RNABindRPlus: A Predictor that Combines Machine Learning and Sequence Homology-Based Methods to Improve the Reliability of Predicted RNA-Binding Residues in Proteins
Open accessAbstract
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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PSSM has been proven to provide crucial information in related topics such as binding site predictions and hot-spot predictions.
“PSSM has been proven to provide crucial information in various related topics, such as binding site predictions37 and hot‐spot predictions.38”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [37]
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Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302 · cited 3× · see citing works
“The PSSMs were constructed by running PSI-BLAST [61] against the NCBI nr database for three iterations with an E-value cutoff of 0.001 for inclusion in the next iteration.”
BackgroundMethodInfluentialBerman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
Cd-hit: Accelerated for clustering the next-generation sequencing data
Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts565 · Cited by 11,175 · see citing works
Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences
Li, W.-Z., & Godzik, A. (2006) · Bioinform. · doi:10.1093/bioinformatics/btl158 · Cited by 10,994 · see citing works
Non-coding RNAs in human disease
Esteller, M. (2011) · Nature Reviews Genetics · doi:10.1038/nrg3074 · Cited by 4,825 · cited 1× · see citing works
“Recent evidence points to the role of non-coding RNAs (ncRNAs) in a number of human diseases [9–12] such as Alzheimer’s [13,14] and various cancers [15–18].”
BackgroundROCR: visualizing classifier performance in R
Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti623 · Cited by 3,205 · see citing works
Comparative Protein Structure Modeling of Genes and Genomes
Martí-Renom, M. A., Stuart, A. C., Fiser, A., Sánchez, R., Melo, F., & Šali, A. (2000) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.29.1.291 · Cited by 3,169 · see citing works
Berman, H. M., Battistuz, T., Bhat, T. N., Bluhm, W. F., Bourne, P. E., Burkhardt, K., Feng, Z., Gilliland, G. L., Iype, L. E., Jain, S. C., et al. (2002) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444902003451 · Cited by 2,578 · see citing works
Assessing the accuracy of prediction algorithms for classification: an overview
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.5.412 · Cited by 2,111 · cited 1× · see citing works
“[68].”
Ridge Estimators in Logistic Regression
le Cessie, S., & van Houwelingen, H. C. (1992) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2347628 · Cited by 1,689 · see citing works
PISCES: a protein sequence culling server
Wang, G., & Dunbrack, R. L. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg224 · Cited by 1,664 · see citing works
Electrophoretic mobility shift assay (EMSA) for detecting protein–nucleic acid interactions
Hellman, L. M., & Fried, M. G. (2007) · Nature Protocols · doi:10.1038/nprot.2007.249 · Cited by 1,214 · see citing works
Alternative Splicing: New Insights from Global Analyses
Blencowe, B. J. (2006) · Cell · doi:10.1016/j.cell.2006.06.023 · Cited by 1,122 · cited 1× · see citing works
“Protein-RNA interactions play key roles in many vital cellular processes including translation [1,2], post-transcriptional regulation of gene expression [3,4], RNA splicing [5,6], and viral replication [7,8].”
BackgroundLong noncoding RNAs and the genetics of cancer
Cheetham, S. W., Gruhl, F., Mattick, J. S., & Dinger, M. E. (2013) · British Journal of Cancer · doi:10.1038/bjc.2013.233 · Cited by 724 · cited 1× · see citing works
“Recent evidence points to the role of non-coding RNAs (ncRNAs) in a number of human diseases [9–12] such as Alzheimer’s [13,14] and various cancers [15–18].”
BackgroundLong Intergenic Noncoding RNAs: New Links in Cancer Progression
Tsai, M.-C., Spitale, R. C., & Chang, H. Y. (2011) · Cancer Research · doi:10.1158/0008-5472.can-10-2483 · Cited by 641 · cited 1× · see citing works
“Recent evidence points to the role of non-coding RNAs (ncRNAs) in a number of human diseases [9–12] such as Alzheimer’s [13,14] and various cancers [15–18].”
BackgroundCLIP: A method for identifying protein–RNA interaction sites in living cells
Ule, J., Jensen, K. B., Mele, A., & Darnell, R. B. (2005) · Methods · doi:10.1016/j.ymeth.2005.07.018 · Cited by 597 · cited 1× · see citing works
“Because of the cost and effort involved in the experimental determination of protein-RNA complex structures [20,21] and RNA-binding sites in proteins [22,23], considerable effort has been directed at developing reliable computational methods for predicting RNAbinding residues in proteins.”
MethodMatthews, L. R., Vaglio, P., Reboul, J., Ge, H., Davis, B. P., Garrels, J. I., Vincent, S., & Vidal, M. (2001) · Genome Research · doi:10.1101/gr.205301 · Cited by 482 · see citing works
Large non-coding RNAs: missing links in cancer?
Huarte, M., & Rinn, J. L. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq353 · Cited by 480 · see citing works
The dependence of viral RNA replication on co-opted host factors
Nagy, P. D., & Pogany, J. (2011) · Nature Reviews Microbiology · doi:10.1038/nrmicro2692 · Cited by 447 · see citing works
Wang, L., & Brown, S. J. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl298 · Cited by 417 · cited 2× · see citing works
“BindN [34] An SVM classifier that uses hydrophobicity, side chain pKa, molecular mass and PSSMs for predicting RNA-binding residues.”
