However, we have previously dem-onstrated that a statistical hydrogen bonding potential can discriminate native structures of protein–RNA complexes from docking decoy sets [17].. This po
Trang 1specificity and relative binding energy of RNA-binding
proteins
Suxin Zheng1,*, Timothy A Robertson2,* and Gabriele Varani1,2
1 Department of Chemistry, University of Washington, Seattle, WA, USA
2 Department of Biochemistry, University of Washington, Seattle, WA, USA
The sequence-specific recognition of RNA by proteins
plays a fundamental role in gene expression by
direct-ing different cellular RNAs to specific processdirect-ing
path-ways or subcellular locations Many experimental
studies have explored the molecular basis for the
sequence dependence of protein–RNA recognition [1–
4]; more recently, a few studies have explored this
prob-lem from a computational perspective as well [5–16]
However, these early studies have emphasized
qualita-tive descriptions of the recognition process; relaqualita-tively
few attempts have been made to quantify the
character-istics of protein–RNA interactions using computational
approaches [17] Here, we present a new approach for
predicting the specificity of RNA-binding proteins and
to evaluate the contribution of individual amino acids
to the energetic of protein–RNA complexes
Knowledge-based potential functions have been
employed in protein structure prediction [18–27], as
well as in the prediction of protein–protein [25,28–30] and protein–ligand interactions [30–33] A few studies have explored the use of knowledge-based methods for the prediction of protein–DNA interactions from structure [30,34,35] More recently, our group [36] and others [37] have independently demonstrated that knowledge-based potentials can provide quantitative descriptions of protein–DNA interfaces comparable to those provided using molecular mechanics force fields [37]
The relative scarcity of high-resolution structures of protein–RNA complexes has represented an under-standable barrier to the quantitative application of computational approaches to the problem of protein– RNA recognition However, we have previously dem-onstrated that a statistical hydrogen bonding potential can discriminate native structures of protein–RNA complexes from docking decoy sets [17] As hydrogen
Keywords
distance-dependent potential; protein–RNA
interaction; RRM recognition; statistical
potential
Correspondence
G Varani, Department of Chemistry and
Department of Biochemistry, University of
Washington, Seattle, WA 98195, USA
Fax: +1 206 685 8665
Tel: +1 206 543 7113
E-mail: varani@chem.washington.edu
*These authors contributed equally to this
work
(Received 25 July 2007, revised 22
Septem-ber 2007, accepted 19 OctoSeptem-ber 2007)
doi:10.1111/j.1742-4658.2007.06155.x
RNA–protein interactions are fundamental to gene expression Thus, the molecular basis for the sequence dependence of protein–RNA recognition has been extensively studied experimentally However, there have been very few computational studies of this problem, and no sustained attempt has been made towards using computational methods to predict or alter the sequence-specificity of these proteins In the present study, we provide a distance-dependent statistical potential function derived from our previous work on protein–DNA interactions This potential function discriminates native structures from decoys, successfully predicts the native sequences recognized by sequence-specific RNA-binding proteins, and recapitulates experimentally determined relative changes in binding energy due to muta-tions of individual amino acids at protein–RNA interfaces Thus, this work demonstrates that statistical models allow the quantitative analysis of protein–RNA recognition based on their structure and can be applied to modeling protein–RNA interfaces for prediction and design purposes
Abbreviations
KH, K homology; MD, molecular dynamics; PDB, Protein Data Bank; RRM, RNA recognition motif; SRP, signal recognition particle.
