This study aims to refine existing statistical potentials for modeling RNA-protein interactions using coarse-grained approaches. The researchers applied existing potentials to a lattice model of a RNA-protein complex and identified the native structure within the lowest energy structures. They then derived new potentials from BLAST alignments of RNA sequences and found them to be largely consistent with the original potentials. The researchers propose exploring a larger dataset of RNA-protein complexes and different normalization methods to further improve the potentials for computational modeling of these interactions.

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As a first test, we applied our established Lustig-Jernigan (1997)
ribonucleotide-amino acid potentials on an updated coarse-grained
model calculation involving some 100-million lattice structures for BIV
TAR-Tat. The native target Tat peptide was identified within the lowest
0.28% of lattice structures as evaluated by these ribonucleotide-amino
acid potentials. However it appears there remains an opportunity for
even better native-target identification in the TAR-Tat model and other
coarse-grained examples by further refining the potentials. As a first
step we applied to the original 1997 RNA-protein contacts, BLASTN-
defined RNA variants replacing those determined from the original
small-RNA databases. The ordering of the quartet of potentials for each
of 5 key amino acids (A, R, N, Q, E) remains largely consistent.
Moreover, the correlation plot of BLASTn-derived potentials versus the
older corresponding potentials show significant co-linearity. This
suggests that it is worthwhile to explore a much larger ensemble (over
600) of post-1997 RNA-protein complexes, using ribonucleotide-amino
acid contact information from X-ray and sufficient complementary RNA
variant data. An updated expression for normalizing the potentials that
includes accounting for random substitutions shows promise in further
refining these potentials.
Abstract
Introduction
Methods and Results
Conclusions
Low-energy coarse grained structures can be evaluated with
existing RNA-protein potentials, realistically allowing
hierarchical modeling of low-energy all-atom models.
Correlation between 1997-data and BLASTn-data shows the
latter approach is plausible.
Though initially here there are only 20 BLASTn alignments for
three RNA-protein complexes, an expected larger set of more
than one-hundred such complexes should show significantly
improved sample size.
References
-Gerstein, M. & Altman, R. B. Average Core Structures and Variability
Measures for Protein Families: Application to the Immunoglobulins. J.
Mol. Biol. 1995. 251, 161-175.
-Hsieh, M., Collins, E. D., Blomquist, T. & Lustig, B. In Flexibility of
BIV TAR-Tat: Models of peptide binding. J. Biomol. Struct. Dyn.
2002, 20, 243-247.
-Lustig, B., Arora, S. & Jernigan, R. L In RNA base-amino acid
interaction strengths derieved from structures and sequences. Nucleic
Acids Res. 1997, 25, 2562–2565.
-Lustig, B. & Jernigan, R. L. Consistencies of individual DNA base-
amino acid interactions in structures and sequences. Nucleic Acids Res.
1995, 23, 4707-4711
-Perez-Cano, L., Solernou, A., Pons, C. & Fernández-Recio, J.
Structural prediction of protein-RNA interaction by computational
docking with propensity-based statistical potentials. Pac. Symp.
Biocomput. 2010A, 15, 293-301.
-Perez-Cano, L. & Fernández-Recio, J. Optimal Protein-RNAArea,
OPRA: A propensity-based method to identify RNA-binding sites on
proteins. Proteins. 2010B, 78, 25-35.
Computational modeling of RNA-protein interactions remains an
important endeavor. This is in part the result of the accelerated
identification of novel non-coding RNAs that exhibit a wide variety of
function and interaction. However, exclusively all-atom approaches that
model RNA-protein interactions via molecular dynamics and related
methods are often problematic in their application. This is very likely a
result of limitations involving RNA flexibility and certain force field
potentials that prove problematic in such complexes. One possible
alternative is the implementation of hierarchical approaches, first
efficiently exploring configurational space with a coarse-grained
representation of backbone placements, such as described in ROSETTA
and in various lattice model approaches. Subsequently, the lowest energy
set of such coarse-grained models can be used as scaffolds for all-atom
placements, a standard method in modeling protein 3D-structure. However,
this likely will require improved ribonucleotide-amino acid potentials as
applied to coarse-grained structures.
