Skip to main content
PMC home page
BMC Genomics logoLink to BMC Genomics
. 2010 Dec 2;11(Suppl 4):S2. doi: 10.1186/1471-2164-11-S4-S2

Predicting RNA-binding residues from evolutionary information and sequence conservation

Yu-Feng Huang 1,#, Li-Yuan Chiu 2,#, Chun-Chin Huang 1, Chien-Kang Huang 2,✉
PMCID: PMC3005934  PMID: 21143803

Abstract

Abstract

Background

RNA-binding proteins (RBPs) play crucial roles in post-transcriptional control of RNA. RBPs are designed to efficiently recognize specific RNA sequences after it is derived from the DNA sequence. To satisfy diverse functional requirements, RNA binding proteins are composed of multiple blocks of RNA-binding domains (RBDs) presented in various structural arrangements to provide versatile functions. The ability to computationally predict RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments.

Results

The proposed prediction framework named “ProteRNA” combines a SVM-based classifier with conserved residue discovery by WildSpan to identify the residues that interact with RNA in a RNA-binding protein. Although these conserved residues can be either functionally conserved residues or structurally conserved residues, they provide clues on the important residues in a protein sequence. In the independent testing dataset, ProteRNA has been able to deliver overall accuracy of 89.78%, MCC of 0.2628, F-score of 0.3075, and F0.5-score of 0.3546.

Conclusions

This article presents the design of a sequence-based predictor aiming to identify the RNA-binding residues in a RNA-binding protein by combining machine learning and pattern mining approaches. RNA-binding proteins have diverse functions while interacting with different categories of RNAs because these proteins are composed of multiple copies of RNA-binding domains presented in various structural arrangements to expand the functional repertoire of RNA-binding proteins. Furthermore, predicting RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments.

Background

RNA-binding proteins (RBPs) are designed to efficiently recognize specific RNA sequences after they are derived from the DNA sequences. Protein-RNA interactions are fundamental to cellular processes, including the assembly and function of ribonucleoprotein particles (RNPs), such as ribosomes and spliceosomes and the post-transcriptional regulation of gene products. For satisfying diverse functional requirements, RNA binding proteins are composed of multiple blocks of RNA-binding domains (RBDs) presented in various structural arrangements to provide versatile functionality [1,2]. Although RNA structure is hierarchical, that is, the primary sequence determines the secondary structure which, in turns, determines tertiary structure, the tertiary structure of RNA is not as stable as secondary structure and is hard to predict [3]. However, sequence conservations in RNA-binding domains have been discovered in RNA-binding proteins [4, 5, 6, 7]. With the recent growth of protein-RNA complexes in the Protein Data Bank (PDB) [8] and the Nucleic Acid Database (NDB) [9], structural analysis on RNA-binding pockets [10, 11, 12, 13, 14, 15, 16, 17, 18] and the themes of RNA-protein recognition [18, 19, 20] have been investigated as well.

Most recent works on predicting RNA-binding residues used support vector machine (SVM) with protein evolutionary information from protein sequence. Wang and Brown (2006) developed the web service, BindN [21], to predict DNA and RNA binding sites using sequence features to represent structural characteristics including relative solvent accessible surface area, side chain pKa, hydrophobicity index and molecular mass of an amino acid. Tong et al. (2008) [22] proposed the hybrid RISP (RNA-Interaction Site Prediction) method by adjusting cutoff value of SVM discrimination function to improve prediction performance. Kumar et al. (2008) developed Pprint [23] by using evolutionary profiles of the position-specific scoring matrices (PSSMs) and amino acid composition while they also adjusted cutoff value of SVM discrimination function to improve prediction performance. Wang et al. (2008) developed PRINTR [24] by using additional structural information from protein-RNA complexes. Cheng et al. (2008) developed RNAProB [25] by smoothing PSSM profiles with consideration of the correlation and dependency from the neighboring residues for each amino acid in a protein. Spriggs et al. (2009) [26] developed the PiRaNhA by using support vector machine with a PSSM profile and three amino acid properties, including interface propensity (IP), predicted solvent accessibility (pA) and hydrophobicity (H) for recognizing RNA-binding residues [27]. Other machine learning approaches such as neural network and Naïve Bayes classifier have also been applied to predict RNA-binding residues. Jeong et al. (2004) [28] applied artificial neural network (ANN)-based method with amino acid sequence and predicted secondary structure information and improved the performance by using post-processing procedures such as state-shifting and filtering isolated interacting residues from prediction. Improved version by Jeong et al. (2006) [29] used evolutionary information extracted from PSI-BLAST profiles and CLUSTALW alignment. Terribilini et al. (2006) [30] applied a Naïve Bayes classifier with amino acid sequence information for predicting RNA interacting residues and presented the results through the web service RNABindR [31]. The ability to computationally predict RNA-binding residues in a RNA-binding protein can help biologists reveal site-directed mutagenesis in wet-lab experiments.

Caragea et al. [32] explored the problem of assessing the performance of classifiers trained on macromolecular sequence data, with the emphasis on cross-validation and data selection methods. In comparison of window-based k-fold cross-validation and sequence-based k-fold cross-validation, window-based cross-validation can yield overly optimistic estimates of the performance of classifier relative to the estimates obtained using sequence-based cross-validation. RNAProB, BindN, RISP, PRINTR and PiRaNhA are predictors that report performance window-based k-fold cross-validation while Pprint and RNABindR report performance with sequence-based k-fold cross-validation. The predictors evaluated with window-based k-fold cross-validation have superior performance than those with sequence-based k-fold cross-validation. The reason is that data instances in the testing fold would be predicted by data instances with sub-sequence identity higher than 25% in the training fold in window-based k-fold cross-validation. Therefore, in data with class imbalance, the metrics that measure the classification performance must be chosen carefully. Matthew's correlation coefficient (MCC), F-score and F0.5-score are widely applied to assess the prediction performance. MCC is used to measure prediction quality with the consideration of both under- and over-predictions. F-score and F0.5-score are used to assess balanced prediction quality on both positive class and negative class.

