An NMR approach to structural proteomics
Adelinda Yee*, Xiaoqing Chang*, Antonio Pineda-Lucena*, Bin Wu*, Anthony Semesi*, Brian Le*, Theresa Ramelot†,
Gregory M. Lee‡, Sudeepa Bhattacharyya§, Pablo Gutierrez¶, Aleksej Denisov¶, Chang-Hun Lee?, John R. Cort†,
Guennadi Kozlov¶, Jack Liao*, Grzegorz Finak¶, Limin Chen*, David Wishart§, Weontae Lee?, Lawrence P. McIntosh‡,
Kalle Gehring¶, Michael A. Kennedy†, Aled M. Edwards*, and Cheryl H. Arrowsmith*,**
*Ontario Cancer Institute and Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada M5G 1L7;†Environmental
Molecular Sciences Laboratory, Pacific Northwest National Laboratory, K8-98, Richland, WA 99352;‡Departments of Biochemistry and Molecular Biology,
Chemistry, and the Biotechnology Laboratory, 2146 Health Sciences Mall, University of British Columbia, Vancouver, BC, Canada V6T 1Z3;§Faculty of
Pharmacy and Pharmaceutical Sciences, 3118 Dentistry?Pharmacy Centre, University of Alberta, Edmonton, AB, Canada T6G 2N8;¶Department of
Biochemistry and Montreal Joint Centre for Structural Biology, McGill University, 3655 Promenade Sir William Osler, Montreal, QC, Canada
H3G 1Y6; and?Department of Biochemistry and Protein Network Research Center, Yonsei University, 134 Shinchon-Dong
Seodaemoon-Gu, Seoul, Korea 120-749
Communicated by Louis Siminovitch, Mount Sinai Hospital, Toronto, Canada, December 19, 2001 (received for review September 11, 2001)
The influx of genomic sequence information has led to the concept
of structural proteomics, the determination of protein structures
on a genome-wide scale. Here we describe an approach to struc-
tural proteomics of small proteins using NMR spectroscopy. Over
500 small proteins from several organisms were cloned, expressed,
purified, and evaluated by NMR. Although there was variability
among proteomes, overall 20% of these proteins were found to be
readily amenable to NMR structure determination. NMR sample
preparation was centralized in one facility, and a distributive
approach was used for NMR data collection and analysis. Twelve
structures are reported here as part of this approach, which
allowed us to infer putative functions for several conserved hy-
major initiative within the biomedical community (see ref. 1 and
other articles in the same issue). The large number of protein
structures expected from these projects will yield valuable clues
to the rules for predicting protein folding and understanding
biochemical function. In these early stages of the structural
proteomics effort, one of the main goals is to identify the best
technologies and the most efficient processes to convert gene
sequence into 3D structural information. One of the decisions
will be to determine the optimal use of x-ray crystallography and
NMR spectroscopy, which are the two techniques that will
provide the majority of experimental data for these initiatives.
X-ray crystallography currently is perceived as the potential
workhorse for structural proteomics, because if provided with a
well diffracting crystal it is possible to determine a 3D structure
in hours. However, the throughput of structure determination
using x-ray crystallography remains unclear, because the rate-
determining step continues to be the production of well diffract-
ing crystals, a process that is unpredictable and can take between
hours and months.
NMR structure determination is limited currently by size con-
straints and lengthy data collection and analysis times (often
months), and the method is best applied to proteins smaller than
250 amino acids. On the other hand, NMR experiments do not
require crystals, and samples appropriate for structure determina-
tion can be identified within minutes of the protein being purified.
In summary, x-ray crystallography and NMR spectroscopy seem to
have complementary deficiencies, and the relative success of these
methods in structural proteomics remains to be determined.
We have shown previously that NMR spectroscopy can play a
significant role in structural proteomics even with its current
limitations (2). The initial pilot project, based on a limited
number of proteins from the thermophilic archaebacterium
Methanobacterium thermoautotrophicum (Mth) suggested that
smaller proteins may be more amenable to structure analysis,
because in this genome a higher proportion of smaller proteins
tructural proteomics, which aims to determine the three-
dimensional (3D) structures of all proteins, has become a
were soluble compared with larger proteins. However, this study
was performed on a single proteome, and it was unclear how
general these conclusions were.
