI Appendix,Fig.S4), but thus far have been un-
able to obtain these proteins to confirm this prediction.)
Computational Predictions of Specificity. We created homology
models of all members of the group for which sequences were
available in 2006, 84 sequences in total (16 of which were envir-
onmental sequences, which were not studied further), using the
structure of the B. subtilis AEE in complex with L-Ala-L-Glu as
atemplate(SI Appendix, Table S1). Several of these models sug-
gested important substitutions in specificity-determining residues.
To predict specific dipeptide substrates, we then used virtual
screening methods, more commonly used in structure-based drug
design, to dock all 400 possible L/L-dipeptides to each homology
model, thus predicting which dipeptides are likely to bind as well as
their poses in the binding site. The results were analyzed by gen-
erating a consensus profile that indicated the dominant amino acid
preferences in the N- and C-terminal positions, averaged over the
putative specificity groups (SI Appendix, SI Methods).
As described previously (9), L-Ala-L-Glu ranked among the
top hits for the B. subtilis AEE, as well as the E. coli AEE homo-
logy model, despite the different sequence determinants of spe-
cificity among these two enzymes and the relatively low sequence
Fig. 1. Sequence similarity network for the putative dipeptide epimerases in
the enolase superfamily. Each node represents a single sequence; edges re-
presents connections with BLAST e-value better than the cutoff value shown
in each panel. Enzymes whose sequences were available in 2006 are colored
according to their computationally predicted specificity, using homology
models and ligand docking (larger nodes). The previously characterized
Ala-Glu epimerases from E. coli and B. subtilis are labeled.
Lukk et al. PNAS March 13, 2012 vol. 109 no. 11 4123
identity between them (approximately 30%). As shown in Fig. 1,
the top docking hits for the sequences closely related to the two
characterized AEEs were dominated by dipeptides with small
amino acids in the N-terminal position and either Asp or Glu in
the epimerized position (e.g., Ser-Asp). The key specificity-deter-
mining residues were also conserved within these two groups, so
we predicted that all of these enzymes are AEEs.
One other uncharacterized group of enzymes was also domi-
nated by hit lists with negatively charged amino acids in the
C-terminal position, but some showed relaxed specificity at the
N-terminal position, e.g., Phe, Asn, or other larger amino acids
among the top-ranked hits. We labeled this group as Xxx-Glu.
Note that it is now clear that this group splits into several clusters
at a Blast e-value of 1e-70 (Fig. 1), but only a subset of these
sequences was available at the time the predictions were made,
and they were treated as one cluster based on the initial phylo-
genetic tree at that time.
Two other clearly distinct specificities were also predicted on
the basis of the modeling. First, sequences from Gloeobacter
violaceus and Methylococcus capsulatus (as well as four closely
related environmental sequences) were predicted to be specific
for positively charged dipeptides, with a strong preference for
positive amino acids in the N-terminal position. We labeled these
as Lys-Xxx (pink nodes in Fig. 1). Finally, a group of sequences,
including the previously reported T. maritima sequence (9), was
predicted to prefer hydrophobic dipeptides. We labeled these as
predicted Ala-hydrophobic epimerases, because many of these
showed a predicted preference for Ala in the N-terminal position.
Determination of in Vitro Biochemical Function. Protein expression
and purification were attempted for dozens of representative se-
quences, heavily weighted towards the groups predicted to have
divergent specificity; i.e., not closely related to the characterized
AEEs. A total of 17 enzymes was obtained for in vitro charac-
terization, with several in each new specificity group (SI
Appendix, Table S2). Of these, only five were among those in the
original homology model-based predictions, from M. capsulatus
(from the Lys-Xxx group), B. thetaiotaomicron (from the Xxx-
Glu group), C. hutchinsonii (from the Ala-Hyd group), E. faecalis
(from the B. subtilis-like group), and T. maritima (Ala-Hyd, de-
scribed previously). Protein expression and purification for many
others failed, but the other 12 enzymes that were successfully
obtained also populate the regions of sequence space predicted
to have divergent specificity.
