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

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

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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

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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/.

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Background
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Background
RBPPred: predicting RNA-binding proteins from sequence using SVM
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“On the other hand, several works, including RNABindRPlus [127] and RBRDetector [128], are attempting to incorporate both template-based and shallow-learning approaches to improve the performance.”
Background
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Background
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“…residues and which was recently shown to outperform other structure-trained predictors of protein interacting residues [59]; RNABindRPlus [50] that was ranked as the best tool in the recent assessment of the structure-trained predictors of the RNA interactions [45]; TargetDNA [52], one…”
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“Sequence-based methods such as SVMnuc ( 7 ), NCBRPred ( 8 ), DNAPred ( 9 ), DNA-genie ( 10 ), RNABindRPlus ( 11 ), ConSurf ( 12 ), TargetDNA ( 13 ), SCRIBER ( 3 ) and TargetS ( 14 ) exploit readily available and abundant protein sequence information to predict binding sites.”
Method
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Self-citation
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Background
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Retracted
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“The webservers assessed include BindN[8], BindN+[9], RNABindR[20], RNABindRPlus[21], DBS-Pred[12], DBS-PSSM[13], KYG[14], PRBR[22], PPRInt[23], DNABINDPROT[24], ProteDNA[25], DISPLAR[10], DR_bind1[26], aaRNA [27], RBscore[28], RBRDetector[29], DNABind[30], xypan[31] and RNAProSite (lilab.ecust.…”
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Background
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“A residue is defined as an RNA-binding site if there exists at least one atom in the protein with a distance cutoff < 5.0Å from an atom of the binding RNA [7, 9–11 , 14– 24 ].”
BackgroundMethodInfluential
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“The sequence-based methods identify the functional sites from the protein sequences, such as TargetS [10], RNABindRPlus [11], TargetDNA [5], DNAPred [12], DRNAPred [1], SVMnuc [13], NCBR-Pred [6], etc.”
Method
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“To our knowledge, only the HomPRIP and RNABindRPlus algorithms proposed by Wilia et al. [34] respectively utilize the template-based and hybrid strategies to recognize RNA-binding residues in protein sequence.”
BackgroundMethod
The HEX 110 Hexamerin Is a Cytoplasmic and Nucleolar Protein in the Ovaries of Apis mellifera
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“In silico analyses Potential RNA-binding sites were searched in the HEX 110 sequence using RNABindR, BindN, PPRint, SVM and RNABindRPlus softwares [36–40].”
Method
RNA-binding residues prediction using structural features
Ren, H., & Shen, Y. H. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0691-0 · Cited by 25
“Besides using sequence/structural features, certain methods predict RNA-binding residues directly from amino acid sequences [34, 38].”
Method
HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins
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“The 29 structure-trained predictors include (in chronological order) a method by Jeong et al. (34), BindN (35), a method by Jeong and Miyano (36), RNABindR (37), PRINTR (38), RISP (39), Pprint (40), RNAProB (41), BindN+ (42), NAPS (43), PiRaNhA (44), ProteRNA (45), RBRpred (46), PRNA (47), PRBR (48), a method by Choi…”
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“Other computational methods, such as RNAProB (14), BINDN+ (15) and RNABindRPLUS (16) have been developed to characterize RBPs by predicting RNA-binding residues derived from the known protein-RNA structures.”
Method
Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type
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“[31], aaRNA search against machine”
BackgroundInfluential
Computational tools to study RNA-protein complexes
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F/YGG-motif is an intrinsically disordered nucleic-acid binding motif
Lindt, J. V., Lázár, T., Pakravan, D., Demulder, M., Mészáros, A., Bosch, L. V. D., Maes, D., & Tompa, P. (2022) · RNA Biology · doi:10.1080/15476286.2022.2066336 · Cited by 22
“These prediction results are in consensus with those of RNAbindRplus [35] and DP-Bind [36].”
Result
Predicting protein-binding regions in RNA using nucleotide profiles and compositions
Choi, D., Park, B., Chae, H., Lee, W., & Han, K. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0386-4 · Cited by 22
“RNABindRPlus [5] also predicts RNA-binding residues in a protein sequence by combining predictions from an optimized SVM and those from a sequence homology method. aaRNA [6] predicts RNA binding residues in protein using sequence-and structure-based features.”
Method
In Silico Prediction and Validation of Novel RNA Binding Proteins and Residues in the Human Proteome
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“Consistent with analysis using catRAPID [19] and RNABindRPlus [20] programs, mutation of KH3, but not of KH1, attenuated the interaction of FUBP3 with GATA2-AS1 (Figs.”
Method
FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues
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“The dataset annotated with the identified interface residues is publicly available at: http://ailab1.ist.psu.edu/ RNABindRPlus/rb44.txt.”
BackgroundMethodResultInfluentialSelf-citation