In this article, we proposed the prediction framework “ProteRNA” with the combination of SVM-based classifier with evolutionary profiles and conserved residues discovery by sequence conservation for identifying RNA-interacting residues in a RNA-binding protein. In the SVM-based classifier, we use features including position-specific scoring matrix computed by PSI-BLAST and secondary structure information predicted by PSIPRED as feature vectors [33]. To exploit the sequence conservation information, WildSpan [34] (http://biominer.bime.ntu.edu.tw/wildspan/), which is developed to discover functional signatures and diagnostic patterns of proteins directly from a set of unaligned protein sequences, is incorporated. The most distinguishing feature of WildSpan is that it links short motifs (local conserved regions) with large flexible gaps to deliver the most frequently observed discontinuous patterns present in related proteins. WildSpan has been embedded in many applications [35, 36, 37, 38, 39] to discover functionally important residues; therefore, we apply WildSpan to discover conserved residues as RNA-binding residues in a protein sequence to improve prediction performance on detecting more RNA-binding residues. The independent testing dataset collected for performance evaluation contains 33 testing RNA-binding proteins with less than 30% sequence identity against with training data. In the independent testing dataset, ProteRNA has been able to deliver overall accuracy of 89.78%, MCC of 0.2628, F-score of 0.3075, and F0.5-score of 0.3546. We emphasize MCC, F-score and F0.5-score because it provides the biochemist with a confidence level for designing an experiment to confirm whether a predicted binding residue is really involved in interaction with the RNA.

Results and discussion

In this section, we will report the experiments conducted to evaluate the performance of our proposed approach, ProteRNA with the combination of SVM-based classifier with evolutionary profiles and conserved residues discovery by sequence conservation. In order to avoid bias, we repeated 5-fold cross-validation procedure 20 times to observe prediction performance on the training dataset RB147 (see Materials and Methods for details). For each run, we applied sequence-based 5-fold cross-validation; therefore, protein chains will be randomly divided into 5 folds: one fold for testing and remaining 4 folds for training. For this study, LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm) was used for data training and classification and WildSpan was used for detecting conserved residues from homologous protein sequences. We use independent testing dataset containing 33 protein chains for comparing ProteRNA with other predictors such as PiRaNhA, Pprint, BindN, and PRIP.

Performance evaluation by five-fold cross-validation

In order to avoid bias, we repeated 5-fold cross-validation 20 times to observe prediction performance and experimental result was shown in Table 1. Only using SVM-based classifier, ProteRNASVM delivers overall sensitivity of 38.85%, specificity of 97.01%, precision of 75.99%, accuracy of 85.93%, MCC of 0.4732, F-score of 0.5170 and F0.5-score of 0.6343. Since the experiments are repeated 20 times for reducing prediction bias, standard deviation for each assessment is also listed. The results have been obtained using the training parameters, C = 21, γ = 2-5, which give better results than other values for prediction of RNA-binding residues. Then WildSpan only outputs patterns for each input protein chain once so there is no information about standard deviation for each assessment. ProteRNAWildSpan delivers overall sensitivity of 12.28%, specificity of 96.26%, precision of 43.60%, accuracy of 80.27%, MCC of 0.1489, F-score of 0.1916 and F0.5-score of 0.2887. After combining prediction results by ProteRNASVM and ProteRNAWildSpan, ProteRNA delivers overall sensitivity of 44.84%, specificity of 93.56%, precision of 62.10%, accuracy of 84.28%, MCC of 0.4378, F-score of 0.5208 and F0.5-score of 0.5766.

Table 1.

Prediction performance evaluated by the 5-fold cross-validation using the training dataset, RB147

Predictors Sensitivity Specificity Precision Accuracy MCC F-score F0.5-score
ProteRNASVM 38.85% ± 0.46% 97.01% ± 0.09% 75.99% ± 0.48% 85.93% ± 0.08% 0.4732 ± 0.0036 0.5170 ± 0.0040 0.6343 ± 0.0034
ProteRNAWildSpan 12.28% 96.26% 43.60% 80.27% 0.1489 0.1916 0.2887
ProteRNA 44.84% ± 0.37% 93.56% ± 0.09% 62.10% ± 0.25% 84.28% ± 0.06% 0.4378 ± 0.0027 0.5208 ± 0.0027 0.5766 ± 0.0022
Open in a new tab

As reported by Towfic et al. [40], over half (55.7%) of the RNAs are rRNAs in the dataset of RB147. According to their study, Table 2 shows the distribution of different categories of RNAs on RNA-binding residues. rRNA is the major group that contains about 38% positive samples in rRNA group. Remaining groups presents highly imbalanced class dataset, containing about 10% positive sample in average. If the predictor tries to predict all samples as negative class exclusive of rRNA group, the predictor may gain better performance in assessment but provide no clues for biologists. Table 1 describes the average prediction performance of 20 runs of 5-fold cross-validation; however, we only choose one of the repeated experiments that had a performance that is close to the average performance for detailed analysis in Table 3. As shown in Table 3 ProteRNAWildSpan predicts an equal amount of true positives and false positives on average. Previous research on studying RNA-binding domains revealed that RNA binding proteins are composed of multiple blocks of RNA-binding domains to provide versatile functionality. Therefore, conserved residues in the same RNA-binding domain from different RNA-binding proteins would not always interact with a specific RNA. Furthermore, while combining prediction results predicted by ProteRNASVM and ProteRNAWildSpan, ProteRNAWildSpan detected additional RNA-binding residues that ProteRNASVM didn’t predict.