MethodGrigull, J., Mnaimneh, S., Pootoolal, J., Robinson, M. D., & Hughes, T. R. (2004) · Molecular and Cellular Biology · doi:10.1128/mcb.24.12.5534-5547.2004 · Cited by 342 · cited 1× · see citing works
“Protein-RNA interactions play key roles in many vital cellular processes including translation [1,2], post-transcriptional regulation of gene expression [3,4], RNA splicing [5,6], and viral replication [7,8].”
BackgroundTadros, W., Goldman, A. L., Babak, T., Menzies, F., Vardy, L., Orr-Weaver, T., Hughes, T. R., Westwood, J. T., Smibert, C. A., & Lipshitz, H. D. (2007) · Developmental Cell · doi:10.1016/j.devcel.2006.10.005 · Cited by 330 · cited 1× · see citing works
“Protein-RNA interactions play key roles in many vital cellular processes including translation [1,2], post-transcriptional regulation of gene expression [3,4], RNA splicing [5,6], and viral replication [7,8].”
BackgroundPrediction of RNA binding sites in a protein using SVM and PSSM profile
Kumar, M., Gromiha, M. M., & Raghava, G. P. S. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21677 · Cited by 284 · see citing works
Konc, J., & Janežič, D. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq100 · Cited by 275 · see citing works
Wang, Y., Ludwig, J., Schuberth, C., Goldeck, M., Schlee, M., Li, H., Juranek, S., Sheng, G., Micura, R., Tuschl, T., et al. (2010) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1863 · Cited by 256 · see citing works
Dissecting the expression landscape of RNA-binding proteins in human cancers
Kechavarzi, B., & Janga, S. C. (2014) · Genome biology · doi:10.1186/gb-2014-15-1-r14 · Cited by 242 · see citing works
BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features
Wang, L., Huang, C., Yang, M. Q., & Yang, J. Y. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-s1-s3 · Cited by 214 · see citing works
A Virological View of Innate Immune Recognition
Iwasaki, A. (2012) · Annual Review of Microbiology · doi:10.1146/annurev-micro-092611-150203 · Cited by 213 · cited 1× · see citing works
“RNA-protein interactions play key roles in the innate immune system in mammals, which is the first line of defense against invading viral and bacterial pathogens [57].”
BackgroundRNABindR: a server for analyzing and predicting RNA-binding sites in proteins
Terribilini, M., Sander, J. D., Lee, J., Zaback, P., Jernigan, R. L., Honavar, V., & Dobbs, D. L. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm294 · Cited by 186 · see citing works
Self-citationPrediction of RNA binding sites in proteins from amino acid sequence
Terribilini, M., Lee, J., Yan, C., Jernigan, R. L., Honavar, V., & Dobbs, D. L. (2006) · RNA · doi:10.1261/rna.2197306 · Cited by 180 · see citing works
Self-citationProtein-Protein Complex Structure Predictions by Multimeric Threading and Template Recombination
Mukherjee, S., & Zhang, Y. (2011) · Structure · doi:10.1016/j.str.2011.04.006 · Cited by 174 · cited 1× · see citing works
“Homology-based methods have proven successful in many bioinformatics tasks, including protein structure prediction [42], protein function annotation [43,44], protein interaction prediction [45], protein-protein docking [46,47] and protein-protein interface prediction, based on either sequence homology [48] or structur…”
MethodA Central Role for Long Non-Coding RNA in Cancer
Mitra, S. A., Mitra, A. P., & Triche, T. J. (2012) · Frontiers in Genetics · doi:10.3389/fgene.2012.00017 · Cited by 173 · cited 1× · see citing works
“Recent evidence points to the role of non-coding RNAs (ncRNAs) in a number of human diseases [9–12] such as Alzheimer’s [13,14] and various cancers [15–18].”
BackgroundNon-coding RNAs in Alzheimer's Disease
Tan, L., Yu, J.-T., Hu, N., & Tan, L. (2012) · Molecular Neurobiology · doi:10.1007/s12035-012-8359-5 · Cited by 171 · see citing works
Crystallization of RNA and RNA?protein complexes
Ke, A. (2004) · Methods · doi:10.1016/j.ymeth.2004.03.027 · Cited by 171 · cited 1× · see citing works
“Because of the cost and effort involved in the experimental determination of protein-RNA complex structures [20,21] and RNA-binding sites in proteins [22,23], considerable effort has been directed at developing reliable computational methods for predicting RNAbinding residues in proteins.”
MethodProtein interface conservation across structure space
Zhang, Q. C., Petrey, D., Norel, R., & Honig, B. H. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1005894107 · Cited by 156 · cited 1× · see citing works
“Homology-based methods have proven successful in many bioinformatics tasks, including protein structure prediction [42], protein function annotation [43,44], protein interaction prediction [45], protein-protein docking [46,47] and protein-protein interface prediction, based on either sequence homology [48] or structur…”
MethodPRIDB: a protein-RNA interface database
Lewis, B. A., Walia, R. R., Terribilini, M., Ferguson, J., Zheng, C. Y., Honavar, V., & Dobbs, D. L. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1108 · Cited by 147 · cited 8× · see citing works
“HomPRIP, our sequence homology-based protein-RNA interface predictor operates as follows: Given a query protein Q, HomPRIP uses a BLAST search against the proteins in the Protein-RNA Interface Database [56], PRIDB, to identify a set of sequence homologs of Q, Homologs(Q), with known RNA-binding residues.”