Trang 2bonds represent only approximately 25% of contacts
between protein and RNA [12], we reasoned that a
more comprehensive approach would describe these
interactions more effectively
In the present study, we report the application of an
all-atom, distance-dependent statistical potential to the
prediction of sequence-specific recognition between
proteins and RNA We demonstrate that this approach
can discriminate native structures of complexes from
even close docking decoys, recapitulate experimentally
determined relative binding energies (DDGs) for several
protein–RNA complexes, and predict the RNA
sequences recognized by a number of different RNA
recognition motif (RRM) and K homology (KH)
domains These results demonstrate that statistical
models can be applied to problems requiring the
high-resolution modeling of protein–RNA interactions The
anticipated future enrichment of the structural
data-base will further improve the predictive performance
of the potential
Results
The all-atom distance potential is constructed from the
distribution of interatomic distances observed in the
high resolution (< 2.5 A˚) structures of protein–RNA
complexes deposited in the Protein Data Bank (PDB)
In this approach, the ‘correctness’ of a protein–RNA
structure is assumed to be approximated by the sum of
the probabilities of observing the set intermolecular
distances defined in the 3D structure, relative to the
likelihood of encountering such distance in the dataset
of all protein–RNA structures This kind of method
was proposed by Sipple [20], and has been applied to
protein structure prediction, protein–protein and
pro-tein–ligand interactions [18–33], as well as to protein–
DNA recognition [30,34–37] The distance-dependent
statistical potential used here for protein–RNA
inter-faces is essentially identical to the score recently
described by us for protein–DNA complexes [36] The
primary difference is the introduction of a new
pseud-count correction, where an optimized number of
pseudocounts are added to the observed counts for
each atom pair (for additional details, see
Experimen-tal procedures) As a control, we also tested a simple
contact-counting method, wherein every contact
between protein and RNA (within a given distance
cut-off) was assigned the same score of)1
Docking decoy discrimination
An important property of any potential function is its
ability to discriminate cognate (native
crystallographi-cally determined structures) from noncognate (decoy) structures [38] As a preliminary test of our method, and a direct comparison with previous work, we used our distance-dependent potential to evaluate five sets
of docking decoys generated for the application of the rosetta physical potential function to protein– RNA interactions [17] These decoys were created using a combination of rigid-body docking and pro-tein side-chain repacking, and range in rmsd (relative
to the native structure) from 0.2 A˚ to over 20 A˚ Thus, they represent a solid basis for comparison to
a much more complex scoring method (the multiterm, hybrid physical⁄ statistical potential function used by rosetta)
When scored with the distance-dependent potential, the native complex can always be identified as the best structure in each of the five decoy sets (Fig 1), even for decoys that are very close to the native structure The native structure Z-scores for these decoy sets are shown in Table 1 These values indicate a strong dis-criminatory ability, comparable to that reported by Chen et al [17] using their significantly more complex scoring method Overall, the distance potential (using
a 6 A˚ cut-off) results in a mean native Z-score of )5.45, versus the value of )6.37 obtained by Chen
et al [17] (Table 1); this difference is statistically insig-nificant (P¼ 0.53, Welch’s two-sided t-test), indicating that the two methods are equivalent
When we investigated protein–DNA complexes using the same approach, we demonstrated that the all-atom potential outperformed a reduced atom description, where relevant groups were grouped according to their chemical similarity (as described in the Experimental procedures) [36] Given the relative sparsity of the structural database, we investigated whether a reduced-atom representation would not lead
to improved performance in the protein–RNA case The all-atom potential performs better than the reduced atom potential (mean Z-score )5.45 versus )4.66; see also supplementary Table S4), although the difference is not as striking as for protein–DNA com-plexes We believe this is due to less favorable statistics (fewer structures of protein–RNA complexes) We anticipate that the increasing availability of protein– RNA structures, together with the availability of data
on specificity, will further improve the performance of the knowledge-based predictive method presented here
We retained the all atom representation because it is already slightly better than the reduced atom approach
The protein–RNA score has distinctive properties compared to the protein–DNA potential When we scored the protein–RNA decoy set using the protein–
Trang 3DNA potential, the average Z-score was
approxi-mately half that obtained with the protein–RNA
potential ()2.84 versus )5.45; see also supplementary
Table S4) Thus, although the chemistry of RNA and
DNA are very similar, the structure of RNA allows
for different interactions between proteins and the two
nucleic acids that are reflected in this result
To investigate whether the statistical potential is not simply reflecting the size of an interface or the number
of intermolecular contacts, we also used a very simple contact-counting potential to evaluate the same decoys;
in this method, the fitness of an interface is evaluated
by counting the number of close approaches between the protein and RNA Satisfactorily, this method was
A B
C
E
D
Fig 1 Score–rmsd plots for the five docking decoy sets generated by Chen et al [17]; the score generated by the distance-dependent potential (in arbitrary units) is plotted versus the deviation from the native structure (open circle at rmsd ¼ 0) (A) Poly A-binding protein in complex with polyadenylate RNA (PDB code: 1CVJ) (B) Nova-2 KH RNA-binding domain 3 (PDB code: 1EC6) (C) HuD protein in complex with AU-rich RNA (PDB code: 1FXL) (D) Human SRP19 in complex with human SRP RNA (PDB code: 1JID) (E) Human U1A protein in com-plex with U1 snRNA hairpin (PDB code: 1URN) Close-up views of near-native decoys (0–3 A ˚ rmsd) are shown in the insets.
