PRIDB: a Protein-RNA Interface Database.
ABSTRACT The Protein-RNA Interface Database (PRIDB) is a comprehensive database of protein-RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein-RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein-RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein-RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein-RNA complexes are provided. The PRIDB database is freely available online at http://bindr.gdcb.iastate.edu/PRIDB.
- SourceAvailable from: Gian Gaetano Tartaglia[Show abstract] [Hide abstract]
ABSTRACT: The recent shift towards high-throughput screening is posing new challenges for the interpretation of experimental results. Here we propose the cleverSuite approach for large-scale characterization of protein groups. Description: The central part of the cleverSuite is the cleverMachine, an algorithm that performs statistics on protein sequences by comparing their physico-chemical propensities. The second element is called cleverClassifier and builds on top of the models generated by the cleverMachine to allow classification of new datasets. We applied the cleverSuite to predict secondary structure properties, solubility, chaperone requirements and RNA-binding abilities. Using cross-validation and independent datasets, the cleverSuite reproduces experimental findings with great accuracy and provides models that can be used for future investigations. The intuitive interface for dataset exploration, analysis and prediction is available at http://s.tartaglialab.com/clever_suite. firstname.lastname@example.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.Bioinformatics 02/2014; · 5.47 Impact Factor
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ABSTRACT: Though most of the transcripts are long non-coding RNAs (lncRNAs), little is known about their functions. lncRNAs usually function through interactions with proteins, which implies the importance of identifying the binding proteins of lncRNAs in understanding the molecular mechanisms underlying the functions of lncRNAs. Only a few approaches are available for predicting interactions between lncRNAs and proteins. In this study, we introduce a new method lncPro. By encoding RNA and protein sequences into numeric vectors, we used matrix multiplication to score each RNA--protein pair. This score can be used to measure the interactions between an RNA--protein pair. This method effectively discriminates interacting and non-interacting RNA--protein pairs and predicts RNA--protein interactions within a given complex. Applying this method on all human proteins, we found that the long non-coding RNAs we collected tend to interact with nuclear proteins and RNA-binding proteins. Compared with the existing approaches, our method shortens the time for training matrix and obtains optimal results based on the model being used. The ability of predicting the associations between lncRNAs and proteins has also been enhanced. Our method provides an idea on how to integrate different information into the prediction process.BMC Genomics 09/2013; 14(1):651. · 4.40 Impact Factor
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ABSTRACT: Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence-derived features of RNA-binding proteins. A webserver implementation of both methods is freely available at http://einstein.cs.iastate.edu/RNABindRPlus/.PLoS ONE 01/2014; 9(5):e97725. · 3.73 Impact Factor
PRIDB: a protein–RNA interface database
Benjamin A. Lewis1,2,*, Rasna R. Walia1,3, Michael Terribilini4, Jeff Ferguson5,
Charles Zheng5, Vasant Honavar1,3and Drena Dobbs1,2
1Bioinformatics and Computational Biology Program, Iowa State University,2Department of Genetics,
Development and Cell Biology, Iowa State University,3Department of Computer Science, Iowa State University,
Ames, IA 50011,4Department of Biology, Elon University, Elon, NC 27244 and5Computational Systems Biology
Summer Institute, Iowa State University, Ames, IA 50011, USA
Received August 15, 2010; Revised October 15, 2010; Accepted October 18, 2010
The Protein–RNA Interface Database (PRIDB) is a
comprehensive database of protein–RNA interfaces
extracted from complexes in the Protein Data Bank
(PDB). It is designed to facilitate detailed analyses
of individual protein–RNA complexes and their
interfaces, in addition to automated generation
of user-defined data sets of protein–RNA interfaces
applications. For any chosen PDB complex or list
of complexes, PRIDB rapidly displays interfacial
amino acids and ribonucleotides within the primary
sequences of the interacting protein and RNA
chains. PRIDB also identifies ProSite motifs in
protein chains and FR3D motifs in RNA chains
and provides links to these external databases, as
well as to structure files in the PDB. An integrated
JMol applet is provided for visualization of interact-
ing atoms and residues in the context of the 3D
complex structures. The current version of PRIDB
protein–RNA complexes available in the PDB (as of
10 October 2010). Atomic- and residue-level contact
information for the entire data set can be down-
loaded in a simple machine-readable format. Also,
several non-redundant benchmark data sets of
protein–RNA complexes are provided. The PRIDB
database is freely available online at http://bindr
Protein–RNA interactions play critical roles in myriad
and diverse biological processes, including many recently
discovered regulatory functions, in addition to well-studied
roles in protein synthesis, DNA replication, regulation of
gene expression and defense against pathogens (1–9).