InfluentialSelf-citationRNA–protein interactions in human health and disease
Khalil, A. M., & Rinn, J. L. (2011) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2011.02.016 · Cited by 138 · cited 1× · see citing works
“Recent evidence points to the role of non-coding RNAs (ncRNAs) in a number of human diseases [9–12] such as Alzheimer’s [13,14] and various cancers [15–18].”
BackgroundmiRNAs and long noncoding RNAs as biomarkers in human diseases
Roosbroeck, K. V., Pollet, J. B. K., & Călin, G. A. (2013) · Expert Review of Molecular Diagnostics · doi:10.1586/erm.12.134 · Cited by 135 · see citing works
Predicting RNA-binding sites of proteins using support vector machines and evolutionary information
Cheng, C.-W., Su, E. C., Hwang, J.-K., Sung, T.-Y., & Hsu, W.-L. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-s12-s6 · Cited by 131 · cited 9× · see citing works
“A recent study [24] compared the performance of Naı̈ve Bayes (NB) and Support Vector Machine (SVM) classifiers trained to predict RNA-binding residues of proteins, from features of a sliding window of 25 amino acid residues centered on the target residue, using three different sequence-based feature representations (a…”
InfluentialKim, O. T. P., Yura, K., & Gō, N. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl819 · Cited by 121 · cited 7× · see citing works
“KYG [37] Uses a set of scores based on the RNA-binding propensity of individual and pairs of surface residues of the protein, used alone or in combination with position-specific multiple sequence profiles.”
MethodInfluentialZehetner, G. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg555 · Cited by 119 · cited 1× · see citing works
“Homology-based methods have proven successful in many bioinformatics tasks, including protein structure prediction [42], protein function annotation [43,44], protein interaction prediction [45], protein-protein docking [46,47] and protein-protein interface prediction, based on either sequence homology [48] or structur…”
MethodZhao, H., Yang, Y., & Zhou, Y. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1266 · Cited by 112 · see citing works
Computational methods for prediction of protein–RNA interactions
Puton, T., Kozlowski, L. P., Tuszyńska, I., Rother, K. M., & Bujnicki, J. M. (2011) · Journal of Structural Biology · doi:10.1016/j.jsb.2011.10.001 · Cited by 110 · cited 25× · see citing works
“The Metapredictor [19], PiRaNhA [60], and NAPS [25] servers were all inaccessible at the time of running the experiments on RB111.”
BackgroundMethodResultInfluentialRegulation of translation by specific protein/mRNA interactions
Standart, N., & Jackson, R. J. (1994) · Biochimie · doi:10.1016/0300-9084(94)90189-9 · Cited by 107 · cited 1× · see citing works
“Protein-RNA interactions play key roles in many vital cellular processes including translation [1,2], post-transcriptional regulation of gene expression [3,4], RNA splicing [5,6], and viral replication [7,8].”
BackgroundPredUs: a web server for predicting protein interfaces using structural neighbors
Zhang, Q. C., Deng, L., Fisher, M., Guan, J., Honig, B. H., & Petrey, D. S. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr311 · Cited by 104 · cited 1× · see citing works
“Homology-based methods have proven successful in many bioinformatics tasks, including protein structure prediction [42], protein function annotation [43,44], protein interaction prediction [45], protein-protein docking [46,47] and protein-protein interface prediction, based on either sequence homology [48] or structur…”
MethodHomPPI: a class of sequence homology based protein-protein interface prediction methods
Xue, L. C., Dobbs, D. L., & Honavar, V. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-244 · Cited by 104 · cited 3× · see citing works
“[48], we define an interface conservation score IC(Q,H) that measures the correlation between the interface (and non-interface) residues of a query protein Q and its putative sequence homolog H when the two are aligned (see Methods for details).”
BackgroundMethodSelf-citationSeeking Membranes: Positive-Strand RNA Virus Replication Complexes
Denison, M. R. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060270 · Cited by 97 · cited 1× · see citing works
“Protein-RNA interactions play key roles in many vital cellular processes including translation [1,2], post-transcriptional regulation of gene expression [3,4], RNA splicing [5,6], and viral replication [7,8].”
BackgroundPredicting protein-protein interface residues using local surface structural similarity
Jordan, R. A., EL‐Manzalawy, Y., Dobbs, D. L., & Honavar, V. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-41 · Cited by 92 · see citing works
Self-citationOptimal protein‐RNA area, OPRA: A propensity‐based method to identify RNA‐binding sites on proteins
Pérez‐Cano, L., & Fernández‐Recio, J. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22527 · Cited by 92 · cited 1× · see citing works
“OPRA [39] Uses patch energy scores calculated using interface propensity scores weighted by the accessible surface area of a residue to predict RNA-binding sites.”