Table 2.

Statistical information of the training dataset, RB147 in terms of RNA-binding residues

Number of RNA-binding residues Total number of residues Ratio of RNA-binding residues
rRNA 3916 10267 38.14%
mRNA 256 1878 13.63%
tRNA 1230 12401 9.92%
others 755 7778 9.71%
Total 6157 32324 19.05%
Open in a new tab

Table 3.

Prediction performance breakdown in terms of the categories of RNA using the training dataset, RB147

Predictor RNA TP FP TN FN Sensitivity Specificity Precision Accuracy MCC F-score F0.5-score
ProteRNASVM rRNA 2060 537 5814 1856 52.60% 91.54% 79.32% 76.69% 0.4933 0.6326 0.7201
mRNA 27 16 1606 229 10.55% 99.01% 62.79% 86.95% 0.2193 0.1806 0.3154
tRNA 234 171 11000 996 19.02% 98.47% 57.78% 90.59% 0.2942 0.2862 0.4105
others 109 93 6930 646 14.44% 98.68% 53.96% 90.50% 0.2441 0.2278 0.3487
Total 2430 823 25344 3727 39.47% 96.86% 74.70% 85.92% 0.4741 0.5165 0.6338
ProteRNAWildSpan rRNA 554 412 5939 3362 14.15% 93.51% 57.35% 63.24% 0.1274 0.2270 0.3560
mRNA 67 121 1501 189 26.17% 92.54% 35.64% 83.49% 0.2139 0.3018 0.3323
tRNA 50 173 10998 1180 4.07% 98.45% 22.42% 89.09% 0.0566 0.0688 0.1178
others 85 272 6751 670 11.26% 96.13% 23.81% 87.89% 0.1045 0.1529 0.1947
Total 756 978 25189 5401 12.28% 96.26% 43.60% 80.27% 0.1489 0.1916 0.2887
ProteRNA rRNA 2256 878 5473 1660 57.61% 86.18% 71.98% 75.28% 0.4618 0.6400 0.6856
mRNA 89 138 1484 167 34.77% 91.49% 39.21% 83.76% 0.2764 0.3685 0.3823
tRNA 238 304 10867 992 19.35% 97.28% 43.91% 89.55% 0.2431 0.2686 0.3502
others 177 366 6657 578 23.44% 94.79% 32.60% 87.86% 0.2118 0.2727 0.3024
Total 2760 1686 24481 3397 44.83% 93.56% 62.08% 84.28% 0.4376 0.5206 0.5764
Open in a new tab

As we known, rRNA is the major group among the training dataset. Comparing the amount of RNA-binding proteins in terms of interacting target (e.g. rRNA, tRNA, mRNA), we find that tRNA generally has the most interaction partners followed by mRNA and rRNA has the least partners. ProteRNASVM tends to predict negative for proteins in the mRNA group and over-predict either positive class or negative class in tRNA group. However, ProteRNAWildSpan shows no different between categories of RNAs because of discovered homologous proteins in Swiss-Prot. In addition, ProteRNAWildSpan detects conserved residues as binding residues that cover regions that ProteRNASVM doesn’t predict; therefore, we apply WildSpan to detect conserved residues because these conserved residues have higher probability to play roles in interacting RNAs.

Comparison with other predictors by independent testing

Only predictors that predict RNA-binding residues from protein primary sequence information were selected for performance comparison. In addition, RISP did not respond with any prediction results after submitting the jobs and PRINTR is unavailable. According to the designed framework of RNABindR, if there is an exact matched protein chain in Protein Data Bank, RNABindR will return the actual RNA-binding residues of protein-RNA complex. Therefore, it is difficult to distinguish whether the returned result is actually binding or predicted binding so RNABindR will be excluded. Finally, Table 4 shows the prediction performance of ProteRNA in comparison with PiRaNhA, Pprint, BindN, and PRIP. While ordering prediction performance in terms of MCC, ProteRNA delivers better performance than other predictors in accuracy, MCC, and F0.5-score.

Table 4.

Comparison of ProteRNA with other predictors using the independent testing dataset, RB33

Predictor* TP FP TN FN Sensitivity Specificity Precision Accuracy MCC F-score F0.5-score
ProteRNA 222 340 8563 660 25.17% 96.18% 39.50% 89.78% 0.2628 0.3075 0.3546
PiRaNhA 265 538 8365 617 30.05% 93.96% 33.00% 88.20% 0.2504 0.3145 0.3236
Pprint 447 1782 7121 435 50.68% 79.98% 20.05% 77.34% 0.2094 0.2873 0.2281
BindN 348 1613 7290 534 39.46% 81.88% 17.75% 78.06% 0.1527 0.2449 0.1994
PRIP 131 835 8068 751 14.85% 90.62% 13.56% 83.79% 0.0526 0.1418 0.1380
Open in a new tab

*Order by MCC.