Here we outline a strategy for the use of NMR spectroscopy
for structural proteomics of small proteins based on data from
513 proteins from five microorganisms. These microorganisms
include both thermophilic and mesophilic species and represen-
tatives from the prokaryotes, archaea, and eukaryotes [Esche-
richia coli (ecoli), M. thermoautotrophicum, Thermotoga mari-
tima (TM), Saccharomyces cerevisiae, and the myxoma virus
(Myx)]. We used an approach in which all proteins were cloned,
expressed, and screened for suitability for NMR analysis in a
single laboratory, and NMR data collection and structure de-
termination were distributed among several NMR laboratories.
Here we report the 3D structures of 12 proteins fully analyzed
in this manner. These proteins are conserved but mostly unan-
notated from four species ranging in size from 8.4 to 22.6 kDa.
The solution structures revealed several unusual folds as well as
hint about the type of (and frequency with which) functional
inferences that can be expected from larger scale structural
proteomics projects focused on small proteins.
Materials and Methods
Expression and Purification. The targets selected for NMR screen-
ing had the following properties: single chain polypeptide with
molecular mass under 23 kDa, no predicted transmembrane
helix using TMHMM (www.cbs.dtu.dk), and no sequence homo-
logue in the PDB database as identified by a BLAST search with
an e-value cut-off of 10?4. Targets were PCR-amplified from
genomic DNA and subcloned in 96-well format into a pET15b
(Novagen) or a modified pET15b vector with the thrombin
cleavage site replaced with a TEV protease cleavage site. These
vectors express proteins with an N-terminal hexahistidine tag
followed by a thrombin or TEV protease cleavage site.
For screening, all proteins were expressed in E. coli strain
BL21- (Gold ?DE3), and in the case of the archaeal and
eukaryote proteins the cells were cotransformed with a plasmid
encoding three transfer RNAs for rare E. coli codons. Cells were
Abbreviations: 3D, three-dimensional; Myx, myxoma virus; HSQC, heteronuclear single
quantum coherence; COG, cluster(s) of orthologous groups.
**To whom reprint requests should be addressed. E-mail: email@example.com.
Data deposition: The atomic coordinates and structure factors have been deposited in the
Protein Data bank (PDB), www. rcsb.org, and chemical shifts have been deposited in the
BioMagResBank (BMRB), www.bmrb.wisc.edu: Mth0637 (PDB, 1JRM; BMRB, 5104),
BMRB, 5165), Mth1743 (PDB, 1JSB; BMRB, 5106), Mth1880 (PDB, 1IQO; BMRB, 5129),
TM0983 (PDB, 1JDQ; BMRB, 5060), YedF_ecoli (PDB, 1JE3; BMRB, 5059), Yjbj_ecoli (PDB,
1JYG; BMRB, 5106), Hha_ecoli (PDB, 1JW2; BMRB, 5166), and Myxv156r (PDB, 1JJG; BMRB,
The publication costs of this article were defrayed in part by page charge payment. This
article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C.
§1734 solely to indicate this fact.
February 19, 2002 ?
vol. 99 ?
no. 4 ?
grown in 1 liter of M9 minimal medium containing15NH4Cl as
the sole nitrogen source and supplemented with ZnCl2, thia-
mine, and biotin. The cells were grown at 37°C to an OD600of
0.6 and induced with 1 mM isopropyl ?-D-thiogalactoside.
Afterward, the temperature was reduced to 15°C, and the cells
were allowed to grow overnight before harvesting. Frozen cell
pellets were thawed in 500 mM NaCl?20 mM Tris?5 mM
imidazole (pH 8) and lysed by sonication. The proteins were
extracted from the lysates by batch Ni2?affinity chromatography
(Qiagen). The Ni2?affinity beads were washed three times with
5 column volumes of 500 mM NaCl?20 mM Tris?30 mM
imidazole (pH 8), and the proteins were eluted with 5 column
volumes of 500 mM imidazole in this same buffer. The purified
proteins were concentrated, and buffer was exchanged by ultra-
filtration and dilution?reconcentration. The final ‘‘generic’’
NMR buffer comprised 450 mM NaCl?25 mM Na2PO4?10 mM
DTT?20 ?M Zn2??1 mM benzamidine?1? inhibitor mixture
(Roche Molecular Biochemicals)?0.01% NaN3(pH 6.5).
The proteins selected for structure determination generally
and [13C6]glucose purified as described above plus a further
chromatography step using either SP Sepharose or DEAE
Sepharose (Amersham Pharmacia) ion-exchange columns. In
some cases, the (His)6 tag was cleaved by incubation with
thrombin or TEV and removed by using a Ni2?affinity column.