MethodWalia, R. R., Caragea, C., Lewis, B. P., Towfic, F., Terribilini, M., EL‐Manzalawy, Y., Dobbs, D. L., & Honavar, V. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-89 · Cited by 87 · see citing works
Self-citationNAPS: a residue-level nucleic acid-binding prediction server
Carson, M. B., Langlois, R. E., & Lu, H. J. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq361 · Cited by 84 · cited 1× · see citing works
“The Metapredictor [19], PiRaNhA [60], and NAPS [25] servers were all inaccessible at the time of running the experiments on RB111.”
MethodA neural network method for identification of RNA-interacting residues in protein.
Jeong, E., Chung, I., & Miyano, S. (2004) · PubMed · Cited by 70 · see citing works
PiRaNhA: a server for the computational prediction of RNA-binding residues in protein sequences
Murakami, Y., Spriggs, R. V., Nakamura, H., & Jones, S. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq474 · Cited by 68 · cited 7× · see citing works
“The Metapredictor [19], PiRaNhA [60], and NAPS [25] servers were all inaccessible at the time of running the experiments on RB111.”
MethodInfluentialMa, X., Guo, J., Wu, J., Liu, H., Yu, J., Xie, J., & Sun, X. S. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22958 · Cited by 66 · see citing works
Decoding the non-coding RNAs in Alzheimer’s disease
Schönrock, N., & Götz, J. (2012) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-012-1125-z · Cited by 60 · see citing works
Exploiting structural and topological information to improve prediction of RNA-protein binding sites
Maetschke, S., & Yuan, Z. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-341 · Cited by 56 · cited 3× · see citing works
“0 [24]) and two structure-based methods (KYG [37] and PRIP [38]).”
MethodProtein function annotation from sequence: prediction of residues interacting with RNA
Spriggs, R. V., Murakami, Y., Nakamura, H., & Jones, S. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp257 · Cited by 54 · see citing works
Molecular mechanisms of viral inhibitors of RIG-I-like receptors
Leung, D. W., Basler, C. F., & Amarasinghe, G. K. (2012) · Trends in Microbiology · doi:10.1016/j.tim.2011.12.005 · Cited by 48 · see citing works
Struct-NB: predicting protein-RNA binding sites using structural features
Towfic, F., Caragea, C., Gemperline, D. C., Dobbs, D. L., & Honavar, V. (2010) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2010.030965 · Cited by 41 · cited 1× · see citing works
“Structure-based methods [37–41] have used features such as amino acid doublet propensities of surface residues, geometry (patches or clefts) of the protein surface, roughness, and atomic protrusion (CX) values, to make predictions of RNA-binding residues in proteins.”
MethodSelf-citationStructure Determination of Protein⧸RNA Complexes by NMR
Wu, H., Finger, L. D., & Feigon, J. F. (2005) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(05)94022-6 · Cited by 38 · cited 1× · see citing works
“Because of the cost and effort involved in the experimental determination of protein-RNA complex structures [20,21] and RNA-binding sites in proteins [22,23], considerable effort has been directed at developing reliable computational methods for predicting RNAbinding residues in proteins.”
MethodIdentification of RNA-binding sites in proteins by integrating various sequence information
Wang, C., Fang, Y., Xiao, J., & Li, M. (2010) · Amino Acids · doi:10.1007/s00726-010-0639-7 · Cited by 36 · cited 1× · see citing works
“Sequence-based methods [25–36] have exploited features such as amino acid sequence identity, physicochemical properties of amino acids, predicted solvent accessibility, positionspecific scoring matrices (PSSMs), and interface propensities, among others.”
MethodXue, L. C., Jordan, R. A., EL‐Manzalawy, Y., Dobbs, D. L., & Honavar, V. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24370 · Cited by 30 · see citing works
Self-citationA Weighted Profile Based Method for Protein-RNA Interacting Residue Prediction
Jeong, E., & Miyano, S. (2006) · Lecture notes in computer science · doi:10.1007/11732488_11 · Cited by 28 · cited 1× · see citing works
“Sequence-based methods [25–36] have exploited features such as amino acid sequence identity, physicochemical properties of amino acids, predicted solvent accessibility, positionspecific scoring matrices (PSSMs), and interface propensities, among others.”
MethodKauffman, C., & Karypis, G. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp561 · Cited by 27 · cited 1× · see citing works
“Homology-based methods have been shown to outperform other methods whenever close sequence or structural homologs of query proteins (used as templates) can be reliably identified [48,49,53].”
BackgroundAssessing the Performance of Macromolecular Sequence Classifiers
Caragea, C., Sinapov, J., Honavar, V., & Dobbs, D. L. (2007) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2007.4375583 · Cited by 20 · cited 1× · see citing works
“Sequence-based cross-validation has been shown to be more rigorous than windowbased cross-validation [67], because it ensures disjoint training and test sets at the sequence level instead of at the residue level.”
BackgroundSelf-citationPrediction of RNA-Binding Residues in Protein Sequences Using Support Vector Machines
Wang, L., & Brown, S. J. (2006) · doi:10.1109/iembs.2006.260025 · Cited by 20 · cited 1× · see citing works
“Sequence-based methods [25–36] have exploited features such as amino acid sequence identity, physicochemical properties of amino acids, predicted solvent accessibility, positionspecific scoring matrices (PSSMs), and interface propensities, among others.”
MethodPosition-specific annotation of protein function based on multiple homologs.