Trang 4much less effective, providing an average Z-score of
)2.64, less than half of the average native Z-score
found using the distance potential (Table 1)
Interestingly, the magnitude of the observed
Z-scores declines significantly as the contact cut-off is
increased from 6 A˚ to 10 A˚ and then to 12 A˚ (see
sup-plementary Table S5), suggesting that short-range
con-tacts provide the bulk of the discriminatory power in
this test This result suggests that protein–RNA
recog-nition specificity is primarily determined by
short-range intermolecular contacts Long-short-range effects (e.g
nonlocal electrostatics) appear to play a more limited
role, at least in decoy discrimination
To test the discrimination ability of the potential for
near native decoys, we next compared its ability to
discriminate near-native protein–RNA structures with
that of the force field implemented in the amber 8
molecular simulation package We generated
near-native protein–RNA decoys for 21 protein–RNA
complexes by conducting molecular dynamics (MD)
simulations of the native complexes, and by selecting
multiple time-steps from the resulting trajectories for
each structure We then scored these structures using
the distance-dependent potential function, and
exam-ined the correlations between distance scores and
amberenergies for each decoy set
This is a difficult test of score performance because
the structures are very close to native Indeed, neither
the distance-dependent score, nor the amber potential
appears to be able to discriminate native structures
from these very near-native, MD-generated decoys
(average Z-score of )0.69 versus )0.59; Table 2)
Although there is no correlation of the either score
with rmsd, the distance-dependent statistical potential
is somewhat correlated (average R2¼ 0.41) with the
energy values predicted by the amber force field Thus,
it remains very difficult for either approach to discrim-inate the native structure from structures that are close
to it in energy
Identifying RNA-binding sequences from structure
Having established the performance of the statistical potential function in decoy discrimination, we investi-gated the ability of the potential to perform tasks rele-vant to its intended application First, we sought to evaluate whether the potential could predict the cog-nate recognition sequences of RNA-binding proteins This is a particularly important problem because sequence specificity is known for only a fraction of all RNA-binding proteins The ability to predict (or at least narrow down) the cognate sequence for ‘orphan’ RNA-binding proteins would greatly facilitate the design of biological experiments aimed at dissecting the function of these proteins It is also a problem that
is not well suited for MD approaches because of the demanding computational requirements
This application relies on a specific structural model of RNA recognition by RRM and KH
Table 1 Native Z-scores and score–rmsd correlation coefficients
for the protein–RNA docking decoy sets prepared by Chen et al.
[17]
Z-scores
Distance-dependent a Coulomb b
ROSETTA +
HB c
Contact count a
Mean ± SD )5.45 ± 1.76 )1.31 ± 0.18 )6.37 ± 2.58 )2.64 ± 0.84
a Using a 6 A ˚ contact cut-off b From Chen et al [17] and referring to
a potential lacking the directional component of hydrogen bonding
(HB) interactions.cFrom Chen et al [17] and referring to the
com-plete potential function.
Table 2 Z-scores and correlations for near-native decoys generated
by MD simulation.