When necessary, proteins were passed through an additional
benzamidine Sepharose (Amersham Pharmacia) column to re-
move the thrombin.
NMR Spectroscopic Screening. All1H–15N heteronuclear single
quantum coherence (HSQC) spectra were acquired at 25°C by
using a Varian INOVA 500- or 600-MHz spectrometer equipped
with a pulse-field gradient unit and actively shielded z-gradient
triple resonance probes. The total number of t1 increments was
64, with 8–64 scans per increment depending on the concentra-
tion of the sample being screened. The data were processed by
using the NMRPIPE software package (3).
NMR Structure Determination. Twelve of the proteins that were
deemed suitable for structure determination were distributed
among six NMR laboratories. The proteins were labeled uni-
formly with13C and15N, resonances were assigned by using
conventional triple resonance techniques, and structures were
calculated by using distance and dihedral angle constraints
derived from nuclear Overhauser effects and coupling constants.
The choice of NMR experiments varied between laboratories
and is documented in Figs. 4 and 5, which are published as
supporting information on the PNAS web site, www.pnas.org.
The total acquisition time varied between 204 and 1,390 h. On
average, the acquisition times were 200 h for a suite of experi-
ments for backbone resonance assignments, 202 h for side chain
Results and Discussion
Protein Production and Screening by15N HSQC. To screen as many
proteins as possible, a total of 513 ORFs were expressed,
purified, and examined by NMR under identical conditions.
Most proteins (85%) expressed well in E. coli grown in minimal
medium, and 68% of the expressed proteins remained in the
soluble fraction of the cell lysate (Fig. 1). Thus, over half the
ORFs chosen in this study could be expressed in soluble form in
We selected the proteins best suited for NMR structure
determination by employing a rapid batch purification of poly-
ing’’ of labeled proteins by1H–15N HSQC spectroscopy. The
HSQC spectrum provides a diagnostic fingerprint of a protein.
One peak is expected for each nonproline residue.
HSQC spectra of the soluble proteins could be classified as good,
promising, poor, or mostly unfolded. The ‘‘good’’ spectra showed
dispersion of peaks with roughly equal intensity and in the
number expected from the sequence of the protein. These
spectra indicated that the protein was amenable to structure
determination by NMR methods. Approximately 33% of the
soluble proteins, or 19% of the total, gave HSQC spectra that
could be classified as good (Fig. 2). ‘‘Promising’’ spectra showed
well dispersed peaks but were either too few or too many in
number and were often of differing intensities. Such spectral
features are indicative of conformational heterogeneity with
slow or nonexistent interconversion between states (too many
peaks) or the presence of dynamic processes on an intermediate
time scale that can broaden and obscure the NMR signals. The
behavior of these proteins sometimes can be optimized by
changing either the protein construct or the solution conditions.
Between 11 and 33% of soluble proteins in each proteome gave
HSQC spectra that showed very little peak dispersion were
classified as either ‘‘mostly unfolded’’ or ‘‘poor.’’ Mostly un-
folded proteins showed many very sharp, intense peaks with
random coil chemical shifts. Less than 10% of the soluble
proteins fall under this classification. The poor HSQC spectra
were characterized by a cluster of broad peaks with little
dispersion in the center of the spectrum. Typically these spectra
did not have the requisite number of peaks for the size of protein
examined and likely reflect aggregated and?or conformationally
unstable proteins that may be partially unfolded or that inter-
convert between multiple conformations. Approximately 27–
55% of the soluble proteins exhibited these properties. Large,
that could be classified as poor. The remaining 10–25% of the
soluble proteins precipitated during concentration and could not
be subjected to further NMR analysis.
Comparison Between Species. One of the goals of this work was to
determine whether there are trends among species for overall
protein ‘‘behavior’’ to facilitate target selection for structural
and soluble (gray) from each organism.
Histogram of the number of proteins cloned (blue), expressed (red),
www.pnas.org?cgi?doi?10.1073?pnas.042684599Yee et al.
proteomics projects. We therefore compared the expression,
solubility, and HSQC results for the five species studied here.