These were first subjected to a mass spectroscopy-based assay
that detects epimerization by incorporation of deuterium and
can be used in a multiplexed fashion with mixtures of dipeptides.
The first library to be screened consisted of all L-Ala-L-Xxx
dipeptides other than L-Ala-Gly or L-Ala-L-Cys; the results are
summarized in SI Appendix, Table S3. A few of these enzymes
showed specificity for L-Ala-L-Glu in this assay, such as the
dipeptide epimerase from Bacteroides thetaiotamicron (a domi-
nant member of the human gut microbiome), from the group of
enzymes predicted based on the modeling to be Xxx-Glu epi-
merases, and Campylobacterales bacterium which clusters with
the B. subtilis AEE group (Fig. 2). However, the majority of the
other enzymes showed divergent (i.e., not Glu) specificities at
the C-terminal position, which were mostly consistent with the
computational predictions for the groups to which they belong.
For example, the M. capsulatus and Desulfobacterium autotrophi-
cum dipeptide epimerases showed clear specificity for positively
charged dipeptides; the enzymes from clusters predicted to be
Ala-Hyd did, in fact, show epimerization with hydrophobic
amino acids at the C-terminal position.
In all cases, the enzymes subsequently were assayed with
additional libraries to define the N-terminal specificity; the com-
plete results are reported in SI Appendix, Tables S4S20.For
five of the dipeptide epimerases, additional experiments were
carried out to determine kinetic constants for various dipeptide
substrates. Selected results, primarily for the best substrates for
each enzyme, are summarized in Table 1; detailed results for all
substrates tested are reported in SI Appendix, Tables S21S25.
The results are summarized on Fig. 2. In cases where kinetic con-
stants were obtained, we label the enzyme with the single best
substrate, as judged by k
. In cases where only mass spec
screening was performed, the assigned specificity is necessarily
more qualitative. In many cases, no clear preference for a single
amino acid was found at either position; in such cases we use the
abbreviations Hyd to represent little selectivity among various
hydrophobic amino acids; Pos to represent Arg, Lys, and His;
and Xxx in cases where most amino acids are tolerated. In cases
where Glu is preferred in the C-terminal position and Ala is
tolerated in the N-terminal position, we label the enzyme as
Ala-Glu, because we assume that that is the relevant substrate
in vivo. In all cases, the concise labels clearly cannot capture all
of the specificity information provided by the screening.
Fig. 2. Sequence similarity network with experimentally characterized
enzyme specificities labeled. Larger nodes represent sequences that have
been experimentally characterized in this or earlier studies. Circles represent
sequences for which crystal structures have been determined. Node border
colors represent the type of experiment used to establish substrate specificity
(red, kinetics; blue, mass spectrometry). In cases where there is no clear spe-
cificity for a single amino acid, Pos refers to specificity for positively
charged amino acids (Lys, Arg, and His), Hyd refers to specificity for hydro-
phobic am ino acids, and Xxx refers to no significant specificity observed.
Table 1. Enzyme kinetics with selected dipeptide substrates
substrate k
] K
B. thetaiotamicron L-Ala-L-Glu 147 ± 10 2.0 ± 1 7.4 × 10
L-Ala-D-Glu 59 ± 7 6.0 ± 2 9.8 × 10
L-Val-L-Glu 96 ± 7 1.7 ± 0.2 5.6 × 10
C. hutchinsonii D-Ala-D-Ala 43 ± 1.3 1.9 ± 0.2 2.3 × 10
D-Ala-L-Ala 58 ± 4 1.1 ± 0.3 5.3 × 10
D-Ala-L-Val 19 ± 0.8 0.50 ± 0.1 3.7 × 10
L-Ala-L-Ala 37 ± 3 5.0 ± 0.7 7.5 × 10
E. faecalis L-Ile-L-Tyr 9.2 ± 0.7 15 ± 0.07 1.2 × 10
L-Val-L-Tyr 8.7 ± 2 0.70 ± 0.4 1.4 × 10
L-Arg-L-Tyr 15 ± 0.07 1.4 ± 0.2 1.0 × 10
H. aurantiacus L-Phe-L-Tyr 4.7 ± 1 4.8 ± 1 980
M. capsulatus L-Lys-L-Arg 8.4 ± 1 0.44 ± 0.1 1.9 × 10
L-Lys-L-Lys 0.029 ± 0.001 0.15 ± 0.02 200
L-Arg-L-Arg 0.72 ± 0.4 0.19 ± 0.03 3.6 × 10
4124 www.pnas.org/cgi/doi/10.1073/pnas.1112081109 Lukk et al.