Largest rmsd (A ˚ )
Z-scores
Distance-dependent versus AMBER (R 2 )
Distance-dependent AMBER
Mean ± SD )0.69 ± 1.28 )0.59 ± 1.94 0.41 ± 0.15
Trang 5domains involving four nucleotides, as detailed in the
Experimental procedures This model is strongly
sup-ported by previous research on the mechanism of
RNA recognition for RRM proteins [6,39,40] and by
the structure of existing KH domains bound to RNA
[6,41–44] As a consequence of the assumptions of
the model, complexes containing two RNA-binding
domains were divided into independent structures
(e.g 1CVJ_1 and 1CVJ_2 represent the first and
sec-ond Poly A binding protein domain of structure
1CVJ, respectively), and the two domains were
con-sidered structurally and thermodynamically unrelated
Because the model assumes that each RRM and KH
domain binds to each of four nucleotides
indepen-dently, we generated a set of 44 (256) different
structures for each protein–RNA complex by
compu-tationally ‘threading’ all possible four-nucleotide
com-binations onto the RNA bases nearest the center of
the b-sheet structure of the RRM We then scored
these sequence-variant structures with the
distance-dependent potential function
Figure 2 shows the results of this analysis If the
potential and model of recognition were perfect, and if
each structure was sequence-specific and corresponded
to the most favorable sequence recognized by a given
domain, the cognate sequences of the tested structures
would be expected to rank as number 1 Because it is
unlikely that the cognate recognition sequences for all
domains will be consistently assigned the best score,
we expressed sequence-discrimination performance in
terms of percentiles (where perfect discrimination of
the cognate recognition sequence would result in a
percentile score of 100) Remarkably, we found that 18
of the 29 tested RRM and KH domain complexes had their cognate recognition sequence ranked above the 90th percentile (i.e had better than ten-fold enrich-ment for the correct sequence) Furthermore, the distance-dependent potential ranks the cognate recog-nition sequences of the protein–RNA complexes in our test set above the 90th percentile, on average By con-trast, when we performed the same test using a simple counting potential as a control (Fig 2), the average rank was the 41st percentile
Among successful examples of binding-sequence discrimination, the native sequences of the RRM1 of Sex-lethal protein (1B7F_1) and KH1 domain of Poly C-binding protein-2 were both ranked first out
of 256 sequences, whereas KH domain 3 of hnRNP K (1ZZI), RRM of U2B¢ protein (1A9N) and RRM 4 of Polypyrimidine Tract Binding protein (2ADC_1) each had their cognate recognition sequences ranked in the top 3 (Supplemental Table S2) However, prediction was less successful for other RRM domains, such as the U1A complex (the cognate recognition sequence of U1A protein was ranked at 30) This result is none-theless not too surprising due to the noncanonical, seven-nucleotide recognition sequence (AUUGCAC) recognized by U1A that makes an unusually specific and strong interaction with RNA, unparalleled in other known RRMs [45] Relatively poor results were also obtained for the Poly A binding potein (1CVJ_1, rank 19), and for RRM1 of the HuD protein (1FXL_1, rank 32) Both Pab and HuD utilize two domains to achieve sequence-specific recognition in a cooperative manner and do not discriminate well between sequences that are related to their cognate rec-ognition motif (A-rich and AU-rich sequences, respec-tively) [46] Notably, however, the nonsequence-specific RNA helicase protein (PDB code: 2DB3, included as a negative control) had an expectedly poor cognate sequence rank of 226⁄ 256
Estimating experimentally determined relative RNA-binding affinities
A second very important property of any potential function is the ability to recapitulate the sequence dependence of experimental binding energies; this is a prerequisite if the potential is to be applied to prob-lems of protein–RNA interface prediction or design Fortunately, a few structures have a relatively dense set of experimentally determined binding constants for interface mutations We used these experimentally characterized mutants to create a set of computation-ally ‘mutated’ structures of the complexes (Table 3),
Fig 2 Structure-based identification of RRM recognition
sequen-ces The cognate sequence is ranked by the distance potential
(cut-off ¼ 6 A˚) for RRM ⁄ KH domain proteins The red line
repre-sents the rank of cognate recognition sequences using the
contact-counting score; the blue line represents the rank of these
sequences using the distance-dependent potential The points in
each colored line are sorted independently by rank; the x-axis is the
sort order The dashed line represents the 10th percentile.