Among the proteomes, T. maritima proteins showed the largest
proportion of soluble proteins (95%), followed by S. cerevisiae
(63%), E. coli (61%), M. thermoautotrophicum (51%), and Myx
(46%), respectively. Similar trends between organisms were
observed for classification of HSQC spectra; 33% of T. maritima
proteins had good or promising spectra followed by E. coli
(26%), M. thermoautotrophicum (25%), S. cerevisiae (24%), and
Myx (11.5%). In an effort to better understand whether the
relative amino acid content of proteins in these organisms was
correlated with their suitablilty for NMR analysis, we compared
the percentages of each amino acid content within four broad
classes of protein behaviors: insoluble proteins, good?promising
HSQC, poor or no HSQC, and unfolded proteins. These per-
centages were compared also with those for proteins deposited
in the PDB to see whether, for example, the amino acid content
of proteins with good?promising HSQCs more closely matched
that of proteins in the PDB compared with insoluble proteins
acid composition either among genomes or between different
classes of protein behavior. The most significant differences in
amino acid content correlated with the difference between
thermophilic and mesophilic organisms and not necessarily with
protein solubility or HSQC classification.
Among the prokaryotes, it appears that proteins from the
thermophilic organism T. maritima had better-behaved proteins
under our growth and lysis conditions. However, this conclusion
could be biased by the relatively small number of proteins that
we sampled from T. maritima (only 21 compared with 130 from
E. coli). Surprisingly, compared with T. maritima, a similar
proportion of soluble proteins from E. coli, M. thermoautotro-
phicum, and S. cerevisiae gave good?promising HSQCs. This
result may suggest that the thermophilic properties of M. ther-
moautotrophicum proteins do not provide a significant advan-
tage for structural proteomics of small proteins by using NMR.
Proteins from Myx showed a particularly low percentage of well
behaved proteins under our generic conditions. This poor be-
havior may reflect the fact that most viral proteins have evolved
to interact with proteins in the host cell and may not be stable
when expressed in isolation in E. coli.
Selection of Screening Conditions. There are several published
methods to assess the suitability of a protein for NMR structure
determination rapidly. In these methods, for example the15N-
pulse labeling (4) and in vivo15N HSQC (5), the suitability for
structure determination is estimated by the behavior of the
impure protein in extracts or cells. Our method of using generic
expression, purification, and final NMR solution condition also
and efficient manner. We elected to pursue this generic method,
because our earlier studies indicate that only a small portion of
the proteins that are soluble at lower concentration in the lysate
or cells can be purified and concentrated. Therefore, many
proteins that may have given good HSQC spectra in an in vivo
screening protocol may not necessarily have been amenable to
in vitro structure determination. However, in the future, with the
advent of improved sensitivity from cryogenic probes, it may not
be necessary to concentrate proteins as much in order collect
NMR data for structure determination, and the in vivo screening
strategy may be more appropriate.
Structural Results. The solution structures for the 12 proteins we
report here provided insight into the functional information that
will be expected from NMR-based structural proteomics and
also enabled us to assess and optimize our distributive data
collection strategy (Fig. 3 and Table 1). The majority of these
proteins (10 of 12) were ‘‘conserved’’ or ‘‘hypothetical’’ proteins
with no functional annotation. Most (8 of 12) also were members
of uncharacterized protein superfamilies or clusters of ortholo-
gous groups (COG). Although several of the superfamilies?
COG had members that were distantly related to proteins of
each of the proteins chosen here had a sufficiently unique
sequence that a good structure alignment could not be estab-
lished with a protein of known 3D structure. The sequences were
submitted to SwissModeler (7, 8) to determine whether a 3D
structure could be predicted based on sequence similarity. In
(d) T. maritima (T. ma.); (e) Myx. Spectra are classified as good (blue), promising (red), mostly unfolded (gray), poor (purple), and those for which no HSQC could
be obtained because of a loss of protein during the concentration procedure (green).
Distribution chart of the HSQC classifications for soluble, purifiable proteins. (a) M. thermoautotrophicum (M. Th.); (b) E. coli; (c) S. cerevisiae (S. ce.);
Yee et al.
February 19, 2002 ?
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in the1H dimension (x axis) and from 107 to 133 ppm in the15N dimension (y axis). The number of residues for each protein is indicated on the HSQC
C-terminal residues 198–208 of Mth1692 are unstructured and not shown. All structure diagrams were created by using the MOLAUTO program within
15N HSQC spectra and the backbone ribbon representations of the 12 structures presented in this paper. All HSQCs are plotted from 6.0–10.5 ppm
www.pnas.org?cgi?doi?10.1073?pnas.042684599Yee et al.
only two cases (Mth1692 and YedF_ecoli) was a structure
prediction returned. In these cases the sequence alignment was
based on a 27.4 and 30.6% sequence identity between Mth1692
and Yrdc_ecoli, and YedF_ecoli with YhhP_ecoli, respectively.