In addition, as summarized in Table 2, a total of 18 crystal
structures were obtained for six of the dipeptide epimerases,
including 11 structures with Mg
and five with dipeptide sub-
strates (SI Appendix, Tables S26S31). These structures reveal
the basis for the observed specificity; depictions of the binding
sites appear in Figs. 3 and 4, and SI Appendix, Figs. S5S11. Com-
parisons of these structures with the previously predicted models,
with optimal substrates bound, are shown in SI Appendix,
Fig. S12. Although the predicted enzyme-substrate complexes
show some differences from the subsequently determined struc-
tures, most of the key interactions between the substrate and the
active site were correctly predicted.
A Unique Class of Ala-Glu Epimerases. The computational modeling
suggested that, in addition to the sequences clustering with the
E. coli and B. subtilis AEEs, a third distinct group of dipeptide
epimerases would have specificity for negatively charged amino
acids at the C-terminal position, but somewhat relaxed specificity
at the N-terminal position (Xxx-Glu in Fig. 1). Among the se-
quences included in the modeling study, the dipeptide epimerase
from B. thetaiotamicron (BT1313) was characterized by mass
spectroscopy-based screening followed by more detailed enzyme
kinetics with selected substrates. These results for BT1313 are
consistent with its assignment as an AEE, with similar kinetic
constants to the previously characterized AEEs from B. subtilis
and E. coli. However, L-Val-L-Glu showed similar kinetics to
L-Ala-L-Glu, and both Ile and Leu were also tolerated in the
N-terminal position with k
> 10
, confirming the
prediction that amino acids larger than Ala would be tolerated in
the N-terminal position. The promiscuous substrate specificity
observed in vitro would be physiologically unimportant if dipep-
tides containing the variants in the N-terminal position are not
present in the organism; metabolomic experiments could provide
support for this expectation.
Crystals could be obtained for BT1313 in the presence of Mg
and either L-Ala-D-Glu (3IJI) or L-Pro-D-Glu (3IJL). These
structures revealed that the dipeptide substrate/product was
bound in a nonproductive complex, with the carboxylate group in
the second coordination sphere of the Mg
. A second monocli-
nic crystal form was then obtained in the presence of Mg
and L-
Ala-D-Glu (3IJQ), with two polypeptides in the asymmetric unit.
Although the carboxylate group of the dipeptide is located in the
second coordination sphere of the Mg
in polypeptide A, in
polypeptide B the carboxylate group is a bidentate ligand of
the Mg
, and the Nξ atoms of Lys200 and Lys298 are in appro-
priate locations relative to the α-carbon of the Glu residue to
function as the acid/base catalysts.
As expected from the sequence identity (31%), the structures
of BT1313 and the template for its homology modeling, the AEE
from B. subtilis, are well superimposed (rmsd ¼ 1.1 Å for 206 C
pairs), although BT1313 is a monomer and the AEE from B. sub-
tilis is an octamer. Focusing on the active sites, however, these
structures reveal differing structural bases for the conserved spe-
cificity for L-Ala-D/L-Glu. In the AEE from B. subtilis, Arg24
in the 20s loop of the capping domain provides key specificity-
determining interactions with the α-carboxylate group of the Glu
moiety of the substrate; the analogous residue in BT1313 is
Arg68. As illustrated in Fig. 3, these two Arg are not spatially
conserved. The crystal structure of the AEE from E. coli has not
been determined with a dipeptide substrate (12), but there are
two Arg in the 20s loop that may coordinate the glutamate side
chain, neither of which aligns to Arg24 of the B. subtilis AEE,
and one of which may align to Arg68 from B. thetaiotaomicron
(SI Appendix, Fig. S2). Thus, the AEE substrate specificity can
be assigned to three distinct groups, highlighting the ability of
divergent evolution within the enolase superfamily to deliver the
same function. Previous examples of the malleability of structure
to enable conserved substrate specificity within the enolase super-
family are provided by N-succinylamino acid racemases (10, 13),
cis,cis-muconate lactonizing enzymes (13), and galactarate dehy-
dratases (producing enantiomeric products) (14).