Andrade-Navarro, M. A. (1999) · PubMed · Cited by 17 · see citing works
High-throughput assays probing protein–RNA interactions of eukaryotic translation initiation factors
Galicia-Vázquez, G., Lindqvist, L., Wang, X., Harvey, I., Liu, J., & Pelletier, J. (2008) · Analytical Biochemistry · doi:10.1016/j.ab.2008.09.037 · Cited by 12 · see citing works
RNA-binding residues in sequence space: Conservation and interaction patterns
Spriggs, R. V., & Jones, S. R. (2009) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2009.07.012 · Cited by 9 · see citing works
Modelling the evolutionarily possible
Casci, T. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2493 · Cited by 2 · see citing works
Counting, coordinating and controlling the alternatives
Muers, M. R. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2494 · Cited by 1 · see citing works
Flagged first, then by citations
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Cited by
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A circular RNA promotes tumorigenesis by inducing c-myc nuclear translocation
Yang, Q., Du, W. W., Wu, N., Yang, W., Awan, F. M., Fang, L., Ma, J., Li, X., Zeng, Y., Yang, Z., et al. (2017) · Cell Death and Differentiation · doi:10.1038/cdd.2017.86 · Cited by 329 · see citing works
Chen, Y., Yang, F., Fang, E., Xiao, W., Mei, H., Li, H., Li, D., Song, H., Wang, J., Mei, H., et al. (2018) · Cell Death and Differentiation · doi:10.1038/s41418-018-0220-6 · Cited by 322 · see citing works
Yang, F., Fang, E., Mei, H., Chen, Y., Li, H., Li, D., Song, H., Wang, J., Mei, H., Xiao, W., et al. (2018) · Cancer Research · doi:10.1158/0008-5472.can-18-1559 · Cited by 213 · see citing works
Xia, Y., Xia, C., Pan, X., & Shen, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab044 · Cited by 179 · see citing works
“RNABindRPlus combines outputs from a sequence homology-based method with those from a SVM classifier (45).”
MethodSalerno, D., Chiodo, L., Alfano, V., Floriot, O., Cottone, G., Paturel, A., Pallocca, M., Plissonnier, M., Jeddari, S., Belloni, L., et al. (2020) · Gut · doi:10.1136/gutjnl-2019-319637 · Cited by 145 · see citing works
“According to these results, DLEU2 interaction with HBx and its recruitment on the viral minichromosome affect cccDNA transcription and viral replication but do not interfere with the establishment of the cccDNA pool.”
Enhanced breast cancer progression by mutant p53 is inhibited by the circular RNA circ-Ccnb1
Fang, L., Du, W. W., Lyu, J., Dong, J., Zhang, C., Yang, W., He, A. T., Kwok, Y. S. S., Ma, J., Wu, N., et al. (2018) · Cell Death and Differentiation · doi:10.1038/s41418-018-0115-6 · Cited by 139 · see citing works
“edu/RNABindRPlus/) [44], RBscore (http://ahsoka.”
Mondal, T., Juvvuna, P. K., Kirkeby, A., Mitra, S., Kosalai, S. T., Traxler, L., Hertwig, F., Wernig-Zorc, S., MIRANDA, C. A., Deland, L., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.01.020 · Cited by 135 · see citing works
catRAPID signature: identification of ribonucleoproteins and RNA-binding regions
Livi, C. M., Klus, P., Ponti, R. D., & Tartaglia, G. G. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv629 · Cited by 126 · see citing works
Qiu, J., Bernhofer, M., Heinzinger, M., Kemper, S., Norambuena, T., Melo, F., & Rost, B. (2020) · Journal of Molecular Biology · doi:10.1016/j.jmb.2020.02.026 · Cited by 123 · see citing works
Zhang, J., Ma, Z., & Kurgan, L. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx168 · Cited by 120 · see citing works
Advances in the characterization of RNA‐binding proteins
Marchese, D., de Groot, N. S., Lorenzo-Gotor, N., Livi, C. M., & Tartaglia, G. G. (2016) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1378 · Cited by 119 · see citing works
“83 on a larger benchmark set of 200 proteins.(80) The working principles of these algorithms are based on different assumptions: the conservation, physicochemical properties or topological properties of the binding site.”
BackgroundLi, G., Panday, S. K., & Alexov, E. G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22020606 · Cited by 114 · see citing works
“PSSM has already been establi hed for r viding crucial information in hot-spot [32] binding site [33] predicti ns and thermodynamics stability predictions [18,23–25,29].”
BackgroundRBPPred: predicting RNA-binding proteins from sequence using SVM
Zhang, X., & Liu, S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw730 · Cited by 103 · see citing works
Direct Evidence for Packaging Signal-Mediated Assembly of Bacteriophage MS2
Rolfsson, Ó., Middleton, S., Manfield, I. W., White, S. J., Fan, B., Vaughan, R. C., Ranson, N. A., Dykeman, E. C., Twarock, R., Ford, J. B., et al. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.11.014 · Cited by 96 · see citing works
“This is also consistent with a prediction of RNA-binding functional domains [65], which identifies two motifs located between amino acids 52–60 and amino acids 162–170 (Fig.”
