CoChat
Sign Up
Cited by 132Machine Learning in BioinformaticsProtein Structure and DynamicsRNA Research and Splicing

The DOI matches its Crossref record; 71 of the 72 references it cites were found in a registry or an index.

RNABindRPlus: A Predictor that Combines Machine Learning and Sequence Homology-Based Methods to Improve the Reliability of Predicted RNA-Binding Residues in Proteins

Open access
Walia, Rasna R., Xue, Li C., Wilkins, Katherine, El-Manzalawy, Yasser, Dobbs, Drena, Honavar, Vasant·2014·PLoS ONE
DOI 10.1371/journal.pone.0097725PMC PMC4028231
Open this paper in CoChat — free accountOpen PDF

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/.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0097725

Cites

71 of 71 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Principal Components Analysis (PCA) of interface conservation scores and sequence alignment statistics. Data points in the plot correspond to the projection of a 6-dimensional vector representing the pairwise alignment of a query and homolog sequence onto a 2-dimensional space defined by the first and second principal components. Blue lines with red circles at their tips represent the axes of the original 6-dimensional space for the 6 variables used in PCA analysis: -log(E) (where is the -value), Identity Score (), Positive Score (), log(L) (where is local alignment length), alignment length fractions ( and , where and are the lengths of the query and homolog proteins, respectively). Each data point is colored according to its computed score, with higher score (red/orange) indicating higher interface conservation and lower scores (blue/green) indicating lower interface conservation. The large gray arrow indicates the direction of increasing degree of interface conservation, from Dark to Twilight to Safe Zone.
Figure 1 Principal Components Analysis (PCA) of interface conservation scores and sequence alignment statistics. Data points in the plot correspond to the projection of a 6-dimensional vector representing the pairwise alignment of a query and homolog sequence onto a 2-dimensional space defined by the first and second principal components. Blue lines with red circles at their tips represent the axes of the original 6-dimensional space for the 6 variables used in PCA analysis: -log(E) (where is the -value), Identity Score (), Positive Score (), log(L) (where is local alignment length), alignment length fractions ( and , where and are the lengths of the query and homolog proteins, respectively). Each data point is colored according to its computed score, with higher score (red/orange) indicating higher interface conservation and lower scores (blue/green) indicating lower interface conservation. The large gray arrow indicates the direction of increasing degree of interface conservation, from Dark to Twilight to Safe Zone.CC BY · PubMed Central
Figure 2 RNABindRPlus flowchart. Flowchart showing the different components of RNABindRPlus.
Figure 2 RNABindRPlus flowchart. Flowchart showing the different components of RNABindRPlus.CC BY · PubMed Central
Figure 3 PDB ID: 3NCU, Chain A: RIG-I. (A) Actual interface residues, (B) Predictions made by HomPRIP, (C) Predictions made by SVMOpt, and (D) Predictions made by RNABindRPlus.
Figure 3 PDB ID: 3NCU, Chain A: RIG-I. (A) Actual interface residues, (B) Predictions made by HomPRIP, (C) Predictions made by SVMOpt, and (D) Predictions made by RNABindRPlus.CC BY · PubMed Central
Figure 4 Comparison of SVMOpt, RNABindRPlus, and the Metapredictor on the RB44 dataset using (A) ROC curves and (B) PR curves with a 5 Å distance cut-off for interface residues.
Figure 4 Comparison of SVMOpt, RNABindRPlus, and the Metapredictor on the RB44 dataset using (A) ROC curves and (B) PR curves with a 5 Å distance cut-off for interface residues.CC BY · PubMed Central
Figure 5 Comparison of SVMOpt, RNABindRPlus, RNABindR v2.0, BindN, BindN+ and PPRInt on the RB111 dataset using (A) ROC curves and (B) PR curves with a 5 Å distance cut-off for interface residues.
Figure 5 Comparison of SVMOpt, RNABindRPlus, RNABindR v2.0, BindN, BindN+ and PPRInt on the RB111 dataset using (A) ROC curves and (B) PR curves with a 5 Å distance cut-off for interface residues.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 72 references

Cited by

Sorted by citations

See all 132

Related

Metadata

Authors
Walia, R. R., Xue, L. C., Wilkins, K., El-Manzalawy, Y., Dobbs, D., & Honavar, V.
Affiliations
  • Iowa State University (US)
  • Pennsylvania State University (US)
  • Cornell University (US)
Corresponding author
Walia, Rasna R.
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2014-05-20
Volume
9
Issue
5
Pages
e97725
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0097725
PMC
PMC4028231
OpenAlex
W2116498086
Cited by
132
References
72
Citations by year
2026: 14 · 2025: 14 · 2024: 14 · 2023: 11 · 2022: 8 · 2021: 10 · 2020: 11 · 2019: 10 · 2018: 14 · 2017: 8 · 2016: 8 · 2015: 10
FWCI
3.55
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Iowa State University Digital Repository (Iowa State University) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Science Foundation (0504304)
  • Iowa State University
  • National Institutes of Health (GM066387)
  • National Institute of General Medical Sciences (R33GM066387, GM 066387, R21GM066387)
  • Division of Graduate Education
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Keywords
RNA-binding residues, RNA-binding proteins, RNA-protein interactions, RNA-binding site prediction, support vector classifier, sequence homology, sequence-derived features, protein-RNA interface, web server
MeSH terms
Animals, Artificial Intelligence, Humans, Models, Theoretical, RNA-Binding Proteins, Sequence Analysis, RNA, Sequence Analysis, Protein

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review