Despite their importance, structures of protein–RNA
complexes have proven difficult to obtain using exper-
imental structure determination methods; such structures
(PDB) (10). For this reason, several computational
methods for predicting the interfaces in protein–RNA
complexes have been developed (11–21). Virtually all
such methods require data in the form of information
about structurally characterized protein–RNA complexes
and their interfaces.
PRIDB is a repository of protein–RNA interface
information derived from structures in the PDB. PRIDB
is designed to facilitate detailed analyses of individual
protein–RNA complexes of interest and rapid identifi-
cation of interfacial atoms and residues in both the
protein and RNA chains of a chosen complex or user-
defined set of complexes. In addition, PRIDB can be used
to generate data sets of protein–RNA interfaces for
machine learning applications, such as the generation of
classifiers for predicting interfaces in protein–RNA
complexes for which high-resolution structures are not
To our knowledge, only one other up-to-date and compre-
hensive online repository of protein–RNA interfaces is
currently available: Biological Interaction Database for
Protein-Nucleic Acid (BIPA) (22). BIPA provides a list of
protein–RNA (and protein–DNA) complexes from the
PDB and displays RNA-binding residues within the
linear primary sequence of a chosen protein, or within a
multiple sequence alignment of related RNA-binding
atomic- and residue-level interfacial information for both
the RNA and protein chains of complexes, providing
previously published reduced-redundancy data sets and
allowing users to make advanced queries and compile
custom data sets. Other collections of protein–RNA
*To whom correspondence should be addressed. Tel: +1 515 294 4991; Fax: +1 515 294 6790; Email: email@example.com
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
Published online 11 November 2010Nucleic Acids Research, 2011, Vol. 39, Database issueD277–D282
? The Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
complexes and related resources include NDB (http://
ndbserver.rutgers.edu/) (23), PRID (http://www-bioc.rice
.edu/?shamoo/prid.html) (24), RsiteDB (http://bioinfo3d
.cs.tau.ac.il/RsiteDB/) (25), w3DNA (http://w3dna.rutgers
.edu/) (26), NPIDB (http://monkey.belozersky.msu.ru/
NPIDB) (27), ProNIT (http://gibk26.bse.kyutech.ac.jp/
jouhou/pronit/pronit.html) (28) and the RNP Databases
http://rnp.uthct.edu/index.html/). Several excellent data-
bases of protein–DNA interfaces are also available,
including PDIdb (http://melolab.org/pdidb/) (29) and
Data extraction, interface definition and motif
Atomic coordinate information for all 926 protein–RNA
complexes in the Protein Data Bank (PDB) on 10 October
2010 was extracted using the REST API advanced
search interface. To generate this comprehensive data set
(rRB926), no filters based on sequence redundancy,
structure resolution or other criteria were applied (see
‘Non-redundant Benchmark data sets’ below). The
complex structures in rRB926 were then scanned to
identify interacting amino acids and ribonucleotides using
two different definitions: (i) a simple distance-based
definition in which a given amino acid residue (AA) in a
protein chain is defined as interacting with a ribonucleotide
(rNT) in an RNA chain if any atom in AA is within a 5-A˚
radius of any atom in rNT; and (ii) a rule-based definition
based on that of Allers and Shamoo (30), in which
interactions are classified as van der Waals, hydrogen-
involving specific AAs and rNTs. All such interacting
AAs and rNTs are defined as ‘interface’ residues.