Trang 6and have scored these structures using the
distance-dependent statistical potential
A first very instructive example is provided by
mutants of bacteriophage MS2 coat protein [47,48]
Starting with the crystal structure of the complex
between MS2 coat protein and the cognate RNA
hair-pin (PDB code: 1ZDI), a series of structures were
gen-erated, representing the RNA and protein mutants for
which binding constants are reported in the literature
Then the distance-dependent potential scores for these
structures were compared with the known binding
con-stants for each mutation Unfortunately, when all of
the MS2 mutations were considered together, a poor
correlation was observed between distance score and
experimental binding affinities (data not shown)
How-ever, excellent correlations were obtained between
these values when the binding-affinity data were
divided into two subsets (Table 3, Fig 3) A first set
corresponds to complexes where the bound RNA
hair-pin contained adenine, guanine or uridine base at
posi-tion )5; the second set contains instead protein
mutants where the bound RNA contained a cytosine
at this position Within each sets of mutants, the
corre-lation between distance score and experimental binding
affinity is strong (R2¼ 0.65, Fig 3A; R2¼ 0.97,
Fig 3B), and statistically significant at the 95%
confi-dence level Figure 3C shows a likely explanation for
this result: an intramolecular hydrogen bond formed
by the cytosine at position )5 [47] When this
nucleo-tide is mutated to any other base, the intramolecular
hydrogen bond is lost, leading to a reorganization of
the RNA structure
This result does not provide direct information on the relative contribution of that hydrogen bond to the overall binding energy; it is simply implied that
Table 3 Correlations between the distance-dependent score and
the experimental free energy of binding for several mutant protein–
RNA complexes.
Distance-dependent Contact counting
6 A˚ 10 A˚ 12 A˚ 6 A˚ 10 A˚ 12 A˚ Protein mutations
MS2 (no cytosine
at position )5)
0.43 0.50 0.65 0.19 0.10 0.08 MS2 mutations
(with cytosine at )5)
0.81 0.81 0.97 0.43 0.14 0.09
RNA mutations
SRP; 2¢-OH mutations 0.87 0.56 0.52 0.36 0.30 0.29
SRP; base mutations )0.07 )0.03 )0.07 0.01 0.07 0.05
a
The native U1A complex was included in the training set for this
experiment b The U2B¢ complex (U1A homolog) was included in
the training set for this experiment.
A
B
C
Fig 3 Correlation between scores generated by the distance-dependent statistical potential and experimental binding free ener-gies (logK d ) for mutants of the MS2 coat protein (A) Complexes between protein mutants and RNA-containing nucleotides other than cytosine at position )5 (B) Complexes between protein mutants and RNA containing cytosine at position )5 (C) The char-acteristic intramolecular hydrogen bond between the amino group
of C5 and the O1P atom of U6 observed in the structure of the MS2–RNA complex containing a cytosine at position )5 that helps organize the RNA structure for protein binding [47].
Trang 7mutations must be segregated into two groups to
obtain a clear correlation between experimental and
predicted relative affinities The most likely
explana-tion for this result is that, at present, the statistical
potential does not consider RNA intramolecular
con-tacts; therefore, contributions to binding energy due to
changes in RNA structure (i.e that occur when that
hydrogen bond is lost) cannot be captured by our
cur-rent approach
A second example that reinforces our interpretation
of the results obtained with MS2 is provided by Fox-1
protein, which regulates alternative splicing of
tissue-specific exons by binding to the GCAUG sequence
[49] The structure of the complex (PDB code: 2ERR)
and the experimental binding constants for two sets of
related mutations have been reported [49]: one set for
mutations on the Fox-1 protein and a second set for
mutations to its target RNA molecule A moderately
strong correlation was observed between the distance
score and the protein mutation data (R2¼ 0.46,
Fig 4), but an anticorrelation was observed for the set
of RNA mutations (R2¼)0.57; Table 3) As in the
previous case, this result reflects the failure of the
current statistical potential to capture the energetic
contribution associated with the disruption of RNA
intramolecular interactions that are a characteristic of
this complex [49]
A third example is human U1A protein (PDB code:
1URN), a great model for the RRM superfamily
because of the availability of NMR and
crystallo-graphic structures [50,51], as well as binding data
In this case, we observed poor correlations between
the distance-dependent score and the experimentally
determined dissociation constants (Kd) [52] when we
conducted a test using a training set of strictly
non-homologous protein–RNA structures Initially, we
assumed that this observation would reflect the very
large and energetically significant conformational
changes that have been observed in the RNA and
protein upon complex formation [53] However, when
the U1A complex itself was included in the training
set, we obtained moderate to strong correlations (R2
values between 0.27 and 0.65, depending on the
choice of distance cut-off) This suggests that U1A
binds to RNA by forming intermolecular interactions
that are not commonly observed in the database of
training structures This hypothesis is supported by
the observation that the inclusion of a close U1A
homolog (the U2B¢–U2A¢ complex) in the training set
improves the results of this test as well (R2 increases