ResultProtein–RNA interaction prediction with deep learning: structure matters
Wei, J., Chen, S., Zong, L., Gao, X., & Li, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab540 · Cited by 92 · see citing works
“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.”
BackgroundPan, Y., Wang, Z., Zhan, W., & Deng, L. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx822 · Cited by 92 · see citing works
Protein–RNA interactions: structural characteristics and hotspot amino acids
Krüger, D. M., Neubacher, S., & Grossmann, T. N. (2018) · RNA · doi:10.1261/rna.066464.118 · Cited by 88 · see citing works
“Importantly, available hotspot prediction for PRIs either relies on information about the evolutionary conservation of amino acids or it is relatively time consuming (Walia et al. 2014; Barik et al. 2016; Pan et al. 2018).”
BackgroundZhang, F., Zhao, B., Shi, W., Li, M., & Kurgan, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab521 · Cited by 89 · see citing works
“…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…”
Roche, R., Moussad, B., Shuvo, M. H., Tarafder, S., & Bhattacharya, D. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae039 · Cited by 85 · see citing works
“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.”
MethodGeng, C., Vangone, A., Folkers, G. E., Xue, L. C., & Bonvin, A. M. J. J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25630 · Cited by 82 · see citing works
Self-citationComputational Prediction of RNA-Binding Proteins and Binding Sites
Si, J.-N., Cui, J. H., Cheng, J. Q., & Wu, R. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms161125952 · Cited by 80 · see citing works
“RNABindRPlus [58] is a recently developed predictor which obviously improved prediction reliability, which combines sequence homology and machine learning methods.”
BackgroundSong, H., Li, D., Wang, X., Fang, E., Yang, F., Hu, A., Wang, J., Guo, Y., Liu, Y., Li, H., et al. (2020) · Journal of Hematology & Oncology · doi:10.1186/s13045-020-00857-7 · Cited by 68 · see citing works
RetractedA Large-Scale Assessment of Nucleic Acids Binding Site Prediction Programs
Miao, Z., & Westhof, É. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004639 · Cited by 68 · see citing works
“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.…”
Villamayor, L., López-García, D., Rivero, V., Martínez‐Sobrido, L., Nogales, A., & DeDiego, M. L. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1176177 · Cited by 65 · see citing works
Villamayor, L., Rivero, V., López-García, D., Topham, D. J., Martínez‐Sobrido, L., Nogales, A., & DeDiego, M. L. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1105309 · Cited by 59 · see citing works
Miao, Z., & Westhof, É. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv446 · Cited by 55 · see citing works
“Similarly to the test shown in Figure 3, RBscore and aaRNA demonstrate stable high accuracy on all the datasets, while RNABindRPlus exert highest accuracy in all RBP datasets but accuracy drops on three DBP datasets.”
MethodInfluentialFang, E., Wang, X., Yang, F., Hu, A., Wang, J., Li, D., Song, H., Mei, H., Guo, Y., Liu, Y., et al. (2019) · Advanced Science · doi:10.1002/advs.201900581 · Cited by 55 · see citing works
Jin, W., Brannan, K. W., Kapeli, K., Park, S. S., Tan, H. Q., Gosztyla, M. L., Mujumdar, M., Ahdout, J., Henroid, B., Rothamel, K., et al. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.06.019 · Cited by 52 · see citing works
Blanco, C., Bayas, M. V., Yan, F., & Chen, I. A. (2018) · Current Biology · doi:10.1016/j.cub.2018.01.014 · Cited by 54 · see citing works
“Other considerations include interface residue propensity (KYG [30]), sequence homology (RNABindRPlus [37]), and predicted secondary structure and conservation of physicochemical properties (PRBR [38]).”
The Role of Long Noncoding RNAs in Gene Expression Regulation
Li, Z., Zhao, W., Wang, M., & Zhou, X. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.81773 · Cited by 53 · see citing works
Song, J., Liu, G., Jiang, J., Zhang, P., & Liang, Y. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22020939 · Cited by 50 · see citing works
“If two protein sequences share similar amino acid compositions and are homologous, the similar segments in their sequences are likely to have common functions [47].”
BackgroundTemplate-based prediction of protein function
Petrey, D. S., Chen, T. S., Deng, L., Garzón, J. I., Hwang, H., Lasso, G., Lee, H., Silkov, A., & Honig, B. H. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.01.007 · Cited by 47 · see citing works
Li, G., Pahari, S., Murthy, A. K., Liang, S., Fragoza, R., Yu, H., & Alexov, E. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa761 · Cited by 41 · see citing works
A boosting approach for prediction of protein-RNA binding residues
Tang, Y., Liu, D., Wang, Z., Wen, T., & Deng, L. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1879-2 · Cited by 42 · see citing works
“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 ].”
BackgroundMethodInfluentialWang, N., Yan, K., Zhang, J., & Liu, B. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac236 · Cited by 34 · see citing works
“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.”
MethodRPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites
Luo, J., Liu, L., Venkateswaran, S., Song, Q., & Zhou, X. (2017) · Scientific Reports · doi:10.1038/s41598-017-00795-4 · Cited by 32 · see citing works
Disordered RNA-Binding Region Prediction with DisoRDPbind
Oldfield, C. J., Peng, Z., & Kurgan, L. (2019) · Methods in molecular biology · doi:10.1007/978-1-0716-0231-7_14 · Cited by 32 · see citing works
Liu, Y., Gong, W., Zhao, Y., Deng, X., Zhang, S., & Li, C. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa747 · Cited by 30 · see citing works
Xiaoxia, Y., Wang, J., Sun, J., & Liu, R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0133260 · Cited by 29 · see citing works
“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.”