Table 5 shows the Top-10 predictions by different predictors ordered by the MCC and precision among 33 independent testing samples. In terms of MCC, we can find that at least 4 predictors have predictions in 6 protein chains of Top-10 ranking. In terms of precision, we can find that at least 4 predictors have predictions in 7 protein chains of Top-10 ranking. Figure 1 (a) and (b) show the predicted RNA-binding residues in the case of E. coli SelB protein with PDB ID 2PJPA [41] by ProteRNA and PiRaNhA respectively. In this case, because WildSpan does not mine any patterns for 2PJPA, only ProteRNASVM gives prediction result. Figure 2 (a) and (b) show the predicted RNA-binding residues in the case of RluA [42]. In this case, ProteRNA outperforms than other predictors in terms of MCC. BindN and Pprint tend to predict more and more class label for each residue; therefore, they recommend more and more false positives and false negatives. Meanwhile, PRIP and PiRaNhA have similar performance in predicting RNA-binding residues in the case of 2I82C. These figures are rendered by PyMOL (http://www.pymol.org/).

Table 5.

Comparison of the top 10 ranking predictions with results from other predictors

Rank ProteRNA PiRaNhA Pprint BindN PRIP
(a) Rank by MCC
1 2PJP_A 2QAM_Z 2QAM_Z 2QAM_Z 2PY9_C
2 2QAM_Z 2QBE_T 1VS8_O 2PY9_C 2QAM_Z
3 2PY9_C 2DER_B 2PJP_A 1VS8_O 2HYI_D
4 1VS8_O 2G4B_A 2PY9_C 2QBE_T 2NQP_B
5 2G4B_A 1VS8_O 2GYA_3 2G4B_A 2IY5_A
6 2Q66_A 2PY9_C 2DER_B 2DER_B 1VS8_O
7 2I82_C 2G8K_A 2G4B_A 2J0Q_A 2I82_C
8 2DER_B 2OZB_B 2QBE_T 2IPY_B 2V47_C
9 2QBE_T 2V47_C 2DR2_A 2HVR_A 2GJE_A
10 2DR2_A 2GJE_D 2QKK_F 2GTT_G 2JEA_B
MCC of Rank 1 0.6668 0.6415 0.6006 0.4364 0.5521
MCC of Rank 10 0.3063 0.2719 0.2390 0.1951 0.0517
(b) Rank by precision
1 2Q66_A 2GYA_3 2GYA_3 2QAM_Z 2QAM_Z
2 2PJP_A 2QBE_T 2QAM_Z 2QBE_T 2PY9_C
3 2PY9_C 2QAM_Z 2QBE_T 1VS8_O 1VS8_O
4 2QAM_Z 1VS8_O 1VS8_O 2PY9_C 2QBE_T
5 2DER_B 2OZB_B 2PY9_C 2GYA_3 2I82_C
6 1VS8_O 2PY9_C 2DER_B 2G4B_A 2V47_C
7 2GYA_3 2DER_B 2V47_C 2J0Q_A 2IY5_A
8 2I82_C 2V47_C 2I82_C 2I82_C 2GYA_3
9 2QBE_T 2G4B_A 2G4B_A 2DER_B 2G4B_A
10 2G8K_A 2Q66_A 2GJE_A 2V47_C 2GJE_A
Precision of Rank 1 100.00% 100.00% 76.92% 76.47% 75.00%
Precision of Rank 10 50.00% 35.71% 25.00% 24.00% 13.33%
Open in a new tab

Values in bold indicate listing in the top 10 by at least 5 predictors.

Values in bold and italics indicate listing in the top 10 by at least 4 predictors.

Figure 1.

Figure 1

Open in a new tab

Case study on E. coli SelB (PDBID 2PJPA) Residues colored by green, red, and blue represent true positive, false positive and false negative, respectively. (a) Predicted RNA-binding residues by ProteRNA. (b) Predicted RNA-binding residues by PiRaNhA.

Figure 2.

Figure 2

Open in a new tab

Case study on RluA (PDBID 2I82C) Residues colored by green, red, and blue represent true positive, false positive and false negative, respectively. (a) Predicted RNA-binding residues by ProteRNA. (b) Predicted RNA-binding residues by PiRaNhA.

Conclusions

This article presents the design of a sequence based predictor aiming to identify the RNA-binding residues in a RNA-binding protein by machine learning and pattern mining approaches. RNA-binding proteins play different roles while interacting with different categories of RNAs to represent diverse functions. However, RNA-binding proteins are accommodated by the presence of multiple copies of these RNA-binding domains presented in various structural arrangements to expand the functional repertoire of RNA-binding proteins. Therefore, it is still difficult to predict RNA-binding residues in a RNA-binding protein. Furthermore, predicting RNA-binding residues in a RNA-binding protein can help biologists reveal site-directed mutagenesis in wet-lab experiments.

In the experiments reported in this article, ProteRNA used not only evolutionary profile with predicted secondary structure but also sequence conservation information. Although these conserved residues can be functional conserved residues or structural conserved residues, they also provide clues to indicate the important residues in a protein sequence. In the independent testing dataset, ProteRNA has been able to deliver overall accuracy of 89.78%, MCC of 0.2628, F-score of 0.3075, and F0.5-score of 0.3546. It is anticipated that the prediction accuracy delivered by ProteRNA will continue to improve as the number of protein-RNA complexes deposited in the PDB continues to grow and the number of training samples that can be exploited continues to increase accordingly. Nevertheless, it is the computational biologists’ primary interest to develop more advanced prediction mechanisms. In this respect, we believe that, as the number of protein-RNA complexes deposited in the PDB increases, we can obtain more insights about the key physiochemical properties that play essential roles in protein-RNA interactions and then we will be able to develop more advanced prediction mechanisms accordingly. In addition, we will exploit the experiences learned in this study in order to design specific predictors for other families of proteins interacting with RNA. We believe that different families of proteins may have very different characteristics. Therefore, concerning a specific type of proteins, a specifically-designed predictor should be able to deliver superior performance in compared to a general-purpose predictor.