Finally, the dipeptide epimerase from Francisella philomiragia
(Fphi1647), a pathogenic bacterium, is an example of an AEE
that does not cluster tightly with any of the three major clades of
AEEs; a second example is the dipeptide epimerase from Cam-
pylobacterales bacterium, which was not structurally characterized.
In the sequence network in Fig. 2, these sequences can be found
on the periphery of the B. subtilis AEE group, and, judging by
overall sequence identity, they are as similar to some of the non-
AEE dipeptide epimerases as they are to the B. subtilis AEE. For
example, Fphi1647 shares 37% sequence identity to the L-Leu-L-
Tyr epimerase from E. faecalis discussed below, and 35% sequence
identity to the AEE from B. subtilis. However, the dipeptide epi-
merase activity screening of Fphi1647 showed strict specificity
for only Glu (preferred) or Asp in the epimerized position (SI
Appendix, Table S3). The structure of Fphi1647 in complex with
L-Ala-L/D-Glu (3R1Z) shows that, despite the relatively low over-
all sequence identity, the interactions between the dipeptide and
protein are virtually identical to those seen for the B. subtilis AEE.
Specifically, Arg26 of the 20 s loop is spatially and sequentially
conserved with Arg24 of the B. subtilis 20 s loop (Fig. 4).
Cationic Dipeptide Epimerases. One of the most striking predictions
made using computational modeling was the existence of a small
group of proteins with specificity for positively charged dipep-
tides. Among the modeled sequences, the dipeptide epimerase
from M. capsulatus (MCA1834) was successfully characterized
by in vitro biochemistry and crystallography; the closely related
Table 2. New dipeptide epimerase crystallographic structures
Species Ligands PDB entry Resolution (Å) Rfree
E. faecalis apo 3JVA 1.7 0.224
L-Ile-L-Tyr 3JW7 1.8 0.254
L-Leu-L-Tyr 3JZU 2.0 0.281
L-Ser-L-Tyr 3K1G 2.0 0.282
L-Arg-L-Tyr 3KUM 1.9 0.270
B. thetaiotamicron L-Ala-D-Glu 3IJI 1.6 0.218
L-Pro-D-Glu 3IJL 1.5 0.205
L-Ala-D-Glu 3IJQ 2.0 0.287
C. hutchinsonii D-Ala-L-Ala 3Q4D 3.0 0.254
D-Ala-L-Val 3Q45 3.0 0.251
M. capsulatus apo 3RO6 2.2 0.229
L-Arg-D-Lys 3RIT 2.7 0.222
F. philomiragia apo 3R10 2.0 0.191
fumarate 3R11 2.0 0.181
L-Ala-D/L-Glu 3R1Z 1.9 0.185
tartrate 3R0U 1.9 0.195
tartrate (no Mg
) 3R0K 2.0 0.197
H. aurantiacus apo (no Mg
) 3IK4 2.1 0.275
All structures contain Mg
unless otherwise indicated.
Fig. 3. Comparison of the active sites of the dipeptide epimer ases from (A)
B. thetaiotamicron (3IJQ) and (B) B. subtilis (1TKK). The dipeptide substrates
and key residues in the catalytic site are shown in stick representation, and
the Mg
is shown as a sphere.