ProSite patterns and profiles (31) appearing in any of
the protein sequences in the database were retrieved using
the ScanProsite REST service (32). RNA structural motifs
were identified in RNA sequences using FR3D’s (33) pure
symbolic search function; specific motif definitions used
for these scans are available in the Tutorial and FAQs
section of the PRIDB online server.
Non-redundant benchmark data sets
Because PRIDB is intended to be a comprehensive
collection of protein–RNA complexes from the PDB, the
rRB926 data set was not filtered on the basis of
redundancy, structure determination method, resolution
or protein/RNA chain length. While it is possible to
filter with such criteria using PRIDB’s advanced search
function, several pre-calculated benchmark data sets,
which have been filtered to limit redundancy and to
exclude low-resolution structures, are also provided for
the user’s convenience. These include two previously
published data sets, RB109 (17,34) and RB147 (35), as
well as a larger, more recently extracted data set
(RB199) (B. Lewis, submitted for publication). Complete
lists of the PDB IDs for protein–RNA complexes in these
data sets, in addition to the pre-calculated interface
residue statistics, can be readily accessed from the
‘Datasets’ section of the PRIDB homepage.
Implementation and availability
PRIDB runs on the Apache 2.2 web server, using MySQL
14.14 as a database backend with AJAX and PHP 5 for
user interface functions. Functions not requiring use of
the database (e.g. calculating interface residues for a user-
submitted complex) are implemented using standalone
Perl 5 scripts and the BioPerl module (36). All PRIDB
code is available on request under the Creative Commons
Attribution Non-Commercial License. All data currently
in PRIDB was obtained from databases or programs
which impose no restrictions on academic use.
PRIDB summary statistics
As summarized in Table 1, the current version of PRIDB
contains structural information for a total of 926 protein–
RNA complexes available in the PDB as of 10 October
2010. These structures contain 9689 total protein chains,
among which there are only 1174 unique sequences. While
this would seem to indicate that most sequences in the
database are repeated several times, this is not the case;
395 of the 1174 (34%) sequences appear only once, and
899 (77%) appear less than eight times (the ‘expected’
average redundancy). This disparity is due to the large
proportion of ribosomal structures in the PDB (and, by
extension, in PRIDB); 9 of the top 10 most abundant
sequences, each present in more than 70 structures, are
repeated more than 100 times, is that of the TRP-
responsive attenuation protein, a protein for which
numerous multimeric structures have been solved.
As shown in Table 2, PRIDB currently contains
1475774 amino acid residues. Based on a 5A˚
cutoff definition for interfacial residues, 397216 of these
residues interact with RNA; of 851853 ribonucleotide
On average, 38% of the amino acids in the RNA-binding
Table 1. PRIDB contents: complexes and chains
aTotal number in PRIDB includes redundant complexes, RNA and
protein chains (i.e. chains with identical sequences).
Table 2. PRIDB summary statistics
D278 Nucleic AcidsResearch, 2011, Vol.39, Database issue
proteins directly interact with RNA, and 28% of the
ribonucleotides in the bound RNAs directly interact with
protein. As before, these averages are skewed by the
prevalence of ribosome structures; ribosomal proteins
account for ?90% of interacting amino acid residues and
?60% of interacting nucleotides.
PRIDB provides a ‘Tutorial and FAQs’ section with
detailed instructions on using PRIDB’s web interface; a
list and brief descriptions of key capabilities of PRIDB are
provided here. Using the ‘Basic Search’ function, users can
retrieve information about protein–RNA complexes using
their PDB ID or a keyword. Using the ‘Advanced Search’
function, users can filter results by specifying:
. the experimental method used to determine the complex
structure (e.g. X-ray diffraction, nuclear magnetic
. a resolutionrange or
microscopy or fiber diffraction);
. the minimum or maximum length of protein or RNA
chains within the complex;
. an amino acid or nucleotide subsequence found within
the sequence of at least one of the protein or RNA
chains in the complex; and
Figure 1. Sample PRIDB output. Amino acid residues and ribonucleotides highlighted in yellow are located in the protein–RNA interface; residues
in red font are part of a ProSite or FR3D motif.