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“(3) RB111 RB 111 has 111 protein chains that share less than 30% sequence similarity with those in RB44.(15) For all the datasets, an interface residue is defined as the one where at least one atom is closer than 5 Å to any atom of RNA.”
Background
A serum microRNA sequence reveals fragile X protein pathology in amyotrophic lateral sclerosis
Freischmidt, A., Goswami, A., Limm, K., Zimyanin, V. L., Demestre, M., Glaß, H., Holzmann, K., Helferich, A. M., Brockmann, S. J., Tripathi, P., et al. (2021) · Brain · doi:10.1093/brain/awab018 · Cited by 19
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Sun, X., Wu, Z., Su, J., & Li, C. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.136933 · Cited by 18
A generative model for constructing nucleic acid sequences binding to a protein
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Regulation of micro- and small-exon retention and other splicing processes by GRP20 for flower development
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“We used the catRAPID and RNAbindPlus programs to investigate whether GRP20 could potentially bind to RNA (Extended Data Fig.”
Influential
Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences
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A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond
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PRIME-3D2D is a 3D2D model to predict binding sites of protein–RNA interaction
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Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms
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SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues
Zhang, B., Hou, Z., Yang, Y., Wong, K., Zhu, H., & Li, X. (2024) · Communications Biology · doi:10.1038/s42003-024-06332-0 · Cited by 13
“Although we cannot directly obtain the data classified according to protein family from BioLip, we categorized the proteins in the BioLip test set based on the provided protein IDs by indexing in InterPro [5], thereby classifying the proteins according to their protein families.”
VirF Relieves the Transcriptional Attenuation of the Virulence Gene icsA of Shigella flexneri Affecting the icsA mRNA–RnaG Complex Formation
Giangrossi, M., Giuliodori, A. M., Tran, C. N., Amici, A. R. D., Marchini, C., & Falconi, M. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.00650 · Cited by 13
RBRIdent: An algorithm for improved identification of RNA-binding residues in proteins from primary sequences
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“11–14 The former is obviously more valuable because of the lack of requirement on structural information, considering the fact that the sequence data of proteins are accumulating in a much faster rate than structures deposited in the database.”
Background
RBscore&NBench: a high-level web server for nucleic acid binding residues prediction with a large-scale benchmarking database
Miao, Z., & Westhof, É. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw251 · Cited by 12
“Hopefully, later development of new nucleic acid binding site prediction programs can take the advantage of NBench database and make direct comparison with the existing 25 approaches to avoid unnecessary bias of dataset, binding site definition or assessment metrics.”
Background
A rice seed-specific glycine-rich protein OsDOR1 interacts with GID1 to repress GA signaling and regulates seed dormancy
Kim, S., Huh, S. M., Han, H. J., Lee, G., Hwang, Y., Cho, M. H., Kim, B., Song, J. S., Chung, J. H., Nam, M. H., et al. (2023) · Plant Molecular Biology · doi:10.1007/s11103-023-01343-7 · Cited by 12
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Sequence-Based Prediction of RNA-Binding Residues in Proteins
Walia, R. R., EL‐Manzalawy, Y., Honavar, V., & Dobbs, D. L. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6406-2_15 · Cited by 11
Self-citation
Discovering protein-binding RNA motifs with a generative model of RNA sequences
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Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions
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Integrative characterization of MYC RNA-binding function
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“To identify the domain of MYC that mediates MYC-RNA interaction, we performed RNA-binding residue (RNABindRPlus) 33 analysis for MYC and comparative analysis of MYC protein sequences across multiple species.”
New Insights into Phase Separation Processes and Membraneless Condensates of EIN2
Lu, J., Wen, C., & Groth, G. (2022) · Plants · doi:10.3390/plants11162149 · Cited by 9
“RNABindRPlus [25]—a hybrid machine learning sequence homology-based approach—confirms that EIN2-C may have several putative RNA-binding sites.”
Background
econvRBP: Improved ensemble convolutional neural networks for RNA binding protein prediction directly from sequence
Zhao, Y., & Du, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.09.008 · Cited by 9
DeepMVF-RBP: Deep Multi-view Fusion Representation Learning for RNA-binding Proteins Prediction
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“For example, a number of computational approaches, such as BINDN+ [1] and RNABindRPLUS [2] have been put forwarded to characterize RBPs by predicting RNA-binding residues.”
Regulatory roles of RNA binding proteins in the Hippo pathway
Peng, S., Li, C., He, Y., Xue, L., & Guo, X. (2025) · Cell Death Discovery · doi:10.1038/s41420-025-02316-z · Cited by 8
Self-citation
A Novel Prediction Method for ATP-Binding Sites From Protein Primary Sequences Based on Fusion of Deep Convolutional Neural Network and Ensemble Learning
Song, J., Liang, Y., Liu, G., Wang, R., Sun, L., & Zhang, P. (2020) · IEEE Access · doi:10.1109/access.2020.2968847 · Cited by 8
“It has been observed that if two protein sequences share similar amino acid compositions, which means they are homologous, the similar segments in their sequences are likely to have some common functions [21].”