Lukk et al. PNAS March 13, 2012 vol. 109 no. 11 4125
enzyme from D. autotrophicum was also characterized by mass
spectroscopy-based screening, with similar results. The in vitro
kinetics for MCA1834 confirmed the predicted preference for
positively charged dipeptides. Dicationic dipeptides were actually
preferred, with L-Lys-L-Arg being the best substrate, with
¼ 1.9 × 10
, which is similar to other members
of the enolase superfamily. This strong preference for dicationic
dipeptides was not predicted.
Crystals structures for MCA1834 were solved either in com-
plex with Mg
ion alone (3RO6) or complexed both with
L-Arg-D-Lys and Mg
(3RIT). Interestingly, the binding site
of MCA1834 contains an Arg at the same position as Arg24 in the
B. subtilis AEE, where it is used to stabilize the δ-carboxylate
group of the glutamate portion of the Ala-Glu substrate. In the
structure of MCA1834, the same residue is involved in a salt
bridge with Glu51 (Fig. 4), perhaps to help position that side
chain and bring about a closed conformation of the active site
upon substrate binding. The conservation of this Arg between
two enzymes with completely different specificities highlights a
challenge of functional annotation; in this case, conservation
of an amino acid critical for specificity in the AEE does not imply
conservation of enzyme specificity (and presumably in vivo func-
tion). Conversely, nonconservation of the same amino acid does
not necessarily imply a new specificity, as illustrated by the group
of AEEs discussed above that achieve specificity in a different
manner than the B. subtilis AEE. These examples highlight the
utility of computationally predicted and experimentally deter-
mined structures for helping to characterize specificity.
The side chain of Glu51 is likely the key specificity determinant
for a positively charged residue in the C-terminal side of the
dipeptide substrate, while the positively charged side chain at the
N-terminal position of the substrate is stabilized via interactions
with the side chains of Asp296 and Asp325. The hydroxyl group
of Tyr19 is hydrogen-bonded to one of the carboxylate oxygens
of Asp296 and secludes the binding pocket for the N-terminal
portion of the substrate from the C-terminal side.
Several Groups of Hydrophobic Dipeptide Epimerases. The computa-
tional modeling suggested that a group of enzymes, distantly
(approximately 30% sequence identity) related to the B. subtilis
AEE, would be specific for hydrophobic dipeptides. One of these
dipeptide epimerases, from T. maritima, was reported previously
(9); to show specificity for L-Ala-L-Phe and similar dipeptides.
Four of the predicted hydrophobic dipeptide epimerases belong
to a group that includes a number of plant enzymes (SI Appendix,
Fig. S3). Three enzymes in this cluster were characterized in vitro,
all of which showed specificity for hydrophobic dipeptides gener-
ally with fairly broad specificity in one or both positions. including
the dipeptide epimerase from the black cottonwood tree, which
showed specificity primarily for hydrophobic dipeptides (Populus
trichocarpa, SI Appendix, Table S15; Herpetosiphon aurantiacus, SI
Appendix, Tables S7 and S24; Geobacter bemidjiensis, SI Appendix,
Table S10).
The E. faecalis dipeptide epimerase (EF1511) clustered closely
with the B. subtilis AEE group in both the initial definition of
the specificity groups and the sequence networks in Fig. 1, and
so was erroneously predicted to have an AEE specificity. Instead,
specificity in the C-terminal (epimerized) position is similar to
that of the previously reported T. maritima dipeptide epimerase,
with Phe, His, and especially Tyr preferred; the two enzymes are
not closely related, however. N-Terminal specificity is broad, with
L-Ile-L-Tyr, L-Val-L-Tyr, and L-Arg-L-Tyr being the best sub-
strates. Several structures of EF1511 in complex with dipeptide
substrates were determined, revealing the basis for specificity
(Fig. 4 and SI Appendix, Fig. S5). The pocket for the C-terminal
Tyr residue is, as expected, primarily hydrophobic, but the OH
group on the Tyr side chain is hydrogen-bonded to Glu295.