Materials and methods

Datasets

We used RB147 as the training dataset for predicting RNA-binding residues in a protein collected by Terribilini et al., containing 147 non-redundant protein chains with resolution better than 3.5 Å in the PDB solved by X-ray crystallography [31,40]. No two protein chains has a sequence identity greater than 30%. Based on the cut-off distance of 5 Å, a total of 32,324 amino acids are in RB147, which contains 6,157 RNA-binding residues and 26,167 non-binding residues. The list of PDB ids of the training dataset, RB147, is shown in Table 6(a).

Table 6.

Datasets for ProteRNA

(a) Training dataset - RB147
1A34_A 1A9N_A 1APG_A 1ASY_A 1AV6_A 1B23_P 1B2M_A 1C0A_A
1DDL_A 1DFU_P 1DI2_A 1E8O_A 1EC6_A 1EIY_B 1F7U_A 1FEU_A
1FFY_A 1FJG_B 1FJG_C 1FJG_D 1FJG_E 1FJG_G 1FJG_I 1FJG_J
1FJG_K 1FJG_L 1FJG_M 1FJG_N 1FJG_P 1FJG_Q 1FJG_S 1FJG_T
1FJG_V 1G1X_A 1G1X_B 1G1X_C 1G2E_A 1GTF_Q 1H2C_A 1H3E_A
1H4S_A 1HQ1_A 1HRO_W 1I6U_A 1J1U_A 1J2B_A 1JBR_A 1JID_A
1K8W_A 1KNZ_A 1KQ2_A 1LAJ_A 1LNG_A 1M5O_C 1M8V_A 1M8X_A
1MZP_A 1N35_A 1N78_A 1NB7_A 1OOA_A 1PGL_2 1Q2S_A 1QF6_A
1QTQ_A 1R3E_A 1RMV_A 1RPU_A 1SDS_A 1SER_A 1SI3_A 1T0K_B
1TFW_A 1U0B_B 1UN6_B 1UVJ_A 1VFG_A 1VQO_1 1VQO_2 1VQO_3
1VQO_A 1VQO_B 1VQO_C 1VQO_D 1VQO_E 1VQO_G 1VQO_H 1VQO_I
1VQO_J 1VQO_K 1VQO_L 1VQO_M 1VQO_N 1VQO_P 1VQO_Q 1VQO_R
1VQO_S 1VQO_T 1VQO_U 1VQO_V 1VQO_W 1VQO_X 1VQO_Y 1VQO_Z
1W2B_5 1WNE_A 1WPU_A 1WSU_A 1WZ2_A 1Y69_8 1Y69_K 1Y69_U
1YVP_A 1YZ9_A 1ZH5_A 2A1R_A 2A8V_A 2ASB_A 2AVY_F 2AVY_U
2AW4_0 2AW4_1 2AW4_2 2AW4_3 2AW4_D 2AW4_E 2AW4_G 2AW4_H
2AW4_J 2AW4_L 2AW4_N 2AW4_P 2AW4_Q 2AW4_R 2AW4_S 2AW4_Y
2AW4_Z 2AZ0_A 2BGG_A 2BH2_A 2BTE_A 2BU1_A 2BX2_L 2CT8_A
2D3O_1 2D3O_S 2FMT_A
(b) Independent Testing Dataset - RB33
1VS8_O 2D6F_D 2DB3_C 2DER_B 2DR2_A 2DU3_A 2F8S_A 2FK6_A
2G4B_A 2G8K_A 2GJE_A 2GJE_D 2GJW_C 2GTT_G 2GYA_3 2HVR_A
2HYI_D 2I82_C 2IPY_B 2IX1_A 2IY5_A 2J0Q_A 2JEA_A 2JEA_B
2NQP_B 2OZB_B 2PJP_A 2PY9_C 2Q66_A 2QAM_Z 2QBE_T 2QKK_F
2V47_C
Open in a new tab

In order to evaluate prediction performance among different prediction models, we collected a new independent testing dataset by extracting all structures of Protein-RNA complexes from the PDB that were added after January 2006. Protein chains with a resolution better than 3.5 Å and sequence length of protein chain longer than 40 amino acids will be reserved. We then performed a redundancy reduction using BLASTclust [2] to ensure that none of the chains showed a sequence similarity of more than 30% within the dataset and also in the training dataset; therefore, 33 protein-RNA complexes were selected to create a dataset called RB33. The list of PDB ids in RB33 are shown in Table 6(b). Based on the cut-off distance of 5 Å, a total of 9,785 amino acids are in RB33, which contains 882 RNA-binding residues and 8,903 non-binding residues.

Framework for prediction RNA-interacting residues

Figure 3 presents the overall framework for predicting RNA-binding residues. In the overall framework, we combined SVM-based classifier and sequence conservation discovery by WildSpan to predict RNA-binding residues. For the SVM-based classifier (ProteRNASVM), we have employed the LIBSVM package with the Gaussian kernel (software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm). The model of the SVM has been generated based on the training data set derived by associating each residue in the training protein chains with the evolutionary profiles of the residue and its 22 neighboring residues (window size = 23) [43,44]. The evolutionary profile of a residue is in fact the vector corresponding to the residue in the position specific scoring matrix (PSSM) computed by the PSI-BLAST package [45] with three iterations (blastpgp -j 3) against with NCBI non-redundant reference sequence database (ftp://ftp.ncbi.nih.gov/blast/db/). The normalization function for PSSM features is defined as follow:

graphic file with name 1471-2164-11-S4-S2-i1.gif

Figure 3.