BackgroundMethodThe HEX 110 Hexamerin Is a Cytoplasmic and Nucleolar Protein in the Ovaries of Apis mellifera
Martins, J. R., & Bitondi, M. M. G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0151035 · Cited by 28 · see citing works
“In silico analyses Potential RNA-binding sites were searched in the HEX 110 sequence using RNABindR, BindN, PPRint, SVM and RNABindRPlus softwares [36–40].”
MethodRNA-binding residues prediction using structural features
Ren, H., & Shen, Y. H. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0691-0 · Cited by 27 · see citing works
“Besides using sequence/structural features, certain methods predict RNA-binding residues directly from amino acid sequences [34, 38].”
MethodZhang, F., Li, M., Zhang, J., & Kurgan, L. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1253 · Cited by 26 · see citing works
“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…”
Miras, M., Truniger, V., Querol‐Audí, J., & Aranda, M. A. (2016) · Molecular Plant Pathology · doi:10.1111/mpp.12422 · Cited by 25 · see citing works
Sharan, M., Förstner, K. U., Eulálio, A., & Vogel, J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx137 · Cited by 25 · see citing works
“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.”
MethodWang, K., Hu, G., Wu, Z., Su, H. L., Yang, J., & Kurgan, L. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21186879 · Cited by 24 · see citing works
“[31], aaRNA search against machine”
BackgroundInfluentialComputational tools to study RNA-protein complexes
Bheemireddy, S., Sandhya, S., Srinivasan, N., & Sowdhamini, R. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.954926 · Cited by 23 · see citing works
Integrative analysis of significant RNA‐binding proteins in colorectal cancer metastasis
Zhou, B., & Guo, R. (2018) · Journal of Cellular Biochemistry · doi:10.1002/jcb.27290 · Cited by 22 · see citing works
Yang, C., Ding, Y., Meng, Q., Tang, J., & Guo, F. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05573-4 · Cited by 22 · see citing works
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 · see citing works
“These prediction results are in consensus with those of RNAbindRplus [35] and DP-Bind [36].”
ResultPredicting 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 21 · see citing works
“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.”
MethodIn Silico Prediction and Validation of Novel RNA Binding Proteins and Residues in the Human Proteome
Chowdhury, S., Zhang, J., & Kurgan, L. (2018) · PROTEOMICS · doi:10.1002/pmic.201800064 · Cited by 22 · see citing works
Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile
Patiyal, S., Dhall, A., Bajaj, K., Sahu, H., & Raghava, G. P. S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac538 · Cited by 20 · see citing works
Wang, X., Guo, Y., Chen, G., Fang, E., Wang, J., Li, Q., Li, D., Hu, A., Bao, B., Zhou, Y., et al. (2023) · Oncogene · doi:10.1038/s41388-023-02798-0 · Cited by 20 · see citing works
“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.”
MethodFastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues
EL‐Manzalawy, Y., Abbas, M. M., Malluhi, Q. M., & Honavar, V. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158445 · Cited by 20 · see citing works
“The dataset annotated with the identified interface residues is publicly available at: http://ailab1.ist.psu.edu/ RNABindRPlus/rb44.txt.”
BackgroundMethodResultInfluentialSelf-citationAn Effective Content Based Image Retrieval System Using Deep Learning Based Inception Model
Ranjith, E., Parthiban, L., Latchoumi, T. P., Ananda Kumar, S., Perera, D. G., & Ramaswamy, S. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10792-8 · Cited by 19 · see citing works
Zhang, L., & Liu, T. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.136147 · Cited by 19 · see citing works
SNB‐PSSM : A spatial neighbor‐based PSSM used for protein–RNA binding site prediction
Liu, Y., Gong, W., Yang, Z., & Li, C. (2021) · Journal of Molecular Recognition · doi:10.1002/jmr.2887 · Cited by 19 · see citing works
“(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.”
BackgroundA 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 17 · see citing works
Sun, X., Wu, Z., Su, J., & Li, C. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.136933 · Cited by 17 · see citing works
A generative model for constructing nucleic acid sequences binding to a protein
Im, J., Park, B., & Han, K. (2019) · BMC Genomics · doi:10.1186/s12864-019-6299-4 · Cited by 17 · see citing works
Wang, J., Ma, X., Hu, Y., Feng, G., Guo, C., Zhang, X., & Mā, H. (2024) · Nature Plants · doi:10.1038/s41477-023-01605-8 · Cited by 16 · see citing works
“We used the catRAPID and RNAbindPlus programs to investigate whether GRP20 could potentially bind to RNA (Extended Data Fig.”