BackgroundMethod
A MOTIF-BASED METHOD FOR PREDICTING INTERFACIAL RESIDUES IN BOTH THE RNA AND PROTEIN COMPONENTS OF PROTEIN-RNA COMPLEXES
Muppirala, U. K., Lewis, B. A., Mann, C. M., & Dobbs, D. L. (2015) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814749411_0041 · Cited by 8
Self-citation
Individually double minimum-distance definition of protein–RNA binding residues and application to structure-based prediction
Hu, W., Liu, Q., Li, M., Pu, X., & Guo, Y. (2018) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-018-0177-z · Cited by 8
SPHot: Prediction of Hot Spots in Protein-RNA Complexes by Protein Sequence Information and Ensemble Classifier
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Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites
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Efficient mapping of RNA‐binding residues in RNA‐binding proteins using local sequence features of binding site residues in protein‐RNA complexes
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“Similarly, on PR111 dataset, we obtain 3% rise in MCC compared to RNABindRplus.(84) On PR205 dataset, compared to PRNA,(85) we have achieved slightly higher accuracy, however large drop in Fscore is observed.”
Method
Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction
Wang, B., & Li, W. (2024) · Genes · doi:10.3390/genes15081090 · Cited by 7
“In the early stage, sequence-based methods encompass a variety of tools, including NCBRPred [10], DNAPred [11], DNAgenie [12], RNABindRPlus [13], ProNA2020 [14], ConSurf [15], TargetDNA [16], SCRIBER [17], and TargetS [18], which predict residues at nucleic acid binding sites using information…”
Method
Partner‐specific prediction of RNA‐binding residues in proteins: A critical assessment
Jung, Y. G., EL‐Manzalawy, Y., Dobbs, D. L., & Honavar, V. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25639 · Cited by 8
Self-citation
Cytoplasmic PXR regulates glucose metabolism by binding mRNAs and modulating their stability
Wang, X., Wang, Z., Li, S., Pattarayan, D., Wang, Y., Zhai, J., Zhang, Y. L., Wang, H., Xu, M., Zhu, J., et al. (2025) · Nature Structural & Molecular Biology · doi:10.1038/s41594-025-01614-5 · Cited by 5
pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data
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“…RNA/ligand-binding sites on the proteins of study, we chose six different prediction algorithms: aaRNA, PST-PRNA, BindUP, FTMap, RNABindRPlus, and DisoRDPbind (Mehio et al, 2010; Walia et al, 2014; Peng & Kurgan, 2015; Paz et al, 2016; Li & Liu, 2022) (see Table 1 for an overview of the tools).”
Influential
A novel sequence-based prediction method for ATP-binding sites using fusion of SMOTE algorithm and random forests classifier
Song, J., Liu, G., Song, C., & Jiang, J. (2020) · Biotechnology & Biotechnological Equipment · doi:10.1080/13102818.2020.1840436 · Cited by 5
PredRBR: Accurate Prediction of RNA-Binding Residues in proteins using Gradient Tree Boosting
Liu, D., Tang, Y., Fan, C. H., Chen, Z., & Deng, L. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822491 · Cited by 5
“Comparing with seven existing RNA binding site prediction methods, including SNBRFinder, RNABindRPlus, BindN, PPRint, Liu2010, BindN+ and RNABindR2.”
MethodInfluential
Structural comparison of homologous protein-RNA interfaces reveals widespread overall conservation contrasted with versatility in polar contacts
Mahmoudi, I., Quignot, C., Martins, C., & Andreani, J. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012650 · Cited by 4
Predicting nucleic acid binding sites by attention map-guided graph convolutional network with protein language embeddings and physicochemical information
Li, X., Peng, W., & Zhu, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf457 · Cited by 4
iNucRes‐ASSH : Identifying nucleic acid‐binding residues in proteins by using self‐attention‐based structure‐sequence hybrid neural network
Zhang, J., Chen, Q., & Liu, B. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26626 · Cited by 4
Advanced computational tools for quantitative analysis of protein–nucleic acid interfaces
Mukherjee, S., & Nithin, C. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90264-9.00011-8 · Cited by 5
PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences
Li, Y., Lyu, J., Wu, Y., Liu, Y., & Huang, G. (2022) · Life · doi:10.3390/life12020307 · Cited by 4
“Y.W. and Y.L. (Yuewu Liu) conceived the methodology; Y.L. (You Li) collected the dataset, implemented RNA–protein interactions play key roles in the regulation of many cellular processes and are increasingly becoming a hot topic.”
Method
A convolutional network and attention mechanism-based approach to predict protein–RNA binding residues
Li, K., Wu, H., Yue, Z., Sun, Y., & Xia, C. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107901 · Cited by 4
Integrative structural analysis of NF45–NF90 heterodimers reveals architectural rearrangements and oligomerization on binding dsRNA
Winterbourne, S., Jayachandran, U., Zou, J., Rappsilber, J., Granneman, S., & Cook, A. G. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf204 · Cited by 3
“Predictions were downloaded for the prediction algorithms BindUp [ 55 ], FTMap [ 56 ], RNABindRPlus [ 57 ], DisoRDPbind [ 58 ], and PST-PRNA [ 59 ].”
GATBind: Accurate protein-RNA binding sites prediction via graph attention networks with pre-trained language model
Shen, S., Wu, S., Chen, J., Huang, Z., & Deng, L. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821999 · Cited by 2

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: Walia, Rasna R.
Xue, Li C.
Dobbs, Drena
Honavar, Vasant
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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
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

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

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