Figure 3

Open in a new tab

The overall framework of ProteRNA for predicting RNA-binding residues

where x is the entry value in 20xN matrix of PSSM (N is sequence length of a protein). With the consideration of structure information, we also used secondary structure information predicted by PSIPRED; the predicted secondary structure information consists of three probability values that represent helix, sheet and coil respectively (e.g. (H, E, C) = (0.75, 0.25, 0.25)). In addition, each residue was labelled based on whether it is involved in binding with the RNA or not. Therefore, for each residue in a protein sequence, we construct a 23 * 24 = 552 dimensional feature factor (window size = 23, feature size = 24); the 24 dimensions include 20 features from PSSM, 3 features from PSIPRED and a boundary flag. As shown in Figure 4, the detail data flow and feature vector preparation for SVM-based classifier is addressed. The best parameters selected for predicting RNA-binding residues is decided by 5-fold cross-validation.

Figure 4.

Figure 4

Open in a new tab

An outline of RNA-binding residue prediction by the SVM-based classifier.

In the part of WildSpan (ProteRNAWildSpan), for protein-based mining suggested by the authors, at most 150 unique homologous proteins with sequence identity ranged from 30% to 90% are required by searching against Swiss-Prot sequence database with PSI-BLAST (blastpgp –j 6). Then we applied default parameter to obtain patterns by WildSpan. WildSpan can’t generate any pattern if there are not enough homologous proteins selected from Swiss-Prot protein sequence database or too similar homologous proteins.

Significance and performance evaluation

The predictions made for the testing instances are compared with the defined class labels (binding or non-binding) to evaluate the predictor. The accuracy is defined as

graphic file with name 1471-2164-11-S4-S2-i2.gif

where TP is the number of true positives (binding residues with positive predictions); TN is the number of true negatives (non-binding residues with negative predictions); FP is the number of false positives (non-binding residues but predicted as binding residues) and FN is the number of false negatives (binding residues but predicted as non-binding residues). Matthew's correlation coefficient (MCC) is defined as follows:

graphic file with name 1471-2164-11-S4-S2-i3.gif

MCC is used to measure prediction performance with the consideration of both under- and over-predictions, where MCC = 1 denotes a perfect prediction, MCC = 0 indicates a completely random assignment, and MCC = -1 means a perfectly reverse correlation.

Since the data for RNA-binding residue prediction is skewed, so-called class imbalanced data, the accuracy alone may be misleading. The predictor can achieve 85% accuracy by simply predicting all residues as negative for datasets where the positive to negative sample ratio is 1:10. Therefore, prediction performance on positive class and negative class should be assessed individually. Metrics of the specificity and sensitivity can help predictors to know their prediction performance on positive and negative samples respectively. The sensitivity is used to measure the prediction capability of positive samples; the specificity is used to measure the prediction capability of negative samples. Specificity and sensitivity are defined as follows:

graphic file with name 1471-2164-11-S4-S2-i4.gif
graphic file with name 1471-2164-11-S4-S2-i5.gif

In addition, precision and Fβ-score are also defined as follows:

graphic file with name 1471-2164-11-S4-S2-i6.gif
graphic file with name 1471-2164-11-S4-S2-i7.gif

Precision is used to assess prediction power on positive class. F-score (F1-score) is the harmonic mean of precision and Sensitivity if β = 1. F0.5-score weights precision twice as much as sensitivity if β = 0.5.

List of abbreviations

RBP: RNA-binding protein, RBD: RNA-binding domain; RNP: Ribonucleoprotein particle; PSSM: Position-specific scoring matrix; NDB: Nucleic Acid Database; PDB: Protein Data Bank; SVM: Support vector machine.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

YFH and LYC developed and implemented the overall framework and drafted the manuscript; CCH provided valuable suggestion on experiments based on his previous works on predicting DNA-binding residues. CKH conceived of the study and participated in its design and coordination and helped to draft this manuscript. All authors have read and approved the final manuscript.

Contributor Information

Yu-Feng Huang, Email: yfhuang@csie.ntu.edu.tw.

Li-Yuan Chiu, Email: r97525034@ntu.edu.tw.

Chun-Chin Huang, Email: d98922043@ntu.edu.tw.

Chien-Kang Huang, Email: ckhuang@ntu.edu.tw.

Acknowledgements

This research has been supported by the National Science Council and National Taiwan University. Funding for article processing charges: National Science Council, NSC 98-2627-B-002-018.

This article has been published as part of BMC Genomics Volume 11 Supplement 4, 2010: Ninth International Conference on Bioinformatics (InCoB2010): Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2164/11?issue=S4.