from 0.04 to 0.39; Table 3) Thus, it appears that the
structure of the U1A or of its homologous complex
contains a set of protein–RNA atomic contacts (i.e
interatomic distances) that are not well represented in the 71 other protein–RNA complexes in our training set
Figure 5 shows the final example, a universally con-served component of the core of the signal recognition particle (SRP) The structure of the complex (PDB code: 1HQ1) and the binding affinity of a series of RNA mutants have been determined [54] The distance potential results in scores that correlate significantly (R2¼ 0.52, P £ 0.05) with experimental binding affini-ties for mutations involving substitutions of deoxy-nucleotides for their corresponding ribodeoxy-nucleotides However, as observed for Fox-1, no significant
Ade-4
Cyt-3
Ura-1 Gua-2
A
B
Fig 4 (A) Correlation between scores generated by the distance-dependent statistical potential and experimental binding free ener-gies (logKd) for mutants of the Fox-1 protein (B) The intramolecular hydrogen bond between uracil 1 and cytosine 3, and the non-Wat-son–Crick base pair between guanine 2 and adenine 4 for the RNA
in complex with Fox-1 protein (PDB code: 2ERR) The protein is represented in yellow; the RNA structure is colored by atom type.
Trang 8correlation was found for mutations of nucleotides
that disrupt critical RNA intramolecular interactions
In this final case, these mutations involve the
disrup-tion of base pairs near the binding interface that define
the secondary structure of the RNA, which is
obvi-ously important for recognition, but do not contribute
directly to the formation of intermolecular contacts
[54]
Disscussion
The central role of protein–RNA interactions in
the regulation of gene expression has led to
consider-able interest in the biochemical processes underlying
these interactions [55–57] However, much of this
research has been devoted to the study of the
struc-ture⁄ function relationship for individual protein–
RNA complexes, and little effort has been made to
develop quantitative models that might describe
these interactions more comprehensively Thus, our
understanding of the mechanisms driving protein–
RNA recognition is still largely descriptive [11]
Recent work on protein–DNA interactions has
shown that quantitative models of protein–nucleic
acid recognition can provide insight into the
mecha-nisms of gene regulation [58,59], and, in the not too
distant future, promise to allow the rational design
of DNA-binding proteins with altered specificity [60]
The development of computational tools capable of
predicting the specificity of RNA-binding proteins
across entire families (such as the RRM
superfam-ily), or of redesigning the specificity of these
pro-teins, would be of equal importance in dissecting
post-transcriptional regulatory mechanisms, and in
providing new tools to interrogate gene expression
pathways
In a previous study, our group demonstrated that a statistical potential function could be surprisingly accu-rate when used to predict protein–DNA interactions from structure [36]; this result was corroborated by a similar study published concurrently by another group [37] Given these results, we hypothesized that the same approach would be equally successful with pro-tein–RNA interfaces Indeed, although various statisti-cal techniques have been used by a number of groups for the prediction of protein structures, protein–DNA and protein–ligand interactions [18–35], such an approach has never been applied to protein–RNA interactions
In the present study, we describe the successful application of the distance-dependent, all-atom statis-tical potential function to the prediction of the ener-getics and recognition specificity of protein–RNA interactions We demonstrate that the statistical potential can recapitulate experimentally determined relative binding constants for a number of protein– RNA complexes (with the caveat that it cannot yet capture the effect of mutations on RNA–RNA inter-actions) We also demonstrate that this simple tech-nique is remarkably successful at predicting the cognate recognition sequences of a wide variety of RNA-binding proteins
The challenge of near native decoy discrimination
The statistical potential performs very well in classi-cal decoy discriminations tests It is quite remarkable that similar Z-scores in tests of decoy discrimination are obtained for the statistical score and the rosetta-derived score because this second method contains many more adjustable parameters that are optimized to reproduce the average composition of these interfaces as observed in nature By contrast, the current statistical potential was generated ‘as is’ from the observed frequency of intermolecular con-tacts in the database of protein–RNA structures Thus, it appears that the distance-dependent statisti-cal potential implicitly captures at least some of the complexities of these intermolecular interactions that are explicitly enumerated in physical energy functions
The question of how to generate and discriminate near-native decoys is still an open challenge for many areas of computational structural biology [61,62] The docking decoy set used here contains many near-native decoys (e.g < 1 A˚ rmsd) that can be discriminated by the distance-dependent potential (Fig 1) However, when testing against the exceptionally near-native
Fig 5 Correlation between scores generated by the
distance-dependent statistical potential and experimental binding free
energies (logKd) for ribose-to-deoxyribose mutants of a universally
conserved protein component of the SRP.