InfluentialTwenty years of advances in prediction of nucleic acid-binding residues in protein sequences
Basu, S., Jing, Y., Kihara, D., & Kurgan, L. (2024) · Briefings Bioinform. · doi:10.1093/bib/bbaf016 · Cited by 15 · see citing works
Jia, P., Zhang, F., Wu, C., & Li, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae162 · Cited by 15 · see citing works
PRIME-3D2D is a 3D2D model to predict binding sites of protein–RNA interaction
Xie, J., Zheng, J., Hong, X., Tong, X., & Liu, S. (2020) · Communications Biology · doi:10.1038/s42003-020-1114-y · Cited by 15 · see citing works
Zhu, L., Davari, M. D., & Li, W. (2021) · Crystals · doi:10.3390/cryst11040324 · Cited by 14 · see citing works
Pharmacological perturbation of the phase-separating protein SMNDC1
Enders, L., Siklos, M. I., Borggräfe, J., Gaussmann, S., Koren, A., Malik, M., Tomek, T., Schuster, M. K., Reiniš, J., Hahn, E., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40124-0 · Cited by 14 · see citing works
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 · see citing works
“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.”
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 · see citing works
Xiong, D., Zeng, J., & Gong, H. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24806 · Cited by 12 · see citing works
“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.”
BackgroundMiao, Z., & Westhof, É. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw251 · Cited by 12 · see citing works
“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.”
BackgroundKim, 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 · see citing works
MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins
Zhang, J., Wang, R., & Wei, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01471 · Cited by 11 · see citing works
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 · see citing works
Self-citationDiscovering protein-binding RNA motifs with a generative model of RNA sequences
Park, B., & Han, K. (2020) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.107171 · Cited by 11 · see citing works
Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions
Sagar, A., & Xue, B. (2019) · Protein and Peptide Letters · doi:10.2174/0929866526666190619103853 · Cited by 10 · see citing works
Integrative characterization of MYC RNA-binding function
Li, S., Wang, Z., Wang, X., Wang, Y., Pattarayan, D., Zhang, Y. L., Nguyen, P., Bhuniya, A., Chen, Y., Huang, H., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100878 · Cited by 9 · see citing works
“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 · see citing works
“RNABindRPlus [25]—a hybrid machine learning sequence homology-based approach—confirms that EIN2-C may have several putative RNA-binding sites.”
BackgroundZhao, Y., & Du, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.09.008 · Cited by 9 · see citing works
DeepMVF-RBP: Deep Multi-view Fusion Representation Learning for RNA-binding Proteins Prediction
Du, X., Diao, Y., Yu, Y., Zhu, H., Yan, Y., & Zhang, Y. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621102 · Cited by 9 · see citing works
“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 · see citing works
Self-citationSong, J., Liang, Y., Liu, G., Wang, R., Sun, L., & Zhang, P. (2020) · IEEE Access · doi:10.1109/access.2020.2968847 · Cited by 8 · see citing works
“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].”
BackgroundMethodMuppirala, 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 · see citing works
Self-citationHu, 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 · see citing works
Zhang, S., Zhao, L., & Xia, J. (2019) · IEEE Access · doi:10.1109/access.2019.2931552 · Cited by 8 · see citing works
Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites
Mou, M., Lu, M., Zhou, Z., Ren, Y., Yu, X., Pan, Z., Zhou, Y., Yang, H., Zheng, L., Shukai, G., et al. (2025) · Advanced Science · doi:10.1002/advs.202516530 · Cited by 7 · see citing works
Agarwal, A., Kant, S., & Bahadur, R. P. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26528 · Cited by 7 · see citing works
“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.”
MethodWang, B., & Li, W.-J. (2024) · Genes · doi:10.3390/genes15081090 · Cited by 9 · see citing works
“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…”
MethodPartner‐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 7 · see citing works
Self-citationCytoplasmic 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 · see citing works
pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data
Chu, L., Christopoulou, N., McCaughan, H., Winterbourne, S., Cazzola, D., Wang, S., Litvin, U., Brunon, S., Harker, P. J. B., McNae, I. W., et al. (2024) · Life Science Alliance · doi:10.26508/lsa.202402787 · Cited by 5 · see citing works
“…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).”
InfluentialSong, J., Liu, G., Song, C., & Jiang, J. (2020) · Biotechnology & Biotechnological Equipment · doi:10.1080/13102818.2020.1840436 · Cited by 5 · see citing works
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 · see citing works
“Comparing with seven existing RNA binding site prediction methods, including SNBRFinder, RNABindRPlus, BindN, PPRint, Liu2010, BindN+ and RNABindR2.”
MethodInfluentialLi, X., Peng, W., & Zhu, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf457 · Cited by 4 · see citing works
Zhang, J., Chen, Q., & Liu, B. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26626 · Cited by 4 · see citing works
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 · see citing works
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 · see citing works
“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.”
MethodLi, K., Wu, H., Yue, Z., Sun, Y., & Xia, C. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107901 · Cited by 4 · see citing works
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 · see citing works
“Predictions were downloaded for the prediction algorithms BindUp [ 55 ], FTMap [ 56 ], RNABindRPlus [ 57 ], DisoRDPbind [ 58 ], and PST-PRNA [ 59 ].”
Mahmoudi, I., Quignot, C., Martins, C., & Andreani, J. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012650 · Cited by 4 · see citing works
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 · see citing works
- 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.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-05-20
- Volume
- 9
- Issue
- 5
- Pages
- e97725
- ISSN
- 1932-6203 (ISSN-L)
- 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
- 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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