Interestingly, EF1511 showed no turnover with dipeptides con-
taining Lys at the C-terminal position, which would also be cap-
able of forming seemingly favorable electrostatic interactions
with Glu295, based on the docking results. We speculate that the
specificity for Tyr may result, in part, from its ability to fill the
large and hydrophobic pocket, which Lys would not do.
The dipeptide epimerase from C. hutchinsonii (CHU2140) is
unique in preferring Ala at both positions, although larger hydro-
phobic residues are somewhat tolerated. The best substrates are
D-Ala-D/L-Ala (Table 1), strongly suggesting a biological role
related to peptidoglycan, which contains D-Ala-D-Ala. Crystal
structures of CHU2140 were solved in complex with Mg
and either D-Ala-L-Ala (3Q4D) or D-Ala-L-Val (3Q45) dipep-
tide substrates. The substrate-binding pocket is formed by
hydrophobic residues including Phe19, Ile21, Phe51, Ile54, and
Phe294. A small hydrophobic pocket containing Phe301 facili-
tates binding of the methyl side chain of D-Ala (Fig. 4). We hypo-
thesize that this hydrophobic pocket is responsible for the stereo-
chemical preference of D-Ala in the N-terminal position of the
substrate molecule. Substituting L-Ala into the same position
would introduce steric clashes with Thr321, resulting in less than
optimal binding and therefore an increased K
value. The latter
was validated experimentally with a range of dipeptide substrates
with an N-terminal L-Ala (Table 1 and SI Appendix, Tables S20
and S22).
Finally, several sequences that cluster with the new, third
group of Ala-Glu epimerases (exemplified by BT1313, from
B. thetaiotamicron) also show specificity for hydrophobic dipep-
tides (Fig. 2 and SI Appendix, Table S3). None of these sequences
were included in the initial modeling; we had assumed, incor-
rectly, that this entire group of dipeptide epimerases would have
the AEE specificity. This group, like the large group of sequences
that cluster with the B. subtilis AEE, contains multiple specifici-
ties, with no clear boundary in the sequence similarity network
between the different specificities.
Through a combination of bioinformatics, computational model-
ing, enzymology, and structural biology, we have identified several
unique classes of dipeptide epimerases and characterized the
structural and chemical determinants that underlie their specifi-
city. The diversity of specificities, summarized in Fig. 2, was unex-
pected and highlights challenges for functional assignment. Most
of these sequences are annotated in Genbank generically as man-
delate racemase/muconate lactonizing enzyme (SI Appendix,
Tabl e S 2), correctly establishing them as members of the enolase
Fig. 4. Binding sites of the dipeptide epimerases from (A) E. faecalis (3JW7),
(B) C. hutchinsoni (3Q4D), (C) M. capsulatus (3RIT) and (D) F. philomiragia
4126 www.pnas.org/cgi/doi/10.1073/pnas.1112081109 Lukk et al.
superfamily, but providing no information about their substrates, a
critical first step in establishing their function. We were able to
correctly identify these proteins as dipeptide epimerases based
on the conservation of key residues in the binding sites, and homol-
ogy models of the enzymes suggested a remarkable diversity of spe-
cificities. Some of the enzymes with divergent (i.e., not Ala-Glu)
specificity are from plants or archaea, as well as a diverse group of
bacterial species. The biological roles of these divergent specifici-
ties are unknown, but the in silico and in vitro studies presented
here provide a starting point for in vivo studies.
The most important role of computational modeling in this
work was to identify groups of enzymes likely to have novel substrate
specificities, thereby guiding the experimental efforts by prioritizing
specific enzymes for expression, purification, and screening. It is not
feasible to experimentally characterize all or even most of the en-
zymes in this group, which is itself only a small fraction of the much
larger functionally diverse enolase superfamily. Moreover, as is clear
from Fig. 2, there are no clear boundaries between specificities in
the sequence map. That is, as has been seen for other classes of
enzymes in the enolase superfamily (4), highly divergent enzymes
can have the same substrates, such as the dominant specificity for
L- Ala-L-Glu, which can be achieved with at least three different
constellations of active site residues. Conversely, relatively closely
related enzymes can exhibit different specificities.