Thu Nguyen, Phuc Tran, Takayuki Kimura, Amos Park, Ariana Cesare, Loc Nguyen, Reema Shalan and Brooke Lustig
brooke.lustig@sjsu.edu
Development of Second-Generation RNA-Protein Statiastical Potentials
Figure 1. Lattice modeling of BIV TAR-Tat (Hsieh et al., 2002). (Left)
Native BIV TAR RNA backbone (1MNB) and its bound Tat peptide
backbone (Orange and Red ribbon) aligned with corresponding lattice
RNA and peptide (Pink and Green line). (Right) Lattice model energy
distributions of non-mutant (red) and glycine (75, 78 and 75/78)
substituted amino acid contacts using existing Lustig-Jernigan
ribonucleotide-amino acid contact potentials.
Acknowledgements: This work includes computational
resources from SJSU College of Science Network and Computer
Services.
Figure 17. Energy distribution of BIV TAR-TAT without Gly substitution and with Gly
substitution (at 75 and/or 78, or at both positions) for one move per 1 amino acids. Less
than 1% of energy distribution is not included, but lowest energy is -92 kcal/mol.
Native-like structure’s energy
Native-like structure’s energy an estimated
-83 kcal/mol
Figure 2. Contact energies for the common set of amino acids,
normalized for each RNA base quartet. The potential energy from
Lustig-Jernigan (1997) ribonucleotide-amino acid potentials (red) and
the twenty BLASTN-defined RNA sequences (orange). Generally the
order of the potentials remains fairly consistent between sets of data.
Figure 3. Correlation plot of the potential energies from 1997 versus
BLASTn ones. The slope of the regression line is 1.12 and the
correlation coefficient is 0.539, P < 0.020.
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A R N Q E
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1997-data BLASTn-data
We used frequencies of contacts between bases and amino acids to
derive relative interaction energies from the acyl tRNA-synthetase and
U1A spliceosomal protein-RNA complexes (Lustig et al., 1997). This
was similar to earlier DNA-protein contact potentials (Lustig et al.,
1995). First we calculate the logarithms of frequencies for all
occurrences of a j-type base interacting specifically with an I-type
amino acid so that the interaction energy eIj is of the form
eIj ~ -ln fIj (1)
where fIj is the sum over all the sets of the relative frequency in which
a base type j interacts with all occurrences of a residue type I. For
each of the four bases, the relative interaction energies are then
normalized as
∑jln fIj = 0 (2)
This original normalization simply shifts the values so that the mean
for the four bases is zero.
Here we applied BLASTn to query RNA sequences corresponding to
the various classes of tRNAs binding to their respective tRNA-
synthetase proteins. First, note there are a diversity of tRNA
sequences corresponding to different organisms, unlike the U1A
spliceosomal binding RNA.
Normalization and other Strategies
Figure 4. Contact energies calculated with different normalizations.
Original normalization (green) uses equation, ln(fraction of the
contacts) = 0 at each quartet, whereas alternative normalization (red)
uses eq. 3.
After we normalized the contact energy normalized within each
quartets, we applied an additional normalization to account for the
random substitution of amino acids in protein (Gerstein & Altman,
1995). Here, we modify eq. 1 by including a fraction FI of all
amino acids in our protein sets that are of class I, such that
eIj ~ ln (FIj /FI) (3)
where FIj just rescales fIj, so that the total number of residues in each
fraction effectively cancel.
Here we propose a number of strategies:
 Evaluate normalized potentials in alternative docking simulation
e.g. AutoDOCK.
 Utilize solvent accessible criteria for counting the residues and
nucleotides (Perez-Cano et al., 2010A).
 Explore alternative normalization for the quartet i.e. normalize
using the fraction of base type j for all nucleotides in the
complexes.
 Specify sub-classes for contacts specific for major grooves
versus minor grooves.
 Explore a much larger ensemble of current 602 RNA-protein
complexes (Perez-Cano et al., 2010B).
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Original normalization
Alternative normalization