Dataset Information

Ribonucleoprotein SS-B/La belongs to a protein family with consensus sequences for RNA-binding.

ABSTRACT: Autoantibodies from systemic rheumatic disorders have become useful reagents in molecular biology. SS-B/La, a major target of autoantibodies in lupus and Sjogren's syndrome, has been identified as a 46 kDa protein component of a ribonucleoprotein (RNP) particle implicated in the maturation of RNA polymerase III transcripts. This report describes the complete sequences of human and bovine SS-B/La and the identification of RNA-binding protein consensus sequences RNP1 and RNP2 in the N-terminal region previously shown to be complexed with RNA in UV-crosslinking experiments. Segments of about 95 residues from the RNA-binding domain of SS-B/La and from 29 RNA-binding domains of several other proteins are analysed with respect to the frequency of amino acids and their hydrophobicity at each position. The data suggest that SS-B/La belongs to a large family of RNA-binding proteins which includes heterogeneous nuclear RNPs, nucleolin, mRNA polyadenylate binding protein, and small nuclear RNPs.

SUBMITTER: Chan EK

PROVIDER: S-EPMC317593 | biostudies-other | 1989 Mar

REPOSITORIES: biostudies-other

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Ribonucleoprotein SS-B/La belongs to a protein family with consensus sequences for RNA-binding.

Chan E K EK Sullivan K F KF Tan E M EM

Nucleic acids research 19890301 6

Autoantibodies from systemic rheumatic disorders have become useful reagents in molecular biology. SS-B/La, a major target of autoantibodies in lupus and Sjogren's syndrome, has been identified as a 46 kDa protein component of a ribonucleoprotein (RNP) particle implicated in the maturation of RNA polymerase III transcripts. This report describes the complete sequences of human and bovine SS-B/La and the identification of RNA-binding protein consensus sequences RNP1 and RNP2 in the N-terminal reg ...[more]

PMID: 2468131

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Project description:BACKGROUND: Many learning approaches to predicting RNA-binding residues in a protein sequence construct a non-redundant training dataset based on the sequence similarity. The sequence similarity-based method either takes a whole sequence or discards it for a training dataset. However, similar sequences or even identical sequences can have different interaction sites depending on their interaction partners, and this information is lost when the sequences are removed. Furthermore, a training dataset constructed by the sequence similarity-based method may contain redundant data when the remaining sequence contains similar subsequences within the sequence. In addition to the problem with the training dataset, most approaches do not consider the interacting partner (i.e., RNA) of a protein when they predict RNA-binding amino acids. Thus, they always predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNA molecules. RESULTS: We developed a feature vector-based method that removes data redundancy for a non-redundant training dataset. The feature vector-based method constructed a larger training dataset than the standard sequence similarity-based method, yet the dataset contained no redundant data. We identified effective features of protein and RNA (the interaction propensity of amino acid triplets, global features of the protein sequence, and RNA feature) for predicting RNA-binding residues. Using the method and features, we built a support vector machine (SVM) model that predicted RNA-binding residues in a protein sequence. Our SVM model showed an accuracy of 84.2%, an F-measure of 76.1%, and a correlation coefficient of 0.41 with 5-fold cross validation on a non-redundant dataset from 3,149 protein-RNA interacting pairs. In an independent test dataset that does not include the 3,149 pairs and were not used in training the SVM model, it achieved an accuracy of 90.3%, an F-measure of 72.8%, and a correlation coefficient of 0.24. Comparison with other methods on the same datasets demonstrated that our model was better than the others. CONCLUSIONS: The feature vector-based redundancy reduction method is powerful for constructing a non-redundant training dataset for a learning model since it generates a larger dataset with non-redundant data than the standard sequence similarity-based method. Including the features of both RNA and protein sequences in a feature vector results in better performance than using the protein features only when predicting the RNA-binding residues in a protein sequence.

Dataset Information

Ribonucleoprotein SS-B/La belongs to a protein family with consensus sequences for RNA-binding.

Publications

Ribonucleoprotein SS-B/La belongs to a protein family with consensus sequences for RNA-binding.

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