The predicted models had an rms deviation between backbone
atoms compared with the experimental NMR structures of 4.4
and 5.8 Å for Mth1692 and YedF_ecoli, respectively. Thus, these
12 structures represent 3D structures and form the basis for
modeling of up to 87 additional homologous proteins from all
Although none of the 12 proteins had a novel fold, several
were novel variants of known folds. Most structures could be
classified as being structurally similar to other structures in the
of Mth0637, which has a fold resembling that of translation
initiation factor 1 (PDB ID code 2IF1), a two-layered ??? fold.
However, Mth0637 has an insert of a two-stranded ?-sheet in the
middle of the sequence that packs orthogonally to the main
?-sheet. Mth0637 also has a similar architecture (from the
CATH classification system (11, 12) to that of a PDZ domain
(PDB ID code 1Q1C) with the N-terminal ?-strand of Mth0637
taking the place of the C-terminal strand in the PDZ domain.
TM0983 and YedF_ecoli (which are members of the same COG)
formed a two-layered ??? fold that is structurally homologous to
translation initiation factor 3 (PDB ID code 1TIG). Myxv156r
has a ?-barrel fold found in S1 RNA-binding domains and
cold-shock domain (13). Myxv156r was annotated as an IFN-
resistance eIF2? homolog, and the structure is very similar to
that predicted for the N-terminal domain of eukaryotic eIF2?.
Hha_ecoli is annotated as a hemolysin modulating protein and
belongs to a superfamily of transcriptional regulatory and DNA-
binding proteins. The structure of Hha_ecoli reveals a helix–
turn–helix motif typical of many other bacterial DNA-binding
proteins. Mth1598 has structural homology to heat-shock pro-
tein 33, which acts as a chaperonin by inhibiting aggregation of
partially denatured proteins (14). Mth1598 also has structural
homology at the architecture level to S8 ribosomal protein (PDB
ID code IFKA-H).
One of the goals of this research is to use structural homology
to help reveal functional clues to previously unannotated pro-
teins. Of the 12 structures solved here, nine proteins have
significant structural similarity to a protein of known biochem-
ical function. For the four proteins with initiation factor-like
folds mentioned above, a possible RNA-binding function may be
suspected. However, none of the four structures determined
here had the key surface features of their structurally homolo-
gous RNA-binding domains, namely a large basic surface com-
bined with several surface-exposed aromatic residues. Interest-
ingly, in the case of Myx156r, the structural similarity to the
predicted structure of the eIF2? N-terminal domain combined
with the positions of conserved surface residues suggests that
Myx156r and its viral homologues may be structural mimics of
eIF2? and compete with eIF2? for regulators such as IFN-
induced protein kinase PKR. Mth1692 has sequence and struc-
tural homology to yrdC_ecoli, which was shown to have high
affinity for double-stranded RNA (15). The structure of
Mth1692 shows a large positively charged cavity, also found in
yrdC_ecoli, that is suspected to be the RNA-binding site.
Mth0865 and Mth1743 are members of COG that are distantly
related to the thioredoxin and the ThiS?Ubiquitin families of
proteins, respectively. Although the level of sequence identity
with thioredoxin or ThiS is insignificant, Mth0865 and Mth1743
Table 1. Sequence and structural characteristics of 12 structural proteomics targets
homologues*Fold classStructural homologues†‡
with 2 helices
Helix bundle with
??? Sandwich Heat-shock protein 33
Mth1743CHP Ubiquitin (1UBI)C-terminal
factor 3 (1TIG)
PNPase fragment (1SR0)
C8, K3L viral
*Superfamily and COG numbers were obtained from the Integrated Protein Classification database (pir.georgetown.edu?iproclass).
†Coordinates were submitted to DALI server (www2.ebi.ac.uk?dali) and structures with Z values larger than 2.0 were examined visually for similarity. Only a
representative structural homologue is listed.
‡The PDB accession numbers for the structural homologues are indicated in parentheses.
§Conserved hypothetical protein.
¶Structural homology was observed from manual search of the CATH protein structure classification (www.biochem.ucl.ac.uk).
Yee et al.
February 19, 2002 ?
vol. 99 ?
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indeed have folds similar to these two proteins, respectively, and Download full-text
likely have similar but not identical biochemical activities. The
putative function of the hypothetical protein Mth1880 was
deduced not from 3D structure but from sequence homology.
The second helix of Mth1880 has sequence homology to a helical
region in syntaxin 1A involved in the calcium-dependent binding
of syntaxin 1A to synaptotagmin (16). Mth1880 has been shown
to bind calcium (G.M.L. and W.L., unpublished data), although
at this point the binding partner of Mth1880 is unknown. The
scaffolds that hold the homologous helices of syntaxin 1A and
Mth1880 are very different even though the sequence homology
between the two proteins extends beyond the active helix.
Here we have demonstrated the feasibility of an NMR approach
to structural proteomics of small proteins. Our strategy relies on
a centralized site for protein preparation and initial character-
ization followed by a distributive mechanism for NMR data
collection and analysis. During this year-long project we iden-
tified several key ‘‘bottlenecks’’ in the process which, if elimi-
we have confirmed protein insolubility and?or aggregation as a
major hindrance in structural proteomics. Our generic purifica-
tion protocol may need to be expanded to explore 2–3 additional
buffer conditions to allow more proteins to be concentrated for
structural analysis. This problem in sample preparation applies
to both x-ray crystallography and NMR spectroscopy. The use of
higher-sensitivity cryogenic NMR probes will allow data collec-
tion for less concentrated samples, which should also help
diminish the aggregation problem.
A second bottleneck for NMR-based structural proteomics is
the amount of time required for data collection, currently ?3–4
weeks per protein. Cryogenic probes will certainly assist in
reducing this bottleneck. The third bottleneck is the NMR data
analysis and structure determination phase. Although it cur-
rently takes on the order of months to assign NMR resonances
and determine a structure, progress is being made on several
fronts. Automated resonance- and nuclear Overhauser effect-
assignment programs such as AUTOASSIGN (18), ANSIG (19),
TATAPRO (20), ARIA (21), NOAH (22), and SANE (23) hold great
promise in reducing the amount of time in structure determi-
nation; however, all these algorithms rely on frequency lists that
often need manual peak-picking editing of lists generated by
automatic peak-picking routines. Therefore, developing a more
robust peak-picking algorithm that can better distinguish noise
and artifacts from real peaks is crucial. Such a program com-
bined with better data, both in terms of resolution and signal-
to-noise, will enhance the reliability of automated and semiau-
tomated procedures greatly. The use of programs such as RFAC
(24) and MADIGRAS (25, 26) to back calculate the nuclear
Overhauser effect spectroscopy spectra for comparison with the
experimental spectra will reduce the need for manual validation
of the calculated structure by measuring an R factor akin to that
although the generation of such data requires additional effort
in sample preparation to identify appropriate orienting media.
Thus, we believe that a feasible and economical strategy for
NMR-based structural proteomics would consist of two key com-
ponents. The first is one or more centralized sample-preparation
facilities that can ridentify the most appropriate sample conditions
for modern NMR analysis rapidly and economically (see for
example www.uhnres.utoronto.ca?proteomics and www.nesg.org).
The second component is a consortium of NMR laboratories to
which previously validated isotope-labeled samples would be sent
for rapid acquisition of high signal-to-noise NMR experiments
appropriate for automated or semiautomated data analysis.
We thank Dr. Grant McFadden for a gift of Myx genomic DNA, and
Anna Kachatryan, Akil Dharamsi, Jun Gu, Alexei Savchenko, and
Dinesh Christendat for excellent technical assistance and PCR cloning.
This research was supported by the Ontario Research and Development
Challenge Fund (A.M.E.), Canadian Institutes for Health Research
(C.H.A, A.M.E, and K.G.), Protein Engineering Network of Centres of
Excellence (L.P.M.), National Institutes of Health Structural Genomics
Center Grant P50 GM62413-02 (C.H.A., A.M.E., and M.A.K.), and US
Department of Energy contracts DE-AC06-76RL01830 (to M.A.K) and
DE-FG06-92RL-12451 (to J.R.C.), Pacific Northwest National Labora-
tory Director’s Research and Development funds (to M.A.K.), and
Korea Science and Engineering Foundation through Protein Network
Research Centre (W.L.). Part of the NMR work was performed at
facility sponsored by Department of Energy Biological and Environ-
mental Research) located at Pacific Northwest National Laboratory and
operated by Battelle. C.H.A., A.M.E., and M.A.K. are members of the
Northeast Structural Genomics Consortium. A.M.E. and C.H.A. are
Canadian Institutes for Health Research Scientists.