References

  1. Lunde BM, Moore C, Varani G. RNA-binding proteins: modular design for efficient function. Nat Rev Mol Cell Biol. 2007;8(6):479–490. doi: 10.1038/nrm2178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Burd CG, Dreyfuss G. Conserved structures and diversity of functions of RNA-binding proteins. Science. 1994;265(5172):615–621. doi: 10.1126/science.8036511. [DOI] [PubMed] [Google Scholar]
  3. Tinoco I, Bustamante C. How RNA folds. Journal of molecular biology. 1999;293(2):271–281. doi: 10.1006/jmbi.1999.3001. [DOI] [PubMed] [Google Scholar]
  4. Yan KS, Yan S, Farooq A, Han A, Zeng L, Zhou MM. Structure and conserved RNA binding of the PAZ domain. Nature. 2003;426(6965):468–474. doi: 10.1038/nature02129. [DOI] [PubMed] [Google Scholar]
  5. Soulard M, Della Valle V, Siomi MC, Pinol-Roma S, Codogno P, Bauvy C, Bellini M, Lacroix JC, Monod G, Dreyfuss G. et al. hnRNP G: sequence and characterization of a glycosylated RNA-binding protein. Nucleic Acids Res. 1993;21(18):4210–4217. doi: 10.1093/nar/21.18.4210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Swanson MS, Nakagawa TY, LeVan K, Dreyfuss G. Primary structure of human nuclear ribonucleoprotein particle C proteins: conservation of sequence and domain structures in heterogeneous nu-clear RNA, mRNA, and pre-rRNA-binding proteins. Mol Cell Biol. 1987;7(5):1731–1739. doi: 10.1128/mcb.7.5.1731. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Zahler AM, Lane WS, Stolk JA, Roth MB. SR proteins: a conserved family of pre-mRNA splicing factors. Genes Dev. 1992;6(5):837–847. doi: 10.1101/gad.6.5.837. [DOI] [PubMed] [Google Scholar]
  8. Berman HM, Battistuz T, Bhat TN, Bluhm WF, Bourne PE, Burkhardt K, Feng Z, Gilliland GL, Iype L, Jain S. et al. The Protein Data Bank. Acta Crystallogr D Biol Crystallogr. 2002;58(Pt6No1):899–907. doi: 10.1107/s0907444902003451. [DOI] [PubMed] [Google Scholar]
  9. Berman HM, Olson WK, Beveridge DL, Westbrook J, Gelbin A, Demeny T, Hsieh SH, Srinivasan AR, Schneider B. The nucleic acid database. A comprehensive relational database of three-dimensional structures of nucleic acids. Biophys J. 1992;63(3):751–759. doi: 10.1016/S0006-3495(92)81649-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bahadur RP, Zacharias M, Janin J. Dissecting protein-RNA recognition sites. Nucleic Acids Res. 2008;36(8):2705–2716. doi: 10.1093/nar/gkn102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Allers J, Shamoo Y. Structure-based analysis of protein-RNA interactions using the program ENTANGLE. J Mol Biol. 2001;311(1):75–86. doi: 10.1006/jmbi.2001.4857. [DOI] [PubMed] [Google Scholar]
  12. Jones S, Daley DT, Luscombe NM, Berman HM, Thornton JM. Protein-RNA interactions: a structural analysis. Nucleic Acids Res. 2001;29(4):943–954. doi: 10.1093/nar/29.4.943. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Treger M, Westhof E. Statistical analysis of atomic contacts at RNA-protein interfaces. J Mol Recognit. 2001;14(4):199–214. doi: 10.1002/jmr.534. [DOI] [PubMed] [Google Scholar]
  14. Morozova N, Allers J, Myers J, Shamoo Y. Protein-RNA interactions: exploring binding patterns with a three-dimensional superposition analysis of high resolution structures. Bioinformatics. 2006;22(22):2746–2752. doi: 10.1093/bioinformatics/btl470. [DOI] [PubMed] [Google Scholar]
  15. Ellis JJ, Broom M, Jones S. Protein-RNA interactions: structural analysis and functional classes. Proteins. 2007;66(4):903–911. doi: 10.1002/prot.21211. [DOI] [PubMed] [Google Scholar]
  16. Kim H, Jeong E, Lee SW, Han K. Computational analysis of hydrogen bonds in protein-RNA complexes for interaction patterns. FEBS Lett. 2003;552(2-3):231–239. doi: 10.1016/s0014-5793(03)00930-x. [DOI] [PubMed] [Google Scholar]
  17. Towfic F, Caragea C, Dobbs D, Gemperline DC, Feihong W, Honavar V. Structural characterization of RNA-binding sites of proteins: Preliminary results. Bioinformatics and Biomedicine Workshops, 2007 BIBMW 2007 IEEE International Conference on: 2-4 Nov. 2007. 2007. pp. 60–66. 2007.
  18. Zhou P, Zou J, Tian F, Shang Z. Geometric similarity between protein-RNA interfaces. Journal of Computational Chemistry. 2009;30(16):2738–2751. doi: 10.1002/jcc.21300. [DOI] [PubMed] [Google Scholar]
  19. Draper DE. Themes in RNA-protein recognition. J Mol Biol. 1999;293(2):255–270. doi: 10.1006/jmbi.1999.2991. [DOI] [PubMed] [Google Scholar]
  20. Draper DE. Protein-RNA Recognition. Annual Review of Biochemistry. 1995;64(1):593–620. doi: 10.1146/annurev.bi.64.070195.003113. [DOI] [PubMed] [Google Scholar]
  21. Wang L, Brown S. BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences. Nucleic acids research. 2006;34(WebServerissue):W243. doi: 10.1093/nar/gkl298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Tong J, Jiang P, Lu ZH. RISP: a web-based server for prediction of RNA-binding sites in proteins. Comput Methods Programs Biomed. 2008;90(2):148–153. doi: 10.1016/j.cmpb.2007.12.003. [DOI] [PubMed] [Google Scholar]
  23. Kumar M, Gromiha MM, Raghava GP. Prediction of RNA binding sites in a protein using SVM and PSSM profile. Proteins. 2008;71(1):189–194. doi: 10.1002/prot.21677. [DOI] [PubMed] [Google Scholar]