Nucleic Acids Research, 2011,Vol.39, Database issue D279
. a motif (as defined by ProSite for protein chains or
FR3D for RNA chains) found within at least one
chain in the complex.
The ‘Advanced Search’ function also allows users to
either specify a different distance cutoff for the distance-
based interaction definition or choose the alternative rule-
As shown in Figure 1, when viewing search results,
. a summary of and basic information (name, resolution
and structure determination method) about each
complex, as well as a link to that complex’s PDB entry;
. a linear display of the amino acid and nucleotide
residues in each chain of each complex, with residues
in the protein–RNA interface highlighted;
. a display of residues (in red font) that are part of a
protein or RNA motif, with information about that
motif (and a link back to its source) provided on
. a JMol applet for 3D visualization of each complex,
with interacting amino acid and nucleotide residues
colored (Figure 2A); and
. a link to a dynamically-generated file containing
atomic-level interface information for each result in a
machine readable format (Figure 2B).
In addition to providing machine-readable results
files for all searches, pre-computed results files for the
non-redundant RB109, RB147 and RB199 data sets
described above have been made available. These files,
along with the complete PRIDB database (rRB926), can
be downloaded from the ‘Datasets’ section of the website.
Users can also generate a machine-readable list of
interface residues for any arbitrary collection of complexes
by inputting a list of PDB IDs. Results files contain a
single line for each pair of interacting atoms listing
residue number and atom name) and the distance
Users may also calculate interface residues for protein–
RNA complexes that are not in PDB using PRIDB by
submitting a structure file in PDB format. A results file
containing interface residues (as calculated using PRIDB’s
5A˚cutoff) is returned via e-mail.
Figure 2. (A) PRIDB provides a JMol applet for visualizing and manipulating interfaces within 3-D structures. (B) PRIDB output can be
downloaded as a CSV file.
D280 Nucleic AcidsResearch, 2011, Vol.39, Database issue
CONCLUSIONS AND FUTURE DIRECTIONS
PRIDB provides researchers with atomic and residue-level
information about structures of protein–RNA complexes
and their interfaces, facilitating analyses of protein–RNA
information and by providing structural information
both interactively onscreen and in a machine-readable
format. It allows users to rapidly identify and visualize
interfaces in protein–RNA complexes on a residue-by-
residue basis and displays identified ProSite or FR3D
motifs along with the amino acid or ribonucleotide
sequences. PRIDB can be used to generate custom data
sets of protein–RNA interfaces for statistical analyses
and machine learning applications. The PRIDB server
also provides pre-calculated benchmark data sets of
protein–RNA complexes for evaluating the performance
of interface prediction methods. PRIDB will be updated
regularly as new structures are released through PDB, and
is intended to be a stable resource for researchers in the
field of protein–RNA interactions.
Future versions of PRIDB will include additional
protein and RNA motifs from other sources, such as
PRINTS (37), PIRSF (38) and other InterPro (39)
member databases. In addition, the current JMol 3D
structures, allowing for more facile manipulation and
examination of interfaces in complexes not currently in
The authors thank members of our research groups for
helpful discussions and especially Usha Muppirala for
critical comments on the PRIDB server and manuscript.
National Institutes of Health (GM066387 to V.H. and
[IGERT0504304 (to D.D.); GK120947929 (to B.A.L.);
NIBIB-NSF0608769 (to V.H., J.F. and C.Z.)]; Iowa
Computational Intelligence, Learning and Discovery (to
V.H.). Funding for open access charge: Center for
Computational Intelligence, Learning and Discovery.
Conflict of interest statement. None declared.
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