Predicting RNA-binding residues from evolutionary information and sequence conservation

Huang, Yu‐Feng; Chiu, Li-Yuan; Huang, Chun-Chin; Huang, Chien-Kang

doi:10.1186/1471-2164-11-s4-s2

Cited by 18 publications

(6 citation statements)

References 46 publications

(30 reference statements)

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“…Tong et al evaluated their classifier using window-based five-fold cross-validation on the RB147 [ 47 ] dataset. ProteRNA [ 19 ] is another recent SVM classifier that uses evolutionary information and sequence conservation to classify RNA-binding protein residues. Sequence-based five-fold cross-validation on the RB147 dataset was used to evaluate performance.…”

Section: Introductionmentioning

confidence: 99%

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

et al. 2012

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BackgroundRNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition ‘code’ that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction.ResultsWe provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those that use an amino acid identity based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based representation (IDStr). PSSMSeq classifiers, when tested on an independent test set of 44 proteins, achieve performance that is comparable to that of three state-of-the-art structure-based predictors (including those that exploit geometric features) in terms of Matthews Correlation Coefficient (MCC), although the structure-based methods achieve substantially higher Specificity (albeit at the expense of Sensitivity) compared to sequence-based methods. We also find that the expected performance of the classifiers on a residue level can be markedly different from that on a protein level. Our experiments show that the classifiers trained on three different non-redundant protein-RNA interface datasets achieve comparable cross-validation performance. However...

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Section: Introductionmentioning

confidence: 99%

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

et al. 2012

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“…Position-specific scoring matrices(PSSMs, 20 features) : PSSM profiles are quite effective in RNA-binding site prediction in previous studies [35–37]. We calculate PSSMs using PSI-BLAST [38] searching against the NCBI NR database, with iterations = 3 and e -value = 0.001.…”

Section: Methodsmentioning

confidence: 99%

A boosting approach for prediction of protein-RNA binding residues

et al. 2017

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BackgroundRNA binding proteins play important roles in post-transcriptional RNA processing and transcriptional regulation. Distinguishing the RNA-binding residues in proteins is crucial for understanding how protein and RNA recognize each other and function together as a complex.ResultsWe propose PredRBR, an effectively computational approach to predict RNA-binding residues. PredRBR is built with gradient tree boosting and an optimal feature set selected from a large number of sequence and structure characteristics and two categories of structural neighborhood properties. In cross-validation experiments on the RBP170 data set show that PredRBR achieves an overall accuracy of 0.84, a sensitivity of 0.85, MCC of 0.55 and AUC of 0.92, which are significantly better than that of other widely used machine learning algorithms such as Support Vector Machine, Random Forest, and Adaboost. We further calculate the feature importance of different feature categories and find that structural neighborhood characteristics are critical in the recognization of RNA binding residues. Also, PredRBR yields significantly better prediction accuracy on an independent test set (RBP101) in comparison with other state-of-the-art methods.ConclusionsThe superior performance over existing RNA-binding residue prediction methods indicates the importance of the gradient tree boosting algorithm combined with the optimal selected features.

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“…Evolutionary information in the form of a position-specific scoring matrix (PSSM) has been used successfully to represent proteins in many applications, such as prediction of DNA-binding residues [ 16 – 21 ] and RNA-binding residues [ 15 , 22 , 23 ]. Here, PSSM profiles were generated using the PSI-BLAST program [ 24 ] to search the nonredundant (NR) database through three iterations, with 0.001 as the e -value cutoff for multiple sequence alignment.…”

Section: Methodsmentioning

confidence: 99%

Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection

Guo

Sun

2015

BioMed Research International

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The prediction of RNA-binding proteins is one of the most challenging problems in computation biology. Although some studies have investigated this problem, the accuracy of prediction is still not sufficient. In this study, a highly accurate method was developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR) method, followed by incremental feature selection (IFS). We incorporated features of conjoint triad features and three novel features: binding propensity (BP), nonbinding propensity (NBP), and evolutionary information combined with physicochemical properties (EIPP). The results showed that these novel features have important roles in improving the performance of the predictor. Using the mRMR-IFS method, our predictor achieved the best performance (86.62% accuracy and 0.737 Matthews correlation coefficient). High prediction accuracy and successful prediction performance suggested that our method can be a useful approach to identify RNA-binding proteins from sequence information.

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Predicting RNA-binding residues from evolutionary information and sequence conservation

Cited by 18 publications

References 46 publications

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

A boosting approach for prediction of protein-RNA binding residues

Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection

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