  24. Wang Y, Xue Z, Shen G, Xu J. PRINTR: prediction of RNA binding sites in proteins using SVM and profiles. Amino Acids. 2008;35(2):295–302. doi: 10.1007/s00726-007-0634-9. [DOI] [PubMed] [Google Scholar]
  25. Cheng CW, Su EC, Hwang JK, Sung TY, Hsu WL. Predicting RNA-binding sites of proteins using support vector machines and evolutionary information. BMC Bioinformatics. 2008;9(Suppl12):S6. doi: 10.1186/1471-2105-9-S12-S6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Spriggs RV, Murakami Y, Nakamura H, Jones S. Protein function annotation from sequence: prediction of residues interacting with RNA. Bioinformatics. 2009;25(12):1492–1497. doi: 10.1093/bioinformatics/btp257. [DOI] [PubMed] [Google Scholar]
  27. Spriggs RV, Jones S. RNA-binding residues in sequence space: Conservation and interaction patterns. Computational Biology and Chemistry. 2009;33(5):397–403. doi: 10.1016/j.compbiolchem.2009.07.012. [DOI] [PubMed] [Google Scholar]
  28. Jeong E, Chung IF, Miyano S. A neural network method for identification of RNA-interacting residues in protein. Genome Inform. 2004;15(1):105–116. [PubMed] [Google Scholar]
  29. Jeong E, Miyano S. A weighted profile based method for protein-RNA interacting residue prediction. Lecture notes in computer science. 2006;3939:123. [Google Scholar]
  30. Terribilini M, Lee J, Yan C, Jernigan R, Honavar V, Dobbs D. Prediction of RNA binding sites in proteins from amino acid sequence. Rna. 2006;12(8):1450. doi: 10.1261/rna.2197306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Terribilini M, Sander JD, Lee JH, Zaback P, Jernigan RL, Honavar V, Dobbs D. RNABindR: a server for analyzing and predicting RNA-binding sites in proteins. Nucleic Acids Res. 2007;35(WebServerissue):W578–584. doi: 10.1093/nar/gkm294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Caragea C, Sinapov J, Honavar V, Dobbs D. Assessing the Performance of Macromolecular Sequence Classifiers. Bioinformatics and Bioengineering, 2007 BIBE 2007 Proceedings of the 7th IEEE International Conference on: 2007. 2007. pp. 320–326.
  33. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics. 2000;16(4):404–405. doi: 10.1093/bioinformatics/16.4.404. [DOI] [PubMed] [Google Scholar]
  34. Hsu C-M, Chen C-Y, Hsu C-C, Liu B-J. Efficient Discovery of Structural Motifs from Protein Sequences with Combination of Flexible Intra- and Inter-block Gap Constraints. 2006. pp. 530–539.
  35. Chen CY, Tsai HK, Hsu CM, May Chen MJ, Hung HG, Huang GT, Li WH. Discovering gapped binding sites of yeast transcription factors. Proc Natl Acad Sci U S A. 2008;105(7):2527–2532. doi: 10.1073/pnas.0712188105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hsu CM, Chen CY, Liu BJ. MAGIIC-PRO: detecting functional signatures by efficient discovery of long patterns in protein sequences. Nucleic Acids Res. 2006;34(WebServerissue):W356–361. doi: 10.1093/nar/gkl309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Su CT, Chen CY, Hsu CM. iPDA: integrated protein disorder analyzer. Nucleic Acids Res. 2007;35(WebServerissue):W465–472. doi: 10.1093/nar/gkm353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Chien TY, Chang DT, Chen CY, Weng YZ, Hsu CM. E1DS: catalytic site prediction based on 1D signatures of concurrent conservation. Nucleic Acids Res. 2008;36(WebServerissue):W291–296. doi: 10.1093/nar/gkn324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Hsu CM, Chen CY, Liu BJ, Huang CC, Laio MH, Lin CC, Wu TL. Identification of hot regions in protein-protein interactions by sequential pattern mining. BMC Bioinformatics. 2007;8(Suppl5):S8. doi: 10.1186/1471-2105-8-S5-S8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Towfic F, Caragea C, Gemperline D, Dobbs D, Honavar V. Struct-NB: Predicting Protein-RNA Binding Sites Using Structural Features. International Journal of Data Mining and Bioinformatics. 2008. [DOI] [PMC free article] [PubMed]
  41. Soler N, Fourmy D, Yoshizawa S. Structural insight into a molecular switch in tandem winged-helix motifs from elongation factor SelB. J Mol Biol. 2007;370(4):728–741. doi: 10.1016/j.jmb.2007.05.001. [DOI] [PubMed] [Google Scholar]
  42. Hoang C, Chen J, Vizthum A Caroline, Kandel JM, Hamilton S Christopher, Mueller EG, Ferré-D'Amaré AR. Crystal Structure of Pseudouridine Synthase RluA: Indirect Sequence Readout through Protein-Induced RNA Structure. 2006. pp. 535–545. [DOI] [PubMed]
  43. Larranaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, Lozano JA, Armananzas R, Santafe G, Perez A. et al. Machine learning in bioinformatics. Brief Bioinform. 2006;7(1):86–112. doi: 10.1093/bib/bbk007. [DOI] [PubMed] [Google Scholar]
  44. Bhaskar H, Hoyle DC, Singh S. Machine learning in bioinformatics: a brief survey and recommendations for practitioners. Comput Biol Med. 2006;36(10):1104–1125. doi: 10.1016/j.compbiomed.2005.09.002. [DOI] [PubMed] [Google Scholar]
  45. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–3402. doi: 10.1093/nar/25.17.3389. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from BMC Genomics are provided here courtesy of BMC

RESOURCES