From the
The use of short oligonucleotide probes is finding increased
application in DNA sequencing and genome characterization techniques,
but a lack of knowledge of the hybridization properties of short
duplexes hinders their use. Melting data were acquired on 128 DNA
duplexes based on the length proposed in sequencing by hybridization
procedures and formed from the general sequences
5`- XYZTGGAC-3`, 5`-GTCCA XYZ-3`,
5`-GC XYZGAC-3`, and 5`-GTC XYZGC-3` where X,
Y, and Z are either A, T, G, or C. These molecules
were designed to elucidate the effects of location and nearest-neighbor
stacking on the stability of base pairing in short DNA duplexes. The
type of base pairs present had a major effect on stability, but was
insufficient to predict stability without the inclusion of
nearest-neighbor terms. Furthermore, the addition of information on
position, or distance from the end, of the nearest-neighbor doublets
led to statistically better fitting of the melting data. However, the
positionally dependent stabilization differences are small compared
with the contributions of base pairing and stacking.
DNA analysis of the level of entire genomes and the scale of
entire populations is being considered. Such a task requires the
advancement of technologies, through the understanding of properties of
nucleic acids and the optimization of procedures. Frequently, these
technologies exploit the specific base pairing properties of DNA for
sequence information and genome localization, with shorter probes
finding increased application. Shorter oligonucleotide probes are
attractive since complete probe sets are possible to obtain and
implement. For example, only 4096 sequences compose the set of all
hexamer oligonucleotides, while >10
Component sequences can be used in a reverse fashion to
decipher unknown sequences as suggested in the technique of sequencing
by hybridization
(8, 9, 10, 11, 12) . In this
application, either an unknown DNA sequence is immobilized and
interrogated by hybridization of short known sequences, or a complete
or partial array of these sequences is immobilized and allowed to
hybridize with the target DNA. The unknown sequence is then deduced by
the overlapping component sequences. Applications in DNA sequencing,
mapping, and diagnostic testing are being demonstrated with promises of
greater efficiency over present methods
(8, 10, 12, 13, 14, 15, 16) .
Although the technology to synthesize the necessary library of
probes is available, the use of these libraries is still hindered by a
lack of information on the hybridization properties of short
oligonucleotides. General rules, which account for hybrid stability
based solely on base pair type and solution environment, may not be
sufficient since local sequence dependences are no longer averaged out
as in longer sequences. Many data sets have been collected to interpret
sequence-dependent stability, but disagreement among these sets, due to
the different types of molecules studied and conditions employed, warns
against extrapolating the data for use on shorter probes
(17, 18, 19, 20, 21, 22, 23) .
There is poor understanding of the limitations on implementation of
short probes, and models for interpretation of resultant data are
imperfect.
For optimal use of short probes, a characterization of
the various interactions that contribute to the hybrid stability is
necessary. These interactions may include Watson-Crick base pair
stability, mismatched base pair stability, nearest-neighbor stacking,
and dangling-end stability, all of which may further depend on solution
conditions as well as the relative position of these interactions in a
short duplex. The positional dependence results from the preference of
melting to initiate from the ends of the duplex. This is commonly
referred to as end fraying or end effects and can propagate several
base pairs into the duplex. Data from nuclear magnetic resonance
experiments show a perturbation of up to 3 base pairs from the end of a
duplex
(24) . For a long DNA duplex (>100 base pairs), this
may be inconsequential to the total thermal stability, but for shorter
duplexes, a significant fraction of the total number of base pairs may
be perturbed. For 18- to 20-base oligonucleotides, frequently used in
priming polymerase reactions, 30% of the base pairs may possess
different interaction energies and perhaps even reduced sequence
specificity. For octadeoxyribonucleotide duplexes, more than half the
base pairs are affected.