Trang 9decoys generated by extracting snapshots from MD
simulations (Table 2), we found that near non-native
decoys could not be reliably discriminated from native
structures, not even by amber, which was used to
con-duct the MD simulations Thus, the question of how
to create a potential that is sensitive to the extremely
subtle structural variations present in very near-native
decoys remains a challenging and important area of
research We are hopeful that the incorporation of
terms describing the higher-order geometric preferences
of protein–RNA interfaces (e.g the incorporation of a
directional hydrogen-bonding potential) [17] may
enhance the discriminatory power of our method, as
will the inevitable increase in high-resolution structural
data available for training Nevertheless, the
distance-dependent potential function already performs on par
with the amber and rosetta force fields in decoy
dis-crimination tests
The impact of contact distance cut-off on
discriminatory power
The contact distance cut-offs used in the present
study were varied to determine the value that
maxi-mizes decoy discrimination performance for protein–
RNA complexes Previously, Robertson et al [36]
showed that shorter contact cut-offs result in optimal
discrimination ability in protein–DNA complexes,
whereas Samudrala et al [21] found that a longer
cut-off (> 10 A˚) was better able to discriminate
cor-rect structures during protein structure prediction
experiments Finally, Lu et al [23] demonstrated that
the first coordination shell (i.e a cut-off between
3.5 A˚ and 6.5 A˚) achieves the greatest selectivity for
protein decoys created using gapless threading
pro-cedures; thus, the question remains as to the best
choice of contact cut-off
To evaluate the influence of different cut-off values
in our study, replicate experiments were conducted
using 6 A˚, 10 A˚ and 12 A˚ distance cut-offs In nearly
all of our tests, the use of a shorter contact cut-off
(6 A˚) results in greater selectivity for structural details
of the interface (Table 1) For the prediction of
mutation energies, however, a longer cut-off appears
to outperform shorter cut-off values for some sets of
mutation data (Table 3) Some of these mutations are
not near the protein–RNA interface (e.g one of the
U1A mutations, D79V, is 9 A˚ from the RNA
mole-cule), and only the use of a longer cut-off value can
capture these effects In light of the differing
conclu-sions of previous research [21,23,36], these results
imply that a ‘one size fits all’ approach to energy
function design may be limiting In other words, it
may be possible to significantly improve potential functions by customizing their parameterization to particular problems
Prediction of RNA recognition sequences from protein–RNA complex structures
An obvious but yet to be attempted application of any potential function for protein–RNA interactions
is the prediction of cognate binding sequences In a test of sequence recognition for 29 unique KH and RRM domains, we found that the potential is able to identify (within the 10th percentile) the cognate RNA recognition motifs of these domains approximately 70% of the time As not all RRM⁄ KH domains (for example, U1A) obey the simple four-nucleotide recog-nition model that we have introduced (where each nucleotide makes independent interaction with the protein) [6], and the specificity of some proteins is limited (i.e they bind nearly equally well to a set of related sequences), this is a remarkably strong result Despite the simple form of the statistical potential, and the over-simplifications of the four-nucleotide recognition model, this method is surprisingly robust over the diverse set of RNA-binding domains that we have considered
Prediction of relative protein–RNA binding energies
When we evaluated the relative free energy of a set
of mutations for several protein–RNA complexes of known structure, the distance-dependent potential was successful within defined structural classes We observed strong, statistically significant (P£ 0.05) score–energy correlations for several sets of mutations that we tested; however, to achieve these results, it was necessary to subdivide several of the mutation data sets For example, for the MS2 complex, the mutation data had to be divided into two classes based on the presence or absence of a cytosine at position )5 in the RNA A likely explanation for the importance of the )5 cytosine mutation is offered by the observation that the amino group of the cytosine at position )5 makes an intramolecular hydrogen bond that increases the propensity of the free RNA to adopt the structure seen in the complex [48] (Fig 3C) Because the dis-tance potential currently measures only intermolecular interactions, it is unable to capture the thermodynamic effect of interactions within the RNA or protein, and
of mutation-induced changes in RNA (or protein) structure The good correlations of distance potential with experimental binding energies (i.e when sequence