For these reasons, functional annotation can benefit signifi-
cantly from structural information, in addition to bioinformatics
analysis of protein sequences, as also shown elsewhere (1517).
By constructing homology models and docking dipeptide sub-
strates, we were able to generate specific hypotheses regarding
substrate specificities. In particular, we correctly predicted the
existence of three specificity groups, although the predictions
were clearly qualitative; e.g., cationic dipeptide epimerase. It is
not yet possible, in our hands at least, to predict the very best
substrate; e.g., L-Lys-L-Arg, which has a value for k
times larger than that of L-Lys-L-Lys for the dipeptide epimerase
from M. capsulatus. Nonetheless, because these computational
methods can be scaled to hundreds or thousands of sequences, this
approach holds the potential to help guide large-scale functional
assignment, particularly to identify enzymes likely to have novel
substrate specificities, as we have demonstrated here.
The current efforts resulted in the identification of catalytic
activities for a significant number of members of the enolase super-
family. However, more important than the discovery of these
activities is the demonstrated synergy between computational
modeling and informatics approaches, which represents an impor-
tant step towards the realization of general strategies for functional
annotation. The assignment of in vitro catalytic activities to indi-
vidual enzymes affords an entry point for defining entire metabolic
pathways. For example, in favorable cases, where interpretable
genome/operon context exists, the determination of a single cata-
lytic activity provides direct insights into the activities of proteins
encoded by physically proximal genes and may ultimately lead to
predictions regarding new metabolic pathways. In turn, these pre-
dictions offer significant cues for the design and interpretation of
experiments to define the true in vivo functions of the initially an-
notated enzyme, as well as its associated metabolic pathway. Thus,
the computational approaches described here are likely to repre-
sent an important component of the overall strategies needed to
systematically define the metabolic repertoire present in nature.
Materials and Methods
The computational and experimental methods have been described pre-
viously (9) in the context of assigning the function of one member of this
group. Detailed methods are described in SI Appendix.
Briefly, the computational predictions were generated using a multiple
sequence alignment generated by Muscle (18). Homology models were cre-
ated for all putative dipeptide epimerases with Prime (Schrodinger LLC),
using the substrate-bound structure of B. subtilis AEE (PDB ID 1TKK) as the
template. All 400 possible L/L-dipeptides were docked to the models using
Glide SP (v4.0108, Schrodinger LLC). Consensus results were generated by
analyzing the top hits (top 5%, i.e., top 20 hits) of all members of a phylo-
genetic group, characterizing the most prominent amino acids in the N-term-
inal and C-terminal positions for the dipeptides.
Network analyses were performed as previously described (11), modified
as given in SI Appendix. Cytoscape networks (19) were created from these
BLAST results at several different e-value cutoffs. Tools used for visualization
of protein networks were created by the UCSF Resource for Biocomputing,
Visualization, and Informatics and are available from the Resource (http://
www.rbvi.ucsf.edu). All of the sequences considered in this study are avail-
able in the SFLD along with additional metadata for each by searching with
the associated gi numbers, given in SI Appendix, Table S1.
ACKNOWLEDGMENTS. This research was supported by National Institutes
of Health P01 GM071790 (to P.C.B., J.A.G., M.P.J., and S.C.A.) and U54
GM094662 (to S.C.A.). Molecular grap hics images were produced using the
University of California, San Francisco Chimera package from the Resource
for Biocomputing, Visualization, and Informatics at the University of Califor-
nia, San Francisco (supported by National Institutes of Health P41 RR-01081).
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    • "Sequence similarity network analysis is a powerful and computation-economic method to depict the relationship among different protein sequences (Atkinson et al. 2009; Lukk et al. 2012; Zhao et al. 2014). In a network, each node represents a protein sequence, and each edge (line) indicates a pair of nodes (protein sequence) that have a BlastP e-value more stringent than a certain cutoff value. "
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