The characterization of the position and
sequence-dependent stability of the Watson-Crick base pairs and the
nearest-neighbor stacking interactions will be useful for the proper
design, implementation, and interpretation of sequencing by
hybridization chips. Furthermore, an understanding of the energetics of
end base pairs will aid in understanding polymerase priming and probe
specificity as well as other applications that utilize short DNA
probes. To this end, we have undertaken optical melting experiments
involving a set of 256 octadeoxyribonucleotides to evaluate the
positional- and sequence-dependent stability of short duplexes in
solution. This molecule set is subdivided into two groups referred to
as the ``end set'' and ``middle set.'' The end set
molecules are of the general sequences 5`- XYZTGGAC-3` and
5`-GTCCA XYZ-3`, where X, Y, and Z may be A, T, G, or C. The end set molecules allow the formation of
64 perfectly matching duplexes with every base pair type in every
nearest-neighbor environment occurring in the end and penultimate
positions. Similarly, the middle set is composed of the sequences
5`-GC XYZGAC-3` and 5`-GTC XYZGC-3` to form 64
perfectly matching duplexes to evaluate the sequence-dependent
stability of internal base pairs.
Samples were prepared for melting by combining
aliquots of the appropriate single strands from concentrated stock
solutions (
The
absorbance versus temperature curves were normalized by
fitting base lines to the nearly linear regions before and after the
steeply sloped transition region and by taking the product of the
difference between the absorbance and the lower base line and the
absorbance difference between the base lines at all data points. The
resulting normalized curve was smoothed once with a sliding boxcar
filter, and the first derivative was taken to identify the melting
temperature and peak height.
The melting
temperatures and peak heights derived from the melting curves were used
to calculate free energies using the van't Hoff method as
described by Marky and Breslauer
(25) . The standard error
associated with the determination of
The preliminary analysis of T
The
melting temperature values in are the result of a total of
over 1000 melting curves collected over a period of several months.
Samples of a particular duplex sequence were prepared and melted on
different days and in random spectrophotometer cuvette positions to
reduce any procedural and instrumental bias as well as changes in
buffer preparation and temperature probes. Errors due to slight
concentration differences, derived from the assumption of a constant
extinction coefficient, as well as minor variabilities in the purity of
the different oligonucleotides are also recognized. However,
concentration differences of a few percent, for a particular sample,
did not lead to any consistent trends in the melting temperature. A
nested-effects analysis of variance procedure
(26) was used to
assess the error associated with measurements collected on a particular
day as well as variability from day to day. The measurement to
measurement variability ( i.e. within a particular day or
experimental session) was observed to fluctuate with time such that the
calculation of a standard error for each duplex was less meaningful.
This fluctuation was presumably due to instrumental problems related to
temperature control or accuracy of measurement. We adopted the
relatively conservative approach of using the greatest (over time)
estimate of the standard deviation associated with individual
measurements. Using this, the (again, conservative) estimate of
standard error for mean T
An example of the error associated with the data set and
the necessity for careful statistical analysis can be seen upon the
respective comparison of the melting temperatures of the end molecule
set sequences 45, 46, 47, and 48 with the middle set molecule sequences
7, 23, 39, and 55. These sequences are common to the two sets, with the
samples derived from separate syntheses and experimental data from
separate experiments. The differences in experimental melting
temperatures are 0.7, 1.4, 1.7, and 0.3 °C for the respective
comparisons with a root mean square error (RMSE)
Also shown in is the calculated
change in free energy at 25 °C based on the all-or-none model as
described by Marky and Breslauer
(25) . The values are shown for
comparison purposes, but it is recognized that they are prone to the
shortcomings of the two-state model as well as the calculational
methods from which they are derived. The calculational method involves
the fitting of base lines for curve normalization, which strongly
influences the assessment of cooperativity and apparent transition
energy. The transition midpoint is less affected and does reflect more
directly the relative differences among the various duplex stabilities.
The low melting temperature of some of the duplexes prevents
establishment of the lower base line. The low melting temperatures,
however, do allow ample data collection after the transition, where
evidence for melting that deviates from the two-state model was
apparent. The high temperature regions of the molecules with lower
melting temperatures showed an increased leveling off of the absorbance
at temperatures above
Different
thermostability models of increasing complexity were assumed and
applied by tallying the counts of the individual terms and evaluating
the components as described by Doktycz et al. (17) .
The adequacy of each model was then estimated by the goodness of fit to
the data set. The simplest models applied to the data in
involved the stability of singlet interactions. The initial
model (BP1) assumes that stability is dependent solely on the number of
the 8 base pairs that are either A
A brief description of
these singlet interaction models is summarized in along
with the number of fitting parameters, i.e. the number of
degrees of freedom associated with the fitted model in the analysis of
variance, the number of uniquely estimable fitting parameters, and the
root mean square error resulting from fits of the various models to the
combined end and middle set data. Also shown are two sets of
significance values ( p-values) for F-tests of goodness of fit.
The p-value is the probability of obtaining the observed
arrangement of data, or an arrangement even more extreme, under the
assumption that the given model is correct. Hence, small values
indicate lack of fit, while larger ones do not. The first
p-value assesses the model fit considering a standard error of
0.4 °C for the T
Models that extend
beyond the base pair model and that account for sequence dependence by
considering the nearest-neighbor stacking interactions were also
considered. These nearest-neighbor models include the terms described
above for BP1, but also account for the 10 double-stranded base
stacking interactions by assuming no positional dependence (NN1), an
end versus internal positional dependence (NN2), or complete
positional dependence (NN3). The general model used for characterizing
these doublet interactions is similar to those described by Doktycz
et al. (17) and Goldstein and Benight
(27) ,
but different from the nearest-neighbor model described by Breslauer
et al. (22) in that the singlet and doublet
information is separated rather than combined. The number of
interactions and resultant parameters are greatly increased with the
nearest-neighbor models. However, the linear dependences between the
nearest-neighbor stacking interactions reduce the number of unique
interactions that are assessable. The number of interactions and the
number of assessable interactions, along with the resultant RMSE values
and associated p-values for each of the nearest-neighbor
models, are summarized in I.
The additional
interactions show clear improvements in fitting the data set. The
incorporation of just nearest-neighbor information reduces the RMSE
nearly by half compared with the base pair models. The addition of
nearest-neighbor terms is more important than the addition of base pair
positional information. Furthermore, positional information on the
nearest-neighbor terms is also significant in reducing the RMSE. The
NN3 model fits the melting data significantly better than the NN2
model, which was found to fit only marginally better than the NN1 model
as judged by F-tests, which compare the size of residual for the two
models. The smaller improvement on going from the NN1 model to the NN2
model may be due to positional information on the singlet interactions
that arise from the linear dependences among the fitting parameters.
Each non-end singlet interaction is related to doublet information
resulting from base pairs on either side, while the end base pairs are
related to only one doublet interaction. This singlet and doublet
information, along with the linear dependences between these terms,
leads to inherent information regarding the end singlet interactions.
Despite the reduced RMSE values of the doublet models over the
singlet models, there is statistically significant lack of fit in these
models when residuals are compared with the standard errors constructed
from variation in the data as indicated by the first set of
p-values given in I. These p-values may
be reflective of poor fits due to information that is not explicit in
the models, or they may be due to errors associated with the data. When
the errors apparent from the independent T
Fitting the models to the data sets
separately shows better fits to the end set molecules than to the
middle and combined sequence sets. The RMSE for the base pair models
fit to the end set data alone is 1.4 °C compared with 2.1 °C
for the middle set data and 1.9 °C for the combined set data.
Likewise, the RMSE resulting from fits of the NN3 model is 0.5 °C
for the end set data versus 1.0 °C for the middle set data
alone and 0.8 °C for the combined set data. As noted above,
measurement error (within a session) clearly changed with time during
the
The RMSE of 1.8 °C, resulting from fitting the NN1
model to the validation set sequences, is somewhat higher than the RMSE
of the model fit to the combined end and middle set data. This higher
value may be due in part to the lower number of sequences from which
the RMSE is obtained. However, the differences between the predicted
and observed values are generally within the range of residuals
resulting from model prediction of the end and middle set molecules.
This can be taken as justification for use of the NN1 model as fitted
from the end and middle sets in that no substantial inadequacies are
revealed by the validation set. This observation is reinforced by the
fact that inclusion of the validation set data for model fitting did
not materially change the RMSE.
The values in were used
to predict the T
The values listed in can be
used to predict the melting temperature of any 8-base pair duplex under
the limitations of experimental conditions and errors associated with
the present experiments and should be useful in assessing the relative
stability of octadeoxyribonucleotide probes as used, for example, in
sequencing by hybridization procedures. Altered salt conditions and DNA
strand concentrations could be adjusted for (see Ref. 28) provided
there is no sequence dependence associated with the nucleation energy
or counterion stabilization. Caution should be exercised, however, when
applying the values to longer DNA sequences or structures that are not
part of the model set. Furthermore, restrictions resulting from the
relatively lower melting temperatures of the molecules studied, such as
the stability associated with the single-strand state as well as other
deviations from two-state behavior, should be considered. The thorough
characterization of other sequence-dependent interactions such as
mispairing and unpaired dangling ends will further aid in the
understanding and use of short DNA probes.
Valuable contributions from Michael T. Lipcan III
(Vasser College; a participant in Student Research Participation
administered through the Oak Ridge Institute for Science and Education)
are gratefully acknowledged.
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS AND DISCUSSION
FOOTNOTES
ACKNOWLEDGEMENTS
REFERENCES
make up the
complete set of 18-mers. The synthesis of thousands to hundreds of
thousands of oligonucleotides is currently possible using commercial
DNA synthesizers or the new parallel synthesis approaches
(1) ,
respectively. The use of shorter probes takes advantage of the fact
that shorter sequences are a subset of longer sequences. For instance,
a particular 18-mer can be obtained by combination of three hexamers.
This strategy is being exploited by using either enzymatic ligation
(2, 3) or continuous stacking
(4, 5, 6, 7) of the shorter oligomers
to produce sequencing primers that may make primer walking strategies
practical.
Sample Preparation
Oligonucleotides were
synthesized by Genosys Biotechnologies, Inc. (The Woodlands, TX) on a
0.2 µ
M scale and were desalted. All oligonucleotides were
assessed for failure sequences by electrophoresis on denaturing 15%
polyacrylamide gels followed by staining with Stains-all. The sequences
were determined to be >90% pure and were used with no further
purification.
1 m
M) of the oligonucleotides with 1 ml of
melting buffer (10 m
M sodium phosphate, pH 7.0, 1.0
M sodium chloride) for a final concentration of 2 µ
M each strand. The oligonucleotide concentrations were determined
from the absorbance at 260 nm assuming an extinction coefficient of 1
10
M
cm
. Samples were then heated in a boiling
water bath and allowed to cool slowly to room temperature. They were
then transferred to cuvettes, covered with mineral oil, capped, and
then cooled to 1 °C in the spectrophotometer.
Optical Melting Experiments
Melts were performed
using a Varian Cary 1E spectrophotometer fitted with a 12-position
thermoelectrically controlled sample holder and motorized sample stage,
which allowed the simultaneous analysis of six sample and reference
pairs. Temperature ramps were performed from 1 to 70 °C at a rate
of 0.5 °C/min. Data were collected at 0.2 °C intervals while
monitoring the temperature by a probe inserted into one of the
cuvettes. Temperature differences among the 12 cuvettes were within 0.1
°C. Denaturation and renaturation experiments were performed
consecutively and repeated for a total of four ramps/experiment. A
minimum of three samples of a particular duplex was examined.
Data Analysis
Melting temperatures and peak
heights resulting from the individual ramps of experiments on a
particular sample were collected and used to assess the variability
within experimental sessions. This variability was further assessed by
comparing the variability among the sessions as a function of the
different sessions using a nested analysis of variance:
T( i, j, k)
= DNA ( i) + session ( i, j) +
measurement ( i, j, k), where
T
is the observed melting temperature (or
alternatively, the measured peak height for calculation of
G
) of the ith molecule during the
kth measurement of the jth session. The variance was
determined from the 128 duplex molecules, analyzed over 383 sessions,
and included 1084 measurements. This analysis of standard variance
yielded conservative estimates of 0.4 and 0.0015 for the standard error
of the mean of all T
and peak height
measurements, respectively, for a given molecule.
G
is
0.12 kcal/mol.
and
G
values, using the measurement
model stated above, was based on a nested-effects analysis of variance
as described
(26) . Calculations for this analysis were
accomplished using a FORTRAN program written specifically for the data
set. Subsequent analyses were performed on average values (per octamer
pair) of T
and
G
, rather than individual measurements, and
are based on general regression and analysis of variance procedures
accomplished through the use of the GLM procedure of the Statistical
Analysis System (release 6.07, SAS Institute, Cary, NC).
Melting Data
Fig. 1A displays
some representative normalized melting curves resulting from duplexes
constructed from the end set molecules. These duplexes contain a common
7-base sequence and differ only in the identity of the end base pair.
Each of the four curves shows a distinct melting profile and resultant
melting temperature. Likewise, Fig. 1 B displays melting
curves of duplexes constructed from the middle set molecules that
contain a single base change at position 4 from the 5`-end. The melting
profiles indicate that base pair type and orientation ( e.g. AT versus T
A) both lead to distinct changes in
thermostability. The different base pair orientations give rise to
different dinucleotide stacking interactions, which may account for the
different thermostabilities. For the molecules in
Fig. 1B, the spread in melting temperatures is nearly 8
°C; the difference between the molecules containing A
T and
T
A base pairs is over 3 °C.
Figure 1:
Normalized melting profiles of duplexes
contained in the end molecule sequence set ( A) and middle
sequence set ( B). The solution conditions were 10 m
M sodium phosphate, pH 7.0, 1.0
M sodium chloride and a
strand concentration of 2 µ
M. The duplex sequences in
A are AACTGGAC/GTCCAGTT, TACTGGAC/GTCCAGTA, GACTGGAC/GTCCAGTC,
and CACTGGAC/GTCCAGTG with melting temperatures, determined from the
first derivative of the curves, of 33.2, 30.4, 33.8, and 35.7 °C,
respectively. The duplex sequences in B are GCAAAGAC/GTCTTTGC,
GCATAGAC/GTCTATGC, GCAGAGAC/GTCTCTGC, and GCACAGAC/GTCTGTGC with
melting temperatures of 33.7, 30.6, 35.7, and 38.5 °C,
respectively.
The melting temperatures for
most of the perfectly matched duplex molecules of the end and middle
sets differ with the sequence variations. A tabulation of the unique
sequence, numeric identifier, and average melting temperature of each
of the perfect duplex molecules formed from the end and middle set
sequences is displayed in Table I. The experimental conditions were 10
m
M sodium phosphate, pH 7.0, 1.0
M sodium chloride
and a strand concentration of 2 µ
M. The differences in
melting temperatures due to sequence variation are as high as 18.9 and
23.3 °C for the end and middle sets, respectively, and as high as
8.2 °C for molecules of similar base pair content. These
differences are due to changes in only 3 base pairs, which lead to
differences in up to three and four nearest-neighbor stacking
interactions for the end and middle set duplexes, respectively.
was
0.4
°C for each duplex. This value was consistent enough for each
sequence analyzed that weighted regression was not used in subsequent
analyses.
(
)
of 0.9 °C. This exemplifies the variability in
precision within the data set and serves as a still more conservative
estimate of the error potentially contained in the assignment of the
melting temperatures.
60 °C. This feature is probably
associated with the melting out of the single strands and is contrary
to a two-state model. Any stability associated with structure of the
single-strand species must be considered when assigning thermodynamic
parameters to the transition of the duplex to single-strand
configuration.
Analysis Models
Thermostability of nucleic acid
duplexes is frequently modeled by assigning stabilities to singlet or
doublet interactions that account for stability due to base pairing or
nearest-neighbor stacking. A singlet interaction is defined as a single
base pair ( i.e. an AT or G
C base pair independent of
orientation), while a doublet interaction is defined as 2 adjacent base
pairs, with defined base pair orientations, which give rise to the
unique nearest-neighbor stacking interactions. Longer range
interactions are difficult to assign due to their relatively lower
energies and require a large data set for estimating the greater number
of variables. The present data set, however, does allow the estimation
of an increased number of variables due to the large number of
sequences analyzed. The variables, which are of primary interest for
this study, are the possible positional dependence of singlet and
doublet interactions. Many of these interactions, however, are linearly
dependent upon each other and therefore cannot be uniquely assigned
(27) . What can be determined are the types of interactions that
are important and the non-unique stability assignments. These
assignments can be used to predict thermostabilities or arranged in
linearly independent combinations for comparison purposes.
T or G
C. This model can be
extended (BP2) to accommodate positional dependence of the singlet
interactions by assuming that different stabilities can be assigned to
an A
T or G
C base pair positioned in the extreme 5`- or
3`-position of a strand as compared with an internal position. The
third extension of the base pair model (BP3) assumes that base pair
stability varies as a function of all positions in the duplex. Here,
there would be four assigned stabilities for both A
T and G
C
base pairs in an 8-base pair duplex due to symmetry ( e.g. position 1 at the 5`-end is assumed to be equivalent to position 8
at the 3`-end in an 8-base pair duplex).
values, while the
second p-value considers the variability associated with the
T
determination for the sequences common
to both the end and middle sets. These p-values indicate
different levels of error, which reflect the different components of
variability mentioned earlier. As judged by the RMSE values, the more
complex base pair models show no improvement in fit over the simpler
models. The fit for all models, as judged by the p-values, is
poor and implies that a model based solely on singlet interactions,
even with the inclusion of positional dependence, does not accurately
characterize the differences in thermostability. This does not imply
that there is no positional-dependent stability that can be associated
with the singlet interactions. Rather, the singlet interactions alone
are not sufficient for describing the data.
assignments of like sequences are considered, the
p-values indicate that the nearest-neighbor models should not
be rejected due to lack of fit. Further credence to the
nearest-neighbor models appears when those molecules with the largest
residuals resulting from the fit are excluded, and the data refit show
no meaningful improvement in the RMSE. Any real lack of fit that may
exist is apparently not due to one or a few molecules that are somehow
different from the others.
4 months during which experiments were conducted, but overall
differences in precision, which can be attributed to the end and middle
sets, are not apparent. Still, there are clear differences in the
performance of the singlet and doublet interaction models in describing
the melting data. The origin of these differences in the model fits is
not clear, but may be attributable to the positional dependence of the
nearest-neighbor stabilities and longer range interactions. The
molecules of the middle set focus on internal nucleotide positions and
result in altering the nearest-neighbor environment in all positions
but the extreme end stacks. The end set molecules focus on the end
positions and alter only the 3 end base pairs and do not affect the
central stack. The internal nucleotide positions and resultant
sequence-dependent stability seem to be more poorly modeled by singlet
and doublet interactions, implying the importance of longer range
interactions for these short sequences. However, these interactions are
too small in magnitude to extract from the data set under the current
level of error associated with the measurements.
Validation Set
The most definitive confirmation of
the various models is to observe their performance when predicting
Tvalues of sequences not contained in
the original molecule set. To this end, randomly generated duplex
sequences were prepared and melted. The resulting ``validation
set'' sequences along with the experimentally and theoretically
derived melting temperatures and changes in free energy are compared in
. These validation sequences are the summary of all
randomly generated duplex sequences examined. The collection of data
from other randomly generated sequences will certainly be useful. The
non-unique parameters resulting from the NN1 model were used in
predicting the T
and
G
values and are listed in . The
use of the positionally dependent terms resulting from the NN2 and NN3
models shows statistically improved fits. However, their practical
utilization is not justified since the additional 30 terms of the NN3
model lead to only slightly reduced RMSE values over the NN1 model.
Although the NN1 model does not include any positionally explicit
doublet information, positionally implicit singlet information is
present.
and
G
values for the validation set sequences and
could be used to predict the T
and
G
of any 8-base pair sequence melted under
the same conditions as used in the present experiments. The values are
not unique due to linear dependencies noted above, and physical meaning
cannot be attributed to them; however, predictions based on them are
unique and would be identical to those obtained using other model
parameterizations. To calculate the melting temperature or change in
free energy of duplex formation for any 8-base pair sequence, the model
coefficients in are multiplied by the number of
corresponding singlet and doublet interactions present and summed. The
resultant value is only meaningful under salt conditions of 1
M NaCl and a DNA strand concentration of 2 µ
M, but may
be useful as a guide for relative sequence stabilities under other
conditions.
Conclusions
Of the many stabilizing and
destabilizing interactions that influence DNA hybrid stability, the
base pairing and base stacking interactions are considered the most
important in describing the sequence-dependent stability. For short
oligonucleotide duplexes, allowance for both types of interactions is
necessary for the accurate prediction of melting temperatures. Models
based solely on base pairing do not fit the data accurately, while
models that incorporate base pair doublet information show improved
performance. Furthermore, consideration of end fraying, or the
preference of melting to initiate from the ends of a short duplex,
shows statistically improved fits. This positional dependence is a
minor component compared with the effects of the singlet and doublet
interactions and is not necessary to include for prediction within the
current level of error.
(
)
Table: Summary of melting temperatures and free
energies for end and middle set duplexes
Table: Summary of singlet interaction models
Table: Summary of doublet interaction models
Table: Non-unique
coefficients from the NN1 model
©1995 by The American Society for Biochemistry and Molecular Biology, Inc.