Trang 10mutations are grouped according to the base identity
at position)5) strongly suggests that the potential
cap-tures the energetic contributions of intermolecular
interactions well
The same limitations observed in the MS2 mutation
data led to the failures in prediction for RNA mutations
in the Fox-1 and SRP complexes In the structure of the
Fox-1 complex, nucleotide U1 interacts with C3 by
forming an intramolecular hydrogen bond, whereas G2
and A4 form a non-Watson–Crick base pair [49]
(Fig 4) Four out of seven Fox-1 RNA mutations that
were tested directly affect these intramolecular
interac-tions, which are not evaluated by the statistical potential
used in the present study In the case of the RNA
muta-tions to the SRP complex, the mutated RNA residues
are located in a double-stranded region of RNA, and do
not interact with the protein [54], yet the disruption of
the helix clearly affects the binding energy The effect of
these changes in RNA conformation cannot be captured
by the intermolecular potential function used here
Given these observations, it is reasonable to
clude that the omission of protein intramolecular
con-tacts might also limit the predictive power of the
method However, additional examples will need to be
examined before definite conclusions can be made
con-cerning the applications of statistical potentials to
pre-diction of relative binding energies
The effect of training set composition
on potential function performance
All knowledge-based potentials face the possibility of
unintentional bias or over-training because their
train-ing depends upon the selection of a representative
sam-ple of structures If great care is not exercised to
ensure that this training set is unbiased (i.e
structur-ally heterogeneous), it is possible to create a statistical
potential that unfairly scores certain structures more
favorably than others simply because they are
over-represented in the training set
The challenge of over-fitting is particularly acute for
protein–RNA interactions because there are relatively
few high-resolution structures of protein–RNA
com-plexes Because of this limitation, a combined
train-ing⁄ test set was used in the present study To avoid
bias, a ‘leave one out’ cross-validation strategy was
employed: the tested structure was always excluded
from the training set Thus, every test in the present
study was conducted with a different score, and
trained using only those structures that were not
homologous to the tested protein–RNA complex
This strategy cannot be avoided at the present time,
yet it leads to situations where the training data does
not contain enough information to capture particular structural phenomena For example, we observed vir-tually no correlation between the distance-dependent score and the experimental binding affinity for muta-tions of U1A protein until the U1A complex structure was added to the training set (Table 3) Addition of the homologous U2B¢ complex structure (PDB code: 1A9N) to the training set improved these results con-siderably, indicating that the training set was missing critical structural information that would help to dis-criminate native-like contacts unique to the U1A com-plex (an unusually high-affinity RRM, with a long, seven-nucleotide recognition sequence) [52] We antici-pate that the performance of the method will improve with the size of the structural database, as more high-resolution protein–RNA structures become available
Conclusions
We have introduced a statistical potential function that discriminates the structures of native protein–RNA complexes from decoys, reproduces experimentally determined relative binding affinities for a number of RNA-binding proteins, and predicts cognate binding sequences for a large set of protein–RNA complexes The statistical potential performs as well as highly optimized physical potential functions in tests of docking decoy discrimination We anticipate that the performance of the potential will only increase with the size of the structural database and as terms are added to the model to account for protein and RNA intra-molecular interactions that are currently ignored Nevertheless, even in its current implementation, this statistical model achieves a high degree of sensitivity to subtle changes in protein–RNA interface structure We are optimistic that this knowledge-based potential function will find broad application to problems requiring the high-resolution modeling of protein– RNA interfaces, such as structure-based genome anno-tation, or the rational design of novel RNA-binding proteins
Experimental procedures
All-atom distance potential The potential function used here is identical to a previ-ously described method [36] (a more complete description
of the method is provided in supplementary Doc S1), with the exception of a modified low-count correction In the present study, the correction described by Sippl [20] is replaced with a weighted pseudocount method, where a constant number of pseudocounts (P) are added to the