1. Smith, T. (2000) Nat. Struct. Biol. Suppl. 7, 927.
2. Christendat, D., Yee, A., Dharamsi, A., Kluger, Y., Savchenko, A., Cort, J. R.,
Booth, V., Mackereth, C. D., Saridakis, V., Ekiel, I., et al. (2000) Nat. Struct.
Biol. 7, 903–909.
3. Delaglio, F., Grzesiek, S., Vuister, G. W., Zhu, G., Pfeifer, J. & Bax, A. (1995)
J. Biomol. NMR 6, 277–293.
4. Gronenborn, A. M. & Clore, G. M. (1996) Protein Sci. 5, 174–177.
5. Serber, Z., Keatinge-Clay, A. T., Ledwidge, R., Kelly, A. E., Miller, S. M. &
Dotsch, V. (2001) J. Am. Chem. Soc. 123, 2446–2447.
6. Begley, T., Xi, J., Kinsland, C., Taylor, S. & McLafferty, F. (1999) Curr. Opin.
Chem. Biol. 3, 623–629.
7. Peitsch, M. C. (1996) Biochem. Soc. Trans. 24, 274–279.
8. Peitsch, M. C., Schwede, T. & Guex, N. (2000) Pharmacogenomics 1, 257–266.
9. Holm, L. & Sander, C. (1993) J. Mol. Biol. 233, 123–138.
10. Holm, L. & Sander, C. (1996) Science 273, 595–602.
11. Orengo, C. A., Michie, A. D., Jones, S., Jones, D. T., Swindells, M. B. &
Thornton, J. M. (1997) Structure (London) 5, 1093–1108.
12. Pearl, F. M. G., Lee, D., Bray, J. E., Sillitoe, I., Todd, A. E., Harrison, A. P.,
Thornton, J. M. & Orengo, C. A. (2000) Nucleic Acids Res. 28, 277–282.
13. Bycroft, M., Hubbard, T., Proctor, M., Freund, S. & Murzin, A. (1997) Cell 88,
15. Teplova, M., Tereshko, V., Sanishvili, R., Joachimiak, A., Bushueva, T.,
Anderson, W. & Egli, M. (2000) Protein Sci. 9, 2557–2566.
16. Fernandez, I., Ubach, J., Dulubova, I., Zhang, X., Sudhof, T. & Rizo, J. (1998)
Cell 94, 841–849.
17. Kraulis, P. (1991) J. Appl. Crystallogr. 24, 946–950.
18. Zimmerman, D., Kulikowski, C., Huang, Y., Feng, W., Tashiro, M., Shimo-
takahara, S., Chien, C., Powers, R. & Montelione, G. (1997) J. Mol. Biol 269,
19. Helgstrand, M., Kraulis, P., Allard, P. & Hard, T. (2000) J. Biomol. NMR 18,
20. Atreya, H., Sahu, S., Chary, K. & Govil, G. (2000) J. Biomol. NMR 17, 125–136.
21. Nilges, M. & O’Donoghue, S. (1998) Prog. Nucl. Magn. Reson. Spectrosc. 32,
22. Mumenthaler, C. & Braun, W. (1995) J. Mol. Biol. 254, 465–480.
23. Duggan, B. M., Legge, G. B., Dyson, J. & Wright, P. E. (2001) J. Biomol. NMR
24. Gronwald, W., Kirchhofer, R., Gorler, A., Kremer, W., Ganslmeier, B., Neidig,
K. & Kalbitzer, H. (2000) J. Biomol. NMR 17, 137–151.
25. James, T. L. (1991) Curr. Opin. Struct. Biol. 1, 1042–1053.
26. Borgias, B. A., Gochin, M., Kerwood, D. J. & James, T. L. (1990) Prog. Nucl.
Magn. Reson. Spectrosc. 22, 83–100.
27. Brunner, E. (2001) Concepts Magn. Reson. 13238–259.
28. Fowler, C., Tian, F., Al-Hashimi, H. & Prestegard, J. (2000) J. Mol. Biol. 304,
29. Gerstein, M. (1998) Folding Des. 3